Dana DiTomaso, principal and founder of Kick Point, returns to the Near Memo (her first visit was GA4's early days) to talk with Greg Sterling and Mike Blumenthal about what's left of attribution: how much of a site's traffic is trackable, why one AI-started purchase can show up as four different channels, and what a marketer can still measure with confidence. Attribution was always partly fiction that Universal Analytics papered over, and GA4 and privacy law have simply exposed it — so as consent banners, ad blockers and AI referrals keep shrinking the trackable slice, the job shifts from reconciling exact numbers to sizing what you can see, watching directional signals like branded search, and explaining the gaps to clients before they find them. ## Dana DiTomaso's Trackability Flowchart Dana's main client tool is a flowchart (she builds hers in Figma) that follows a visitor from first touch to the CRM and shows where each one drops out of view _(11:56–12:43, 32:13–34:12)_. Her rule for every stage is the same: "track the heck out of" the people you can see, and let go of the rest. Stage | What it answers | Primary lever ---|---|--- **Before the site** | Where did the journey actually start: an AI answer, a brand search, a shopping listing, a link a spouse texted? | Walk through specific scenarios with the client. Add UTMs wherever you control the link. Watch branded-search impressions in Search Console. **Consent banner** | What share of visitors said no to tracking? | Read the decline rate from the consent tool (she uses Cookiebot), and make sure "no" actually stops the tags. **Ad blockers and browser privacy** | How much of the consenting traffic still disappears? | A cookieless backup (Plausible) for page views, and server log files, which nobody can hide from. **GA4 processing** | How does the visible traffic get bucketed? | 14-month data retention, and custom channels for GBP, Bing, tab collectors and any small channel that matters. **CRM** | Did the lead close, and where did it really come from? | Tag the sign-up source (in store or online) and check self-reported answers against GA4. ## Takeaways * **Attribution isn't dead, just not healthy.** It is "not as dead as the parrot" in the Monty Python skit. Universal Analytics hid incomplete attribution, while GA4 hands you the data and lets you find the gaps. Privacy laws such as GDPR and California's CPRA arrived at the same time and made the gaps bigger. _(02:03–02:40)_ * **Your trackable share depends on who visits.** In Texas, with a non-technical audience, few people go untracked. For some of Kick Point's European-facing clients, 80% decline tracking, and about half of the European visitors to Dana's own site say no. Ad blockers and Safari's Intelligent Tracking Prevention cut further into the rest. _(04:48–05:52)_ * **Most small businesses will never qualify for GA4 modeling.** Consent Mode v2 sends cookieless pings, but GA4 only models the people who declined if you have roughly 1,000 consenting and 1,000 declining visitors a day on 7 of the past 28 days. "If you're never gonna qualify… just do something else instead." _(05:57–06:56)_ Google's thresholds [1] * **One AI-started purchase can show up as four different channels.** A shopper asks ChatGPT for a Canadian lighting manufacturer. If the link carries no UTM, the click lands as direct. If the shopper then Googles the brand and clicks its shopping listing, Search Console records a branded search, Merchant Center records an impression, and GA4 credits Google Shopping. The tell is an srsltid parameter on the URL. _(07:54–09:40)_ [2] * **"Unassigned" is often tab collectors.** A visitor who leaves your blog post open for two weeks and then books has had their session time out, so GA4 shows them as unassigned. Dana moves that traffic into a channel she names "session timeout reactivation" (or "tab collectors"), so nobody has to keep explaining it. _(09:47–10:55)_ Kick Point Playbook * **Size the slice before you make decisions.** Start with 100 visitors, subtract everyone who said no, and then look at the channel mix. A channel that looks like half your traffic in GA4 can be a much smaller share of what really happened. She also warns against the opposite mistake: a Fortune 100 client spent more on meetings about 0.05% of its traffic than that traffic earned. _(13:03–15:38)_ * **"How did you hear about us?" undercounts what actually worked.** Home buyers credit the "show home this way" sign, not the ad that put the builder in their head. One client's branded PPC leads say "recommended by a friend," which is no reason to cut branded search: it's cheap and it keeps competitors off your name. Rotate drop-down options, because people pick the first one. _(15:48–16:50, 37:55–38:20)_ * **Use regression to forecast staffing four weeks out.** Dana regressed weekly GA4 sessions against booked appointments in Google Sheets and found a moderately strong relationship. A pacing chart shows whether sessions will reach the appointment goal before the month runs out, early enough that a client doesn't have to lay anyone off. After the client redesigned its booking flow, the line turned "almost straight up." _(20:22–22:20)_ Kick Point Playbook * **Branded search is your best sentinel metric.** Dana tracks Search Console impressions for her own name ("however they decide to spell it"). For any client running billboards, radio, CTV or anything else outside PPC, she treats branded-search volume as table stakes, and in the AI era it is "probably gonna be the best signal that you've got." _(24:44–26:30)_ * **Day-of-week spikes are probably rank trackers.** Leave your own brand name out of your rank tracker. If impressions jump on the same weekday, such as every Monday, it's probably a competitor's tracker, unless you work in personal injury or bankruptcy, where Monday demand is real. _(26:58–27:36)_ * **AI visibility tools aren't rank trackers.** Position in a tool's synthetic prompt won't match any real user's answer. Use the tools to learn whether AI knows you exist and what it says when asked about you, then correct whatever is outdated. Dana discloses that she sits on SE Ranking's customer advisory board and uses SE Visible. She also likes Knowatoa, Gumshoe and Rand Fishkin's Alertmouse, and says nobody needs to spend "thousands of dollars on it a month." _(26:16–26:45, 28:52–31:36)_ * **Two GA4 settings to change today.** Set event data retention to 14 months. The default is two, which silently empties Explorations. Then split organic search, often 40–50% of traffic, into Google organic, Google Business Profile, Bing and other organic. Small channels that matter get their own line too, like one client's Yelp ads channel. Finally, check your country report: an Edmonton realtor's traffic was about 80% "from China," which Dana believes is LLM bots, and bots also inflate direct traffic. _(40:06–41:42, 45:41–46:35)_ ### Related Reading * How Cookieless Tracking Actually Works (And Why Your Numbers Look Different) — Dana's side-by-side comparison of GA4 Consent Mode, Matomo and Plausible. * Stop Explaining GA4 Unassigned Traffic — Rename Confusing Channels Instead — the "tab collectors" custom channel group. * How to Predict Whether Clients Will Hit Their Targets — the Google Sheets regression and Looker Studio pacing chart. * We Are Not Data Accountants — Dana's case for interpreting trends instead of reconciling numbers. * Ep 155: Dana DiTomaso — To Google GA4 analytics hell & back again — her first Near Memo appearance (2024). * EP 277 — Correcting the Machine: Citation Labs on the Six Layers of AI Visibility (Part 1) — last week's episode; Part 2 is next. * EP 275 — Rankings Up, Calls Down: What AI Visibility Is Really Worth to Local Businesses (Part 2) — the self-reported lead source debate from the other side. * EP 270 — The Hidden Advantage: How Brand Awareness Decides the Click in Legal Search — the Near Media user research Greg and Mike describe at 16:50. * Behavioral modeling for consent mode — Google's eligibility thresholds. * Tools mentioned: Plausible, Matomo, Cookiebot, PostHog, SE Visible, Knowatoa, Gumshoe, Alertmouse. For GA4 training: Analytics Mania, Loves Data, Kick Point Playbook. ## Practitioner Notes * **Self-reported source and GA4 have to be read together.** Dana's finding that branded-PPC leads describe themselves as "recommended by a friend" is the same tension that runs through Near Media's monthly lead-source attribution work for law firms. Neither the intake answer nor the analytics channel is the truth on its own. The useful report puts the two side by side, as Dana does when she matches web sessions to phone calls. * **Greg's AI Overview observation makes branded search an AIO metric.** In Near Media's user research, searchers who see firms named in an AI Overview often follow up with a brand search _(27:36)_. Search Console won't link the two queries, but Dana's sentinel metric means a rise in branded impressions after a firm starts appearing in AIOs is a usable, if indirect, signal. It pairs with the brand-awareness findings in EP 270. * **A dedicated GBP channel should be standard for local clients.** Dana's split of "organic search" into Google organic, Google Business Profile and Bing is the GA4 counterpart to tagging the GBP website link with UTMs. Without both, a local business can't tell how much of its "organic" traffic is really its profile, and it can't see how that share shifts as AI Overviews and Ask Maps change the local SERP. ### Concepts * **Directional trends** — reading analytics for direction and rate of change, not exact counts, because no tool sees everyone. * **The trackable slice** — the share of real visitors left after consent declines, ad blockers and browser privacy. Every channel percentage is a share of this slice, not of all traffic. * **Tab collectors (session timeout reactivation)** — visitors who return to a long-open tab after their session has expired. GA4 loses their source and files them as "unassigned." * **Cookieless pings** — the signal-only hits Consent Mode v2 sends for visitors who decline tracking. Like Plausible, each one counts as a brand-new visitor. * **Effectiveness ratio** — total impressions across every channel compared with closed sales or leads. If the ratio is flat or rising, keep going. If it falls, something you're doing has lost its punch. * **Pacing chart** — a regression-based forecast of whether current sessions will reach the month's appointment or sales goal before the month runs out. * **Sentinel metric** — a "canary in a coal mine" measure, such as branded-search impressions, that shows shifts no single channel report can. * **Visibility, not position** — the right question for an AI visibility tool: does the model know you, and is what it says accurate? ### Pick your starting point: 00:00 Welcome and Dana DiTomaso 01:58 Is attribution dead? 04:42 How much of your traffic can you track? 05:52 GA4 modeling thresholds 07:45 One AI search, four attribution channels 09:47 Tab collectors and "unassigned" traffic 13:28 Sizing the trackable slice 15:38 The trouble with "How did you hear about us?" 19:29 The effectiveness ratio 20:22 Forecasting bookings with regression 24:44 Branded search as a sentinel metric 28:37 Are AI visibility tools worth it? 31:36 The way forward: consent, flowcharts and trust 39:43 Two GA4 settings to change right now 43:23 The best GA4 alternatives 45:07 LLM bot spam in GA4 ### Full Transcript --> on-line ### Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### Study: Why People Leave Bad Reviews Basic communication and service failures are responsible for a majority of bad reviews. By the same token, good service and transparent pricing generally result in positive reviews. This is intuitive, but still is somehow not fully understood by large numbers of businesses. These findings are Whitespark's latest research analyzing reviews from 1,200 Google Business Profiles across six verticals: home services, legal, medical, animal care, automotive and restaurants and retail. The study found that communication issues and pricing surprises were the most frequently cited complaints in negative reviews but the precise form of those complaints was tied to the industries involved. Communication problems in home services, for example, included failing to answer phones or return calls, arriving late without notice, being rude and not fully explaining fees and costs. In legal, complaints included inconsistent case updates and the inability to reach lawyers and staff. Positive reviews were basically the flip side of those complaints: professionalism, promptness, clear pricing and friendliness. Interestingly, friendliness at restaurants overcame meh food quality and, surprisingly, was prized over the quality of outcomes in healthcare/medical. At the highest level all this should be self-evident but it's apparently not. With AI search, reviews are even more important today. Business owners need to focus on service quality and be scrupulously ethical. We previously discussed many of these issues under the banner of "operational excellence" as a key part of local brand building in our interview with John Jantsch. ****Top Complaints by Vertical ### This post is for subscribers only Become a member to get access to all content Subscribe now

Garrett French, James Wirth and Valerie Cecil of Citation Labs and its local-sponsorship sibling ZipSprout join Greg Sterling and Mike Blumenthal for Part 1 of a two-part Near Memo conversation, on what has changed in the market and how an enterprise link-building shop now diagnoses and repairs what AI systems say about a client. Part 2 will cover the agency side: skills, tooling and pricing. AI visibility is a chain of dependencies — a brand has to be returned, read, cited, mentioned and recommended, and framed correctly at every step — so the work shifts from winning a ranking to finding the layer where the chain breaks and supplying the missing or corrected facts, mostly on pages the brand doesn't own. ## James Wirth's Six Layers of AI Visibility James lays out the model Citation Labs audits against _(34:58–38:09)_. Each layer raises the odds of the next, and none guarantees it. The audit that maps a client onto these layers is what the team calls a visibility probe. Layer | What it answers | The primary lever ---|---|--- **1. Returned** | Does the brand's page appear in the search results the model pulls when it runs its query fan-outs? | Traditional SEO and link building; new content for fan-out queries where the brand has no presence **2. Read / retrieved** | Does the model open the page, or answer from the search snippet alone? | Page structure the model can lift cleanly; watching page-retrieval patterns as an early signal **3. Cited** | Is the page used as a source in the answer? | Insertions on publishers that are already being cited; correcting cited sources that state the wrong facts **4. Mentioned** | Is the brand named in the answer? | "Narrative expansion": covering the part of the story the brand is absent from **5. Recommended** | Is the brand put forward as an option, at what rank, and with what qualifier? | Supplying the facts that remove a limiting qualifier and move the brand up the list **6. Framing** | Across all five: are the facts, pricing, sentiment and attributes right? | Off-domain content the client can keep current when third-party pages won't update ## Takeaways * **Accountability for links became the AI-visibility method.** Citation Labs won an enterprise client by being the only one of five agencies, in a six-month pilot with equal budgets, able to show that a link moved a ranking that mattered. James was on the client side of that test and then asked for a job. Garrett says the habit of proving "what the work did" is what the team carried into AI visibility. _(01:33–04:33)_ * **Every enterprise client has moved to AI visibility.** Garrett is explicit that this is three clients and anecdotal, but all three have been hit by zero-click and all three pushed the agency "wholly into AI visibility." Links still matter; they are now one lever among several. _(10:32–11:58)_ * **Conference-floor conversations contradict the "business as usual" data.** At BrightonSEO San Diego, James heard talks built on large aggregate data sets showing little change, then heard the opposite at the booth: organic traffic down sharply, direct and referral numbers that no longer reconcile, and conversions nobody can attribute. His own audits show declines "far more than what the large data sets are reporting." _(12:36–15:17)_ * **Local is where the damage is smallest and the AI answers are worst.** Mike sees web traffic falling for local clients without matching drops in calls and conversions. James doesn't trust local AI answers, finds them often wrong, and ends up back in the map pack or a maps app. Greg's read: AI works the top and middle of the funnel, Google gets the last click, and the AI influence never shows up in referrals. _(15:17–20:26)_ * **Greg's roof repaint never touched Google.** He asked ChatGPT for three body shops that would paint only a roof, got a ranked list built on Yelp data, had it pull in Google reviews for comparison, and clicked through to Yelp's quote request. He calls himself an outlier. The anecdote still shows a complete local purchase path with Google present only as review data inside another product. _(20:46–22:03)_ * **Prompts are built from the offering, not from keywords.** The team starts at the client's transaction pages and works backward to the problem a buyer is solving, drawing on customer-service logs, Reddit complaints and interviews with subject-matter experts. The resulting prompts run 50 to 300 words and specify a role inside the ideal customer profile. Ten or more are tracked daily for each offering, not each client. _(28:31–32:50)_ * **The model's opinion of the brand replaces the searcher's.** A searcher used to bring their own sense of a brand's reputation to the ten blue links. Now the model assembles that judgment from far more sources than any person would read and attaches its own qualifiers. That is why sentiment and framing are tracked alongside citations. _(33:04–34:27)_ * **Stale comparison pages are a repair job you can do off-site.** Garrett's example is a client whose service packages change often. Affiliate-style comparison pages kept getting cited with outdated facts and wouldn't update. Citation Labs published accurate pages it controls and can revise, then tracked the result: the new pages were cited, the brand was mentioned, and the framing changed. Garrett on the work: "We get to correct." _(39:41–41:41)_ * **A months-old Bing snippet set a telecom's pricing in the answer.** In one audit, a non-Google model ran its fan-outs through Bing, which was still serving a cached snippet from a page updated months earlier. The model never opened the page and answered from the snippet, producing wrong pricing, offer terms and expiration dates. _(43:21–44:37)_ * **Models discount what a brand says about itself.** James reports seeing models pass over owned-domain content in favor of third-party pages, even to the brand's cost. Many high-value cited publishers are closed to placements, which is why the team builds off-domain microsites as well as pitching insertions. _(38:49–39:22, 43:11–43:21)_ * **Page-retrieval patterns are the earliest signal.** Valerie watches which pages models pull and in what form: FAQs, comparisons, graphs, statistics. Retrieval shows up before citations or mentions do, so it tells the team whether a content bet is working. James's caveat: write concisely, the way you would argue before a judge, without rewriting everything for machines at the reader's expense. _(41:46–43:11)_ * **Fan-outs now skip discovery and query brand sites directly.** The team saw models stop running non-brand searches and go straight to `site:` queries against brands they already know, a shift that spiked in ChatGPT around April and that James ties to a model change. Valerie adds that .gov sources are being favored more in some industries. Garrett's hunch: a fan-out means the training data wasn't enough to answer. _(45:58–47:12)_ [1] * **They "professionalize" prompts instead of personalizing them.** Greg has told ChatGPT never to show him eBay results, and it complies. Citation Labs can't model that. It asks what a given role (a CFO, say) would need in a perfect-information setting and builds that context into the prompt as a proxy. Owning the tracking tool means no cap on prompt volume for testing. _(48:19–51:39)_ * **Google still gets the tie-break.** They measure ChatGPT and Google's surfaces together and act on the aggregate, but James would not close a ChatGPT gap at the expense of AI Overviews, AI Mode or Gemini. Success is measured in sentiment shift, mention count, recommendation rank, and whether a limiting qualifier disappears. _(51:42–54:10)_ ### Related Reading * Citation Labs and its team page — Garrett French (CEO), James Wirth (Sr. Director, Strategy and Growth Marketing) and Valerie Cecil (Operations Manager). * Citation Labs BrightonSEO 2026 resources — the field guides (AI Visibility, Information Design, Business Impact) from the BrightonSEO San Diego appearance James describes. * Xofu — the Citation Labs sister company's prompt tracker for AI answers. On air the team says only that it built its own AI visibility tools. * How Consumers Navigate High-Stakes Purchases in AI Mode — Kevin Indig's write-up of the usability study with Citation Labs and Eric Van Buskirk's Clickstream Solutions, the "project we did with Eric" Garrett mentions at 27:07. * How to Track and Fix BOFU Visibility in LLMs — the Citation Labs podcast on the same sales-page-first approach. * EP 276 — GEO Is CRO: Ross Hudgens on Chasing Influence, Not Citations — last week's counterpoint: Ross argues against chasing today's cited sources; Citation Labs argues for repairing them. * * EP 275 — Rankings Up, Calls Down: What AI Visibility Is Really Worth to Local Businesses (Part 2) — the attribution problem Mike raises at 15:17. * EP 274 — Why "Position One" Doesn't Exist in Local Anymore (BrightonSEO Recap) — Near Media's own recap of the same conference. * EP 270 — The Hidden Advantage: How Brand Awareness Decides the Click in Legal Search — the legal user-behavior research Greg describes at 25:41. ## Practitioner Notes * **The six layers give local audits a missing vocabulary.** Most local AI-visibility reporting stops at "mentioned or not." Splitting returned / read / cited / mentioned / recommended / framing tells you which fix applies. It maps directly onto the fan-out and retrieval-hierarchy work on Ask Maps versus Gemini: if a GBP-grounded surface never issues a web fan-out, layers 1–3 are a profile and review problem, not a content problem. * **James's distrust of local AI answers matches the benchmark data.** His "often wrong" experience lines up with LocalBench, where LLM accuracy on local numerical queries came in below 15.5%. That is the case for treating framing audits (hours, pricing, service area, offer terms) as a recurring local deliverable, and for checking Bing's index freshness after any change to a client's key pages. * **Garrett's critique of simulated purchase studies is the design brief for the survey work.** His objection at 27:07, that participants have no skin in the game, is the gap the Choosing a Lawyer research (EP 270) acknowledges and the conversational consumer-journey survey is built to narrow, by interviewing people about a decision they actually made. Worth stating that limitation up front in the brand white paper, since a peer raised it unprompted. * **"Models discount owned content" raises the value of what local businesses already have off-site.** For a local firm the third-party corpus is reviews, GBP, directories and sponsorship pages. ZipSprout-style community sponsorships produce exactly the independent, locally relevant pages the returned and cited layers reward, which makes them worth testing as an AI-visibility input and not only a link source. ### Concepts * **Six layers of AI visibility** — returned, read/retrieved, cited, mentioned, recommended, and framing (which runs across the other five). The diagnostic model for locating where a brand drops out of an AI answer. * **Visibility probe** — Citation Labs' initial audit: simulate the prompts, run the fan-outs, and record where the brand appears or is missing at each layer. * **Query fan-out (QFO)** — the set of searches a model issues behind a single prompt to ground its answer. Being absent from a fan-out query is treated as a fixable gap. * **Citation optimization** — getting an already-cited third-party source to state what is factually true about a client. Presented as the successor to link building. * **Tracking-worthy prompts** — long, role-specific prompts (50–300 words) that carry the buyer's situation, built from the offering backward and tracked daily. * **Sales-page link building** — the firm's earlier discipline of building links to transactional pages and proving the ranking effect, which set the standard for proving impact now. * **Narrative expansion** — adding the missing part of a brand's story so it gets mentioned for things it currently isn't. * **Page-retrieval patterns** — which pages and formats a model actually fetches; used as a leading indicator ahead of citations. * **Chunkifying** — structuring content into concise, self-contained units a model can lift without losing meaning. * **Professionalize, don't personalize** — modeling the information needs of a role instead of an individual's history and preferences. * **Framing** — how the model characterizes the brand: accuracy of facts, sentiment, and the qualifiers attached to a recommendation. ### Pick your starting point: 00:00 Welcome and introductions 01:33 The five-agency test that started it all 06:23 What Citation Labs and ZipSprout do 09:09 Is AI search overhyped? 12:36 Traffic is down more than the data says 15:17 Local: traffic falls, conversions don't 20:46 Greg's ChatGPT hunt for a body shop 24:29 Attribution and the last-click habit 28:31 Building prompts from the offering backward 33:04 When AI decides a brand's reputation 34:27 The six layers of AI visibility 39:41 Fixing stale comparison pages 43:21 The telecom and the stale Bing snippet 44:37 Training data, caches and fan-outs 48:19 Personalization and the eBay problem 51:42 Weighting Google against ChatGPT ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

Ross Hudgens, founder and CEO of Siege Media and author of the new Wiley book Generative Engine Optimization: The Definitive Guide to AI SEO, joins Greg Sterling and David Mihm (Mike Blumenthal is out) to argue over the label, then get practical: which engines are worth tracking, where AI answers get their evidence, and how a business earns the recommendation. ## Ross Hudgens' Current State vs. Ideal State Ross's recurring distinction _(15:38, 39:42)_ : what the models cite today is a snapshot, not a strategy. His top "what marketers get wrong" answer is the same idea: they aim for the current state instead of influence. Lever | The current-state trap | The influence play ---|---|--- **Citations** | Chasing placement on sites that show up in ChatGPT today, even AI spam sites nobody visits | Go where people actually talk about you (Reddit, reviews, the press); citations follow as a byproduct **Source mix** | Reading "60% of citations are our own site or competitors" as a reason to focus only on-site | Treating a lack of third-party citations as an opportunity: models want third-party reviews **Engines** | Default tracker settings that over-weight Perplexity | Weighting tracking toward the engines customers say they actually used **Measurement** | Trusting GA4 referrals or synthetic prompt-volume data | Open-ended "how did you hear about us," plus daily prompt tracking used as a compass, not a GPS **Brand** | Paying to get onto "best X" listicles without the awareness to convert | Data PR, awards and review platforms — "gasoline on a fire that's already burning" ## Takeaways * **Local is where SEO and GEO diverge least.** Google has already compiled the review context that models are hunting for. Nationally, the models go off-site to Reddit, affiliates, LinkedIn and YouTube, which Ross argues were once indirect ranking inputs and are now direct ones. A niche D2C brand that could never outrank Amazon can still get recommended through its reviews and listings. _(10:08–11:47)_ * **Drop Perplexity from your default tracker.** Many tracking tools default to it, so an unweighted tracker can push you to chase a listing that looks like 25% of the impact but delivers under 1%. For the average local business, Ross sees ChatGPT as the only relevant LLM besides Google's models. _(13:08–14:17)_ * **Claude is the B2B surprise — and it searches through Brave.** Ross's own open-ended lead form surfaced Claude "out of nowhere." Since Claude's web search runs on Brave, your Brave rankings are a cheap proxy when full tracking is too expensive. David's read of Similarweb data: ChatGPT has stalled while Gemini and Claude are the only engines still growing. _(14:17–15:22, 34:20–35:34)_ [1] * **Missing third-party citations are an opening, not a destiny.** In year one, Ross saw homepages, competitors and similar sites taking ~60% of citations. He reads that as a gap in third-party reviews that the models would fill if the reviews existed. _(15:38–17:01)_ * **YouTube is the most-cited single domain for consumer queries.** Siege rarely makes the videos itself. It pays affiliates and influencers to place the client "in context," and Ross suggests local TV stations and publishers, who are "starving for cash," as sponsored-video partners for local firms. David's version for law firms: move the statute-of-limitations content nobody read on the blog onto YouTube. _(17:01–20:56)_ * **Mine your reviews for the landing pages you're missing.** Feed the review corpus to Claude, ChatGPT or Gemini alongside the Ahrefs or Semrush MCP and look for ways customers describe you that no page reflects. Petco's example: "picky eater" kept surfacing across dog-food reviews and became its own angle. Use search volume as a proxy for prompt volume to prioritize. _(22:48–25:39)_ * **GEO is CRO.** Help the model make the decision the same way you'd help a buyer: put awards, top reviews and hard proof points ("14 years in business," "80 case studies") on the homepage. "Hit LLMs over the face with" data points. _(26:26–27:20)_ * **Versus pages predict AI traffic best.** In Siege's analysis, having comparison ("X vs. Y") pages correlated more strongly with _site-wide_ AI traffic than any other content type. You control the message and face little competition for the click. _(27:20–28:22)_ Siege Media study [2] * **Some brand can only be bought.** In personal-injury law, brand comes from billboards and TV (Ross cites Thomas J. Henry, whom Near Media sees constantly in its research). In "best car insurance" listicles, a startup can pay its way onto the list for a week, but Geico's brand click-through will push it off. _(29:51–31:01)_ * **Siege won't take clients without existing demand.** Its Reddit and affiliate services now require a minimum level of branded search, around 20,000 a month. "Gasoline on a fire that's already burning." Data journalism and digital PR are its main way of building brand from scratch. _(31:12–32:07)_ * **Run a quarterly award hunt.** Awards are Ross's fastest brand lever for local: run a Claude workflow every quarter to find "best in [city]" lists, apply, show the wins on your site, and repeat every year. _(33:04–34:08)_ * **Ask open-ended attribution questions, not multiple choice.** Multiple-choice answers default to the first option. A low-friction open field surfaces channels you didn't know were working. Ross treats LLM tracking as "a compass rather than a GPS" and avoids synthetic prompt-volume data. Siege uses Peec AI and also rates Profound and Scrunch. _(34:20–37:23)_ [3] * **On Reddit, monitor and redirect — don't fake it.** Use the free Reddit Pro to catch recommendation threads. Then ask your known five-star Google reviewers to add a review on Yelp, or to weigh in on the thread if they authentically can. Respond to brand mentions fast. _(37:53–39:28)_ * **On Clutch, go deep, not wide.** Diversify reviews, but go all-in on the highest-quality platform for your category. For agencies, Ross says that's Clutch: sponsor the city sub-lists ("San Francisco SEO companies") rather than the national list. _(41:10–42:33)_ ### Related Reading * Generative Engine Optimization: The Definitive Guide to AI SEO — the book's landing page (Wiley, 2026). Amazon · Wiley. * The Content That Predicts AI Search Traffic Most: Versus Pages — Siege Media's correlation study behind the versus-page claim. * How Generative Engine Optimization (GEO) Rewrites the Rules of Search — the a16z post that popularized the term "GEO." * EP 275 — Rankings Up, Calls Down: What AI Visibility Is Really Worth to Local Businesses (Part 2) — Andrew Shotland and Mark Kabana on attribution and self-reported lead sources. * EP 274 — Why "Position One" Doesn't Exist in Local Anymore (BrightonSEO Recap) — the same BrightonSEO San Diego conference that David references. * EP 270 — The Hidden Advantage: How Brand Awareness Decides the Click in Legal Search — the user-behavior research Greg describes at 28:22. * AI Search Stats in 2026 — Similarweb's gen-AI traffic-share tracker. * Yelp deal pushes local reviews into ChatGPT — Axios on the Yelp–OpenAI partnership Greg mentions. * The New Rules of AI Search — Crystal Carter's book, mentioned in the intro. * Tools mentioned: Peec AI, Profound, Scrunch, cloro, Clutch. ## Practitioner Notes * **Open-ended attribution is the measurement layer local firms are missing.** Ross's "Claude came out of nowhere" moment came from a free-text "how did you hear about us" field. Near Media's monthly lead-source attribution work for law-firm clients faces the same blind spot in GA4. An open-ended intake question is the cheapest way to see LLM-sourced leads, and it pairs naturally with the self-reported lead-source argument in EP 275. * **Ross's "brand must be bought" point in legal is backed by Near Media's own research.** Thomas J. Henry's ubiquity is exactly the recognition effect in Near Media's Choosing a Lawyer user study (EP 270). The open question is whether that offline awareness carries over to LLM recommendations the way it carries over to SERP clicks. * **The GBP card in AI Overviews is the exception to "no clicks."** David's observation: AIO mentions almost never click through to a local business website, but the newer desktop GBP card, with stars and a photo, does draw clicks. That makes GBP completeness and review velocity a direct AI-visibility lever, not only a local-pack one — worth tracking in GBP research. * **Yelp is back on the review-diversification list.** With Yelp's data now licensed into ChatGPT, Ross's "ask your five-star Google reviewers for a Yelp review" tactic stops being optional for local clients that have let Yelp lapse. ### Concepts * **Current state vs. ideal state** — the gap between what models cite today and what they would cite if better third-party evidence existed. Ross treats the gap as the opportunity. * **Influence over citations** — optimizing for the places real people form opinions (and that writers research), on the bet that citations follow, rather than buying placement on low-traffic pages that happen to be cited. * **GEO is CRO** — making a page easy for a model to recommend uses the same moves as making it easy for a buyer to choose: awards, reviews and hard proof points up front. * **Versus content** — head-to-head comparison pages ("X vs. Y"). Siege's data ties them to site-wide AI traffic. * **Search volume as a proxy for prompt volume** — prioritizing with real keyword demand rather than synthetic estimates of what people ask LLMs. * **Compass, not GPS** — LLM visibility tracking is directional. It tells you whether you're improving, not your exact position. * **"Gasoline on a fire"** — paid amplification channels (Reddit, affiliate) accelerate existing brand demand but can't create it. * **ICP mapping** — building pages for each ideal-customer dimension (industries, use cases, roles) to catch long-tail, refined prompts. ### Pick your starting point: 00:00 Intro: Ross Hudgens and the GEO book 02:42 GEO vs. AEO vs. AI SEO: the label fight 09:37 How different is GEO from SEO? 11:47 Yelp's comeback in AI answers 13:08 Which AI engines to track (drop Perplexity) 15:38 Current state vs. ideal state for citations 17:01 YouTube and third-party video 22:16 Mining reviews for content 26:26 GEO is CRO 27:20 Versus pages and AI traffic 28:22 Brand: when it has to be bought 33:04 Awards as a brand lever 34:08 Attribution and tracking tools 37:23 Reddit without spamming 39:28 Biggest mistake: chasing citations 44:16 Will LLM clicks grow? ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

AI visibility is SEO's old job — machine-readable facts plus third-party mentions — with a much worse measurement problem, so as ads absorb the transactional click and rank reports stop mapping to revenue, the durable levers become complete attribute data, real earned media, and asking every lead how they found you. ## Mark Kabana's AI Visibility Measurement Ladder At 22:26, Mark lays out why rank tracking no longer answers the question: traditional SEO asked one thing, while AI visibility breaks into a sequence of questions, most of which no current tool measures directly. Rung | What it answers | Primary lever ---|---|--- Discovered | Did the system find the business at all? | Presence across the surfaces AI draws from — website, listings, directories Considered | Was it a candidate for _this_ query, context, and time of day? | Attribute completeness — every fact a model could filter on Understood | What did the system believe about the business? | Consistent facts, reviews that corroborate them Selected | Why was it chosen over the alternatives? | Third-party mentions and earned media Evidenced | What sources did the answer rely on? | Citation footprint on the sites models actually read Acted on | Did the recommendation produce a call, visit, or sale? | Self-reported attribution; leads, calls, clicks ## Takeaways * **Rankings up 20–30%, clicks and calls down 40%.** Andrew's 100-location franchise client grew rankings year over year while leads fell — and he can't say whether AI Overviews, more ads, or declining interest is to blame. Mike's read: it's the ads. _(25:17–25:50)_ * **A storage SERP: 43% ads and three organic results in five screens.** Mike's mobile teardown of a low-funnel storage query found ads front-loaded and better-dressed than local — calls to action, reviews — a third of the pack itself sponsored, and the first organic listing on screen three. _(25:50–29:10)_ * **ChatGPT's local answers used zero Google reviews.** In a summer study with Local Falcon covering several million local queries, Local SEO Guide found ChatGPT's local sources were Bing, Foursquare, Yelp, TripAdvisor, BBB, and editorial "best of" lists — no Google review data at all.[^1] _(13:57–14:28)_ * **Review count stops lifting AI visibility at about 200.** Across eight verticals, more reviews correlated with more AI visibility on both Google and ChatGPT up to roughly 200; past that, visibility plateaus but the radius a business can rank in keeps widening. _(14:28–14:59)_ * **You can't be ranked if you're never considered.** Mark's framing: a model first filters for candidates on the facts it has — parking, pricing, accessibility, happy hour — so a missing attribute means exclusion before ranking starts. The job is the "largest surface area possible" across site, listings, and every other surface. _(03:05–04:34)_ * **Content with no search value can win in conversation paths.** A vet ranked #1 in Google for "what is doggy dementia" got zero in-market clicks in a year; in an LLM conversation that narrows from "vet near me" to "my dog has dementia," the same post becomes the reason to recommend the practice. _(16:18–17:08)_ * **Search AI for your own prices.** A competitor's page or a Reddit thread may say you charge $50 when you charge $100 — Andrew calls asking AI about your pricing the easiest way to find out your numbers are out of whack. _(20:57–21:31)_ * **Real press moves ChatGPT; cheap tricks decay to zero.** A client quoted in Business Insider and Reuters saw an instant visibility pop lasting about three weeks. In noisy niches, "low calorie cheap tricks" — self-issued "10 best" press releases — fall back to zero the moment you stop. _(45:22–47:37)_ * **The PI firm that quit Google ads lost its leads.** One of its market's biggest personal-injury firms stopped advertising because "we can't make the math work in Google" — and its leads largely vanished. Andrew's rebuild: 30 days of prompt tracking, pattern-mining the responses, listicle-style content, and one declarative brand claim pushed onto every profile that exists. _(40:16–45:22)_ * **Ad-skeptics still click ads — and conversational ads are next.** Near Media's videos show 20–25% of users verbalizing that they ignore sponsored results, yet some still choose from LSAs and ads. Andrew hears ChatGPT ad performance for local is improving, and Google's AI Mode ad formats include one users can question directly. _(29:39–31:41)_ * **A sub-$500 monthly ad budget buys almost nothing.** Tiny SMB budgets can't win impression share in competitive markets, so they burn out — while CPCs rise roughly 25% a year, forcing the desperate to keep upping spend. _(31:41–32:25)_ * **Ask every lead how they found you.** Andrew's number-one recommendation: capture self-reported source on forms and calls. Tool-reported "10% more ChatGPT visibility" can't be tied to a lead; enough self-reports — even when "ChatGPT" stands in for Claude — can justify the spend. _(52:07–52:46)_ ### Related Reading * EP 274 — Why "Position One" Doesn't Exist in Local Anymore (Brighton SEO Recap) — Part 1 with Andrew and Mark; the "visibility as distribution" argument Mark returns to here. * EP 269 — Tactics for Building a Local Brand in the Age of AI Search (Agency Panel) — Andrew on the brand-series panel; the earlier version of his "press moves AI visibility" case. * Local AEO 2026: The AI Visibility Masterclass — Local SEO Guide's ChatGPT source study. * Yext: The State of Local Search — the panel Mark moderates with Joy Hawkins and Andrew. * The Blind Spot in Every AI Search Study — Christian Ward — Yext's citation research, and the source Andrew is reaching for on attributes. * Thryv to sell print business for $142 million — the Yellow Pages sale Greg uses to frame Google as the new directory. * Near Media personal-injury research — the legal research program Greg references. ## Practitioner Notes * **Mike's storage teardown and Andrew's franchise data are the same finding.** Rankings rising while calls fall is what 43% ad share on a transactional SERP predicts — the local version of the "it's the ads, not the AI" pattern Near Media keeps documenting, and the EP 273 hotel audit's ~40% ad share. Pairing a pixel-share audit with a client's call data is a concrete way to show them where the lost leads went. * **Google's new attribute Q &A is the "consideration" layer in GBP form.** Mark's case that missing attributes mean exclusion, plus the business-dashboard Q&A Mike flags and the review-prompt tactic from EP 274, make an attribute-completeness audit a GBP deliverable in its own right — one that feeds Google's local graph and every model that scrapes it. * **The PI firm that quit Google is the white paper's argument in one client.** A firm that went dark on ads and lost its leads is the live case for the brand thesis in the Choosing a Lawyer research — and Andrew's press-driven visibility pops show brand is buildable, while his "cheap tricks decay to zero" is the cautionary half. * **Test the Google-reviews finding against Near Media's own data.** Andrew's study found no Google reviews in ChatGPT's local sources, while Mike argued in EP 274 that ChatGPT still scrapes Google's local data. Both can be true — facts versus reviews — and the distinction decides where a local business's review effort pays off in AI. ### Pick your starting point: 00:00 Welcome back: Part 2 01:00 Productizing AI visibility 04:34 Is AI optimization really different from SEO? 07:23 Who actually asks for ChatGPT rankings? 11:50 What reviews do for AI visibility 13:57 ChatGPT's local sources: no Google reviews 14:28 The 200-review plateau 16:18 Doggy dementia and conversation paths 22:26 The AI visibility measurement problem 25:17 Rankings up 30%, calls down 40% 25:50 Storage SERP: 43% ads 29:11 ChatGPT ads and conversational ads 37:25 Google as the new Yellow Pages 42:49 The PI firm playbook 46:02 Real PR vs. cheap tricks 52:07 Ask every lead how they found you ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

Local discovery is now a fragmented set of tools no single platform controls — so as Google's results fill with ads and its regulated modules get pushed around, and as AI assistants ride device defaults into everyday use, the advantage shifts away from optimizing the search result and toward the real-world brand demand and platform distribution that decide who gets surfaced at all. ## Takeaways * **Discovery fragmented, but the human referral didn't.** Across roughly a thousand consumers in several verticals, search sits near 54% and standalone AI near 38% at the discovery stage — yet offline, human-to-human referral has held around 30–35% since Mike's 2015 survey of lawyers. Digital keeps expanding without eating the personal referral. _(02:48–04:16)_ * **AI collapses at the bottom of the funnel.** People lean on AI heavily to understand a problem and compare options, then largely abandon it to make contact or buy — a trust gap for local information, with Google's AI Mode a partial exception. Top and mid funnel show real AI usage; the bottom "falls way, way off." _(06:08–07:03)_ * **Attribution is a diminishing return.** These were screened, real purchase paths recalled from memory across multiple tools, so last-click tells you where someone arrived, not where they decided. "It's more important to understand customer behavior… than to invest tons and tons of effort into attribution." _(08:00–08:55)_ * **Brand is the hidden entry point.** Done right in a local market, brand simultaneously feeds AI, Google, and social — and it's usually invisible to companies staring at their funnel trying to reverse-engineer what customers do. _(08:55–09:14)_ * **The DMA finding is sweeping — and it's €460M.** The Commission's Article 6(5) self-preferencing decision carries a €460 million fine (the search portion of a combined €890 million penalty), orders cease-and-desist within 60 days, and reaches across roughly nine services — comparison shopping, Flights, trains, the hotel finder, Places/the local pack, and Maps among them. Noncompliance has run since March 2024. _(11:39–13:20)_ * **"The local pack is just search" — rejected.** Mike sat in DMA meetings 2 years ago where Google argued the local pack was simply search, not an added module; the Commission explicitly rejected that, treating it as a self-preferenced module alongside ads because of its added functionality. _(11:39–12:05)_ * **AI Overviews are the untouched flank.** The decision says nothing about AI Mode or AI Overviews — so if Google routes self-preferencing through AI Overviews with third-party links, it may sidestep the ruling entirely. The remedy also lets Google _either_ elevate third parties or bury its own modules. _(13:54–14:10)_ * **The hotel SERP is ~40% ads on both sides of the Atlantic.** Mike's mobile audit (Berlin vs. US, first four screens ≈ 99% of use) found sponsored ads occupying about 40% of the real estate in _both_ markets, with the "hotel finder" functioning as just another ad surface dominated by booking.com. Google's claim that EU results are "the worst consumer experience ever" reads as self-serving — the results are bad everywhere. _(14:15–18:35)_ * **Siri's growth is a distribution story, not a quality one.** Apple cites 2.5 billion Siri interactions a day (Mike figures half are false positives), an S-curve that inflected in 2024 as Apple surfaced Siri more aggressively — usage climbed _despite_ years of quality criticism, because it's the default on a rising iPhone base. _(20:24–24:07)_ * **Wearables become the AI's input layer.** New Apple Watch chips enable on-device conversation transcription for accessibility, and $125 AirPods 5 add audio understanding — positioning the phone as the AI hub with body-worn devices feeding it, and raising Mike's open question of whether the app survives as the interface. _(24:59–26:47)_ * **Apple's privacy moat is the real asset — and it's under pressure.** The counter-example: LG smart TVs caught scanning home networks and logging standby audio for ad revenue. Apple's privacy halo is what keeps Mike on its hardware — but Maps naming decisions (Gulf of America, then Lake Ontario) and a South Park rebrand mocking Apple and Google show the brand taking hits. _(27:11–30:28)_ * **For the hosts, Ask Maps beats general search.** Greg says AI assistants and Google's Ask Maps have "almost completely eliminated" his Google search use; once the habit breaks, "the mystique is completely gone." Google's durable moat isn't general search — it's the local data set behind Maps. _(37:25–40:03)_ ### Related Reading * Lastmile Retail — the retailer-facing local-marketing platform behind the consumer-journey research previewed here; full findings due at Brighton SEO. * SOCi's Consumer Behavior Index — Damian Rollison's research on fragmented local discovery, the earlier data point the hosts build on. * European Commission — €890M DMA decision against Google — the official announcement; the €460M search self-preferencing fine is one half of it. * Nine services named in the EU search decision — the module-by-module breakdown (shopping, Flights, trains, hotels, Places, Maps, translation, and more). * Instagram's Mosseri on Australia's algorithm opt-out debate — the "chronological feed will overwhelm users" argument the hosts compare to Google's. * Apple's foldable iPhone Duo event — CEO John Ternus, the iPhone 18 Pro, Siri, and the $2,000 Duo. * LG smart TVs scanning networks and logging audio — the surveillance counter-example Mike raises. * The Near Media brand-building series (five episodes) — the running argument that brand is the entry point into AI, Google, and social; see nearmedia.co. ### Concepts * **Decide / where-to-buy stage** — the bottom-funnel step where AI usage collapses and Google functions as a proxy for offline information (phone numbers, store location, hours). * **Self-preferenced module** — a SERP unit (local pack, hotel finder, Flights) the DMA treats as favoring Google's own service over comparable third parties; the "it's just search" defense was explicitly rejected. * **Places sites / aggregators** — the EU-specific block of third-party aggregator listings that appears above Google's own hotel finder in European results. * **Hotel finder as ad surface** — the framing that Google's hotel module is not a consumer utility but another ad-delivery surface, in practice dominated by booking.com and its subsidiaries. * **Distribution advantage** — the idea that default placement (Siri on every iPhone; Gemini across Search, Android, Chrome, Workspace) drives usage more than product quality does. * **Body-worn AI inputs** — wearables (Watch, AirPods) acting as the capture-and-interface layer that feeds AI running on the phone. * **Appless environment** — a possible future in which AI delivery no longer routes through discrete apps at all. * **Ask Maps** — Google Maps' Gemini-powered natural-language planning feature, used here to assemble a multi-stop day from one complex query. ## Practitioner Notes * **Survey your own customers before assuming the mix.** The research is directional and the channel split compresses or stretches by category — an "immediate 24/7 tow" need behaves nothing like a considered purchase — so measure your own funnel by vertical rather than importing an average. * **Move budget from attribution to behavior and brand.** With multi-tool, from-memory journeys, over-investing in last-click attribution misreads causation; fund the brand and real-world demand that feed every channel at once, which is exactly the throughline of Near Media's brand-building series. * **For local, defend the data layer.** Google's real moat is its local data set — the thing feeding both the (now-regulated) local pack and AI answers — so keeping Google Business Profile and listings accurate is what keeps a business surfaceable as answers get generated rather than ranked. * **Watch the AI Overviews flank in the EU.** The DMA remedy doesn't touch AI Overviews; track how Google reroutes local, hotel, and flights answers through AI surfaces, since that's where self-preferencing can quietly re-form. ### Pick your starting point: 00:10 Intro 00:57 Lastmile Retail consumer-journey research 06:08 AI collapses at the bottom of the funnel 08:00 Why attribution is losing value 08:55 Brand as the entry point 09:50 The DMA Article 6(5) decision 12:05 The €460M fine & what's next 14:15 Hotel SERP audit: EU vs. US 16:09 ~40% of the results are ads 20:06 Apple event: Siri & 2.5B uses a day 24:59 iPhone 18 Pro, Watch & AirPods 5 27:11 The AI-hardware trust problem 28:30 LG TVs & Apple's privacy moat 30:28 The $2,000 iPhone Duo 37:25 Ask Maps & breaking the Google habit 41:07 Wrap & Brighton SEO ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

Local discovery has become a loop, not a funnel — consumers now rotate through search, social, AI, and reviews to corroborate a choice before they act, so the brand that wins is the one present and consistent at every station, not the one that ranks first at any single one. ## The verification loop Rollison frames this year's report around a single idea he calls the **verification loop** _(19:17–22:37)_. Instead of a linear path from awareness to action, each channel is a station on a circle; consumers hop between them — sometimes once, sometimes several times, exiting at different points — and the data bears it out. When an AI tool recommends a local business, almost nobody just goes: Station | What the consumer is doing there | Why they leave for the next station ---|---|--- **AI** | Getting a fast, conversational shortlist or an orientation to a category | It "can't be trusted yet" — only 19% act on the recommendation directly **Search (Google)** | Verifying: photos, reviews, hours, directions, contact info | Still the default utility layer, but increasingly the _checkpoint_ , not the start **Social** | Wanting a visceral, media-first sense of the place (TikTok/Instagram) | A recommendation you watch rather than read; then confirm elsewhere **Reviews** | Cross-checking reputation on dedicated sites (Yelp, TripAdvisor) | Consistency across sources is the thing being tested The loop only exists because every station is still missing something the others have — and that gap is the story. ## Takeaways * **AI usage for local just broke the survey's records.** The share of consumers who used an AI tool to research a local business in the last 30 days jumped from **9% to 52%** year over year — "the biggest leap we've seen in any question since we've been running the survey." Monthly AI usage of any kind climbed from **19% to 60%.** _(10:14–13:00)_ * **Consumers don't trust the AI answer — they verify it.** After an AI tool recommends a local business, only **19% would contact the business directly.** The rest go checking: 33% read reviews, 20% look at social, 16% re-run a search engine, 12% consult multiple sources. In total **~81% verify before acting.** _(21:35–22:37)_ * **Search didn't lose — it changed jobs.** Search as a channel actually ticked _up_ , from **83% to 84%.** People aren't trading search for AI; they're adding AI on top. Google increasingly functions as the verification checkpoint after discovery happens elsewhere. _(14:20–15:03)_ * **No single channel wins the _start_ of the journey.** Across nearly every vertical, a majority of consumers begin somewhere other than a search engine. Restaurants: only **40%** start with search; the other **60%** is spread across social, AI, maps, and review sites. **Property was the one vertical where search held the majority.** _(25:49–28:34)_ * **The starting line is vertical-specific.** **Financial services** was the category most likely to _start on social_ ; **fuel & auto** skewed to **mapping apps** ("gas station near me"). The fragmentation argument is really a per-industry argument. _(26:00–29:18)_ * **Social is now a mainstream local channel, not a Gen Z quirk.** **78%** of all consumers use social networks regularly and **55%** use them to search for local businesses — the maturation of the "40% of Gen Z would rather find lunch on TikTok than Google" finding from a few years ago. _(13:00–14:17)_ * **The AI user skews affluent and millennial.** Millennials led AI-for-local at **63%** (Gen Z and Gen X sat around 48–49%), and adoption rises "basically in a straight line" with household income — roughly **65% at $100k+.** If you're targeting AI users, you're targeting affluent millennials. _(16:13–17:43)_ * **Decision factors don't transfer across categories.** Reviews and word of mouth edge out the field as the top decision driver, but the mix shifts hard by vertical — so pick your battles. Property, restaurants, and hospitality should over-invest in **visual content** ; other verticals can weight it less. _(34:33–37:49)_ * **Surveys tell you attitudes; behavior tells you truth — watch the gap.** Only **18%** said visual content matters in **healthcare** , yet primary-care behavioral research shows patients "scrolling and scrolling" for doctor photos. A survey captures what people _think_ they do; don't let a stated preference override observed behavior. _(38:00–40:08)_ * **Last-click attribution is blind to the loop.** Marketers see the Google referral clearly but not the discovery that led to it. In a multi-channel journey, where the search _starts_ often matters more to the brand than where it ends. _(22:37–23:30)_ * **Local demand is lumpy and habitual — not everyone is searching.** As Blumenthal notes, much local behavior is habitual (people already have "one in every category" they trust); real digital local search spikes on emergencies, a move to a new city, or a "blank-canvas" category like finding a handyman. _(40:38–43:25)_ * **You don't have to be everywhere — you have to be in the few places that matter.** SOCi's AI-visibility work points to a short priority list for most brands: **your own website, Google Maps, Yelp, and Facebook** , with Gemini/AI Overviews and ChatGPT the AI surfaces that carry weight. Reddit gets attention but under-delivers for local transactions. _(46:00–48:09)_ ### Related Reading * SOCi — "The Verification Loop" webinar (Sept 2, 2026) — the deeper, systematic walk-through of this study; register for the replay if you missed the live session. * SOCi Consumer Behavior Index — background on why SOCi built its consumer-side research and how "fragmented local discovery" became the throughline. * SOCi 2026 Local Visibility Index - Local discovery has become a loop, not a funnel — consumers now rotate through search, social, AI, and reviews to corroborate a choice before they act, so the brand that wins is the one present and consistent at every station, not the one that ranks first at any single one. * Damian Rollison at SEL — his ongoing writing on AI and local search. ### Concepts * **The verification loop** — SOCi's name for the current shape of local discovery: consumers rotate among search, social, AI, and review sites to corroborate a choice, rather than moving down a single funnel. The loop persists precisely because no channel is yet trusted or complete on its own. * **Local discovery** — behavior in digital channels where the ultimate intent is a local transaction; broader than "local search" because it spans AI, social, and maps, not just search engines. * **Fragmentation** — the finding that, for nearly every vertical, the majority of consumers _start_ somewhere other than a search engine, so no single channel "wins" the journey. * **Last-click attribution's blind spot** — the gap between the referral a marketer can measure (usually Google) and the discovery, higher up the journey, that actually created the demand. * **Zero-click era** — Rollison's shorthand for the decade in which Google absorbed the directory traffic that once went to sites like CitySearch and YellowPages.com, becoming the default before social and AI reopened the field. * **Habitual vs. one-time local use** — Blumenthal's distinction: much local behavior runs on an existing roster of trusted providers; digital local discovery is triggered by the exceptions — emergencies, relocation, and unfamiliar categories. ## Practitioner Notes * **Category-level research beats generic benchmarks.** As Blumenthal argues, the pathway for a personal-injury lawyer differs from an employment lawyer, and both differ from a plumber. Use industry data like this to frame the questions, then interview _your own_ customers about how they found you — the only reliable way to know where to deploy resources. * **Instrument for discovery, not just conversion.** If last-click attribution is all you watch, you'll credit Google for demand that TV, social, or an AI shortlist actually created. Where a brand's search _starts_ is often the number worth chasing — build the measurement to see it. * **Prioritize a short presence list, then keep it consistent.** For most local brands the high-leverage surfaces are the website, Google Maps, Yelp, Facebook, plus Gemini/AI Overviews and ChatGPT. Being accurate and consistent across those few beats spreading thin across every niche AI tool and directory. * **Match content investment to the vertical's decision factors.** Property, restaurants, and hospitality should treat fresh, locally sourced visual content as a priority; a healthcare brand may say photos don't matter and still lose patients who are silently scrolling for a face — weigh observed behavior against the survey. ) — the sibling brand-performance study benchmarking multi-location brands across search, social, and AI; see also SOCi's finding that AI recommends only a fraction of brand locations. ### Pick your starting point: * 00:11 Meet Damian Rollison (SOCi) * 02:48 Local Visibility Index vs. Local Discovery Index * 04:49 The consumer study: method & sample * 09:53 Google's more nuanced picture * 10:14 AI explodes: 9%→52% for local * 13:00 Social goes mainstream (78% / 55%) * 14:17 Search gained, didn't lose (83%→84%) * 16:13 AI-for-local skews millennial & affluent * 19:17 The verification loop, defined * 20:55 Only 19% act on the AI directly * 25:45 "Where people start" by vertical * 34:20 Decision factors by category * 40:38 Habitual vs. one-time local use * 46:00 The short priority list that matters * 48:09 Final thoughts + close ### Full Transcript --> On-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### Lake Ontario Fiasco As trade negotiations with Canada fell apart, Trump issued an order renaming Lake Ontario "Lake America" A petty rebuke, it follows a similar episode in January 2024, when Trump took aim at another North American neighbor and renamed the Gulf of Mexico the "Gulf of America." At that time, Google and Apple changed their maps to reflect the ridiculous name change. And despite no legal requirement to do so, Google has done it again, embracing "Lake America" (Apple hasn't yet). Google says it relies on the U.S. Geological Survey (USGS) to determine place names and so when a name changes in that database, it changes on Google Maps. Facing immediate and significant blowback, however, the company sheepishly tried to explain its position as a purely "technical" decision. People across social media weren't having it. Other mapping providers at this point haven't followed Google's lead. In addition to Apple (so far), Bing Maps, Mapbox, OpenStreetMap and, especially Mapquest, haven't adopted "Lake America." Mapquest has been the most explicit: "We're not changing it," read a company post earlier today. As a result, Mapquest has captured national coverage, burnished its brand and driven significant app downloads. It's currently number 6 in the App Store, ahead of Google itself. By "showing a spine," Mapquest scored a major brand win. But it's another cringeworthy brand misstep for Google. ****Google vs. Mapquest ### This post is for subscribers only Become a member to get access to all content Subscribe now

Apple, Google, and Meta are all reaching for the same lever — more ad and services revenue from every surface they control — so the pattern that actually matters isn't AI eating search traffic but ads doing it, privacy and child-safety promises bending to the P&L, and businesses paying an ever-larger toll to reach customers they used to reach for free. ## Takeaways * **It's the ads, not the AI, eating clicks.** The hosts' recurring point: Near Media user-behavior research keeps showing that _ads_ — not AI Overviews — are absorbing the clicks that once went to organic and local, as Google blurs the ad/organic line with weak labeling and richer ad units. "All of the traffic allegedly being stolen by AI Overviews is really about ads." _(17:38–22:03)_ * **Apple Maps ads launched with no way to turn them off.** They're contextual and largely on-device rather than personalized, but there's no opt-out — Mike's complaint is that even a top-tier subscriber who'd happily pay can't buy their way out. _(01:43–05:33)_ Independent coverage confirms there's no toggle to disable them. * **The "$150 to start" is really a 15% credit.** Setup is trivial — pick a business, add a card, 25-character promo text — and Apple seeds it with a credit worth up to ~$150/month (15% of up to $1,000 in spend). Home services, crypto, and bail bonds are excluded; personal-injury lawyers likely aren't. _(06:25–09:36)_ * **Apple Maps skews young and high-intent.** Apple markets ~1B relevant searches, a one-in-two action rate, and 57% Gen Z/millennial users. Mike's own client data puts Apple at ~5–7% of last-touch referrals (equal to or above Facebook), and "knocking it out of the park" in hospitality, where Apple partners with Yelp, TripAdvisor, and Hotels.com. _(02:01–09:35)_ * **The privacy brand is the casualty.** Apple built its brand on "you're not the product"; monetizing Maps quietly undercuts that, and the on-device-privacy explanation is too complex for most consumers to internalize. "Whether it undermines the reality, we don't know — but it undermines the message." _(10:56–13:23)_ * **Google will now charge for 20-second missed calls.** Starting Oct 1, missed LSA calls during business hours become billable leads if the caller stays on the line more than 20 seconds — and this lands _after_ Google already stopped allowing most lead disputes. The hosts read it as a straightforward money grab that makes fast, human call handling a direct cost lever. _(13:31–17:38)_ * **LSAs have moved into the main ad org.** Google rolled Local Services Ads out of its standalone local unit and into the broader ads organization — both an income play and a tell about where local monetization is headed. _(17:38–18:12)_ * **"Paid search is getting bigger" means pixels, not market.** Greg's read of Tom Capper's Moz piece: the growth isn't the paid-search _market_ , it's the ad _real estate_ — the pixel height and share of the page ads consume, which Near Media's research sees translating into a rising share of clicks. _(18:12–19:40)_ * **Even ad-skeptics now say "these are ads I like."** Rich local ad units — photos, a map image, reviews, stars — out-inform the local pack, and users who claim to ignore sponsored listings still click them, partly because Google's block-level labeling makes ads read less like ads. _(19:09–20:50)_ * **Ranking now feeds on ad-driven visits, too.** Mike's sharper point: through Chrome behavioral signals, a _successful_ visit lifts organic rank no matter how the user arrived — ad, organic, or third party — so "if your whole search result is ads, you'd better predicate your ranking algorithm on ads as well as organic." _(23:17–24:15)_ * **Meta's $18B settlement is cheap, contingent, and admits nothing.** No admission of liability; the top-line only lands if YouTube and TikTok adopt matching terms; and spread over a decade it's a rounding error against Meta's ad revenue and AI spend. "This eighteen billion doesn't mean eighteen billion." _(24:15–30:55)_ [1] [2] * **The shared discovery pool is the real exposure.** Because the state cases were coordinated as a multidistrict litigation, depositions and documents can flow to the thousands of individual suits still pending — so the deal buys Meta out of _this_ case and case law, but not the paper trail that will fuel the next ones. _(34:36–36:11)_ ### Related Reading * Google Local Services Ads will charge for some missed calls starting Oct. 1 — Search Engine Land on the LSA policy. * Apple Maps Ads Are Here, and There's No Way to Turn Them Off — MacRumors on the Apple Maps rollout. * Meta settles social-media addiction claims with states for up to $18 billion — NBC News on the settlement; see also CNN and Time. * "Paid Search Is Getting Bigger: How SEOs Can Respond" — Tom Capper, Moz. * "EU wants Meta to extend US teen safety restrictions to Europe" — France24. ### Concepts * **Enshittification** — the predictable decay of a platform as it shifts value from users to itself; the hosts' "path of ads." * **Brand conquesting** — bidding on a competitor's brand keywords so your ad appears on their name. * **Pay-per-call & lead disputes** — billing per phone lead rather than per click; the long-running fight over which leads are "valid" and disputable. * **SERP real estate / pixel height** — measuring ad dominance by the vertical space and share of page ads occupy, not by the count of ad units. * **Contextual vs. personalized ads** — targeting from the immediate query and location versus a cross-session profile of the user. * **Product-defect ("big tobacco") theory** — a liability theory that routes around Section 230 by treating the product's addictive design, not third-party content, as the defect. * **Multidistrict litigation (MDL) / shared discovery pool** — coordinated federal litigation in which depositions and documents are shared across all plaintiffs, so discovery need not be re-gathered case by case. ## Practitioner Notes * **Test Apple Maps now, while it's cheap and uncrowded.** Geo-critical, deal-driven categories — storage, hospitality — look like the early-mover advantage, and the 15% credit funds a low-risk test. This is exactly the kind of channel question Near Media's attribution and user-behavior work is built to answer. * **Fix intake before Oct 1.** The LSA missed-call change turns call answer speed and handling into a direct billing lever — the operational other half of EP 267's "you can't outspend a bad intake experience" with Gyi Tsakalakis. * **Arm clients with the "it's the ads, not the AI" counter-narrative.** Near Media's user-behavior research is the evidence that ad real estate — not AI Overviews — is what's compressing organic and local; showing that to clients reframes where the lost traffic actually went, and what to do about it. ### Pick your starting point: 00:00 Intro 01:43 Apple Maps ads launch 03:17 What the ads look like 05:04 Why Apple's doing it 06:25 Building an Apple Maps ad 08:35 Attribution & hospitality 10:56 The privacy brand undermined 13:31 Google's LSA missed-call charge 15:20 The history of pay-per-call 17:38 It's the ads, not the AI 19:09 Why ads now beat organic 22:03 Ranking feeds on ad visits 24:15 Meta's $18B settlement 26:55 The contingent structure 34:36 The discovery-pool problem 36:18 Upbeat albeit idiosyncratic close ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

Brand awareness is a pre-existing advantage that decides which lawyer gets the click long before anyone compares options — so as search fragments across Google, AI Overviews, and third-party sites, the firm people already recognize keeps winning, and a challenger's only real lever is creative strong enough to close the recognition gap. ## Takeaways * **Recognition, not position, drives the click.** In the user-testing video the searcher skipped the local pack, fixated on the Local Service Ads, and zeroed in on the one firm she already knew — Montlick. She then used the firm's BBB page (not accredited, but an A+ rating) only to _confirm_ a choice prior awareness had already made. _(~03:30–05:30)_ * **Fewer than 2% go to AI first to find a lawyer.** Near Media's 2025 national survey found under 2% would turn first to a tool like ChatGPT; Google is the default entry point. AI's real job is background research — understanding the case, prepping consultation questions, comparing options — not sourcing the firm. The 2026 number ticked up but the pattern held. _(~09:00–10:00)_ * **People will still consider an AI-surfaced firm.** Majorities said they're open to hiring a lawyer they saw in an AI Overview or AI mode (roughly 78% would consider one), and ~18% would use AI somewhere in the process — so brand presence in AI matters even while direct referrals sit at 1–2%. _(~10:30)_ * **Two things decide the hire: proven experience and fear of cost.** Ranked decision factors collapse into experience with _my kind of case_ and the financial terms (contingency, free consult). Both are really one thing — minimizing risk and buying trust in a high-stakes, hard-to-judge purchase. _(~15:30)_ * **"We support the community" is a tiebreaker, not a closer.** Respondents _say_ local involvement matters, but in the message test "we support the community" ranked at the bottom of what would actually make them contact a firm — a nice-to-have, not persuasive on its own. It's the classic survey-attitude-vs-observed-behavior gap Near Media keeps finding. _(~17:00)_ * **TV built the awareness — not digital.** Asked where they'd heard of the firms, respondents overwhelmingly said television; traditional media (TV, radio, outdoor, street furniture) far outpaced digital as the _source_ of brand awareness, with ~55% recalling brands from TV. Greg flags this as unusually strong in legal, where firms spend on traditional media at a scale doctors and restaurants don't. _(~20:00)_ * **Known brands get the "local" benefit of the doubt.** Four of the five firms are Georgia-based; the one that isn't — Morgan & Morgan, headquartered in Florida — was most often assumed to _be_ the Georgia firm, purely because it's the most recognized. Awareness overrides fact. _(~21:00)_ * **Great creative can close a 20-point awareness gap.** Montlick trailed Morgan by ~21 points on awareness, yet its ad scored ~10 points higher and lifted Montlick to near-parity on consideration. "If you have powerful messaging, good creative, you can close the gap with a better-known rival as a challenger." _(~22:00–25:00)_ * **…but forced to pick one, people revert to the best-known firm.** At the final single-choice question, Morgan won on brand strength (~38% chose the best-known brand). Strong creative narrows the gap; brand strength still closes the sale. Everyone gets business — brand decides who gets the largest share. _(~25:00)_ * **When brand is absent, reviews are _the_ single biggest factor.** Greg's clearest answer to Mike: if you must name one lever that moves behavior, it's reviews. Not everyone carries brand awareness, and undifferentiated searchers "lean on and rely on" reviews to both qualify and disqualify. _(31:24–33:04)_ * **Brand and performance marketing are interdependent, not rival budget lines.** The either/or framing — all-in on performance, brand is soft and unmeasurable — is the mistake. Brand lifts performance, and both Google and AI favor brands because consumers click brands and Google reads that behavior as signal. _(~26:00)_ * **Offline demand routes back through Google as branded search.** Mike's "full circle," echoing Cyrus Shepard: community and offline activity build awareness that shows up as branded queries, and branded-search demand itself lifts rank — so cheap, local, real-world work compounds online. _(35:22–36:03)_ ### Related Reading * EP 269 — Tactics for Building a Local Brand in the Age of AI Search (Agency Panel) — Part 4; the panel (French, Hawkins, Shotland, McGee) that name-checks the very Atlanta PI study Greg presents here. * EP 268 — AI Overviews, Brand Search & Local SEO: Cyrus Shepard on Earning the Click — why brand search correlates with rankings. * EP 267 — You Can't Outspend a Bad Intake Experience: Brand-Building for Legal — the legal-vertical deep dive with Gyi Tsakalakis; the conversion side of the brand front door. * EP 266 — Cyrus Shepard: What Local Brand Actually Means to Google and AI — the technical, brand-as-entity view behind "Google and AI favor brands." * EP 265 — Decoding Local Branding with John Jantsch — Part 1; "you have a brand whether you build one or not." * Lunch Hour Legal Marketing Summit — the Nashville event (run by AttorneySync and Mockingbird Marketing) where Greg first gave this talk. * Impact of Brand Awareness on Consumer Choice Presentation (PDF) ### Concepts * **Unaided vs. aided awareness** — open-box recall ("name a PI firm") versus recognition from a supplied list. Both produced the _same_ firm hierarchy; aided simply lifts the totals. * **Benefit-of-the-doubt effect** — a recognized brand is assumed to have attributes it may not have (e.g., being a local Georgia firm), because familiarity substitutes for knowledge. * **Qualify vs. disqualify** — reviews, websites, and BBB pages used on the _front end_ to build a shortlist and on the _back end_ to eliminate a tentative choice. * **The say-do gap** — the divergence between survey-stated attitudes ("community involvement matters") and observed behavior (it rarely closes the deal); why Near Media pairs surveys with user testing. * **Brand–performance interdependence** — treating brand and performance marketing as mutually reinforcing rather than competing line items; brand lowers the cost and lifts the yield of performance spend. * **Local Service Ads (LSAs)** — Google's pay-per-lead units atop local results, styled nicer than the local pack (photos, reviews, Google Screened badge); the module that repeatedly captures attention, especially on mobile. ## Practitioner Notes * **The user-testing video is the proof layer surveys can't provide.** The say-do gap and the discover-in-AI/validate-on-Google pattern both argue for Near Media's user-research methodology — watching a real searcher move from LSAs to website to BBB is what exposes behavior a survey would miss, and it's a differentiated deliverable for firms drowning in attribution noise. * **Ad-creative testing is a shippable service, not just a study.** Montlick's ~10-point creative lift over a far-better-known rival is measurable, market-sourced feedback — exactly the "we use the market to give feedback to our clients" offer Greg describes, and a way to de-risk media spend before a firm commits to it. * **This episode is the front door; EP 267 is the conversion.** Brand awareness wins the click and reviews carry the undifferentiated searcher — but "you can't outspend a bad intake experience." The awareness findings here only pay off if the intake and review engine behind them holds. ### Pick your starting point: 00:00 A different episode: original brand research 02:30 The video: how one searcher chose a lawyer 04:00 Debrief: LSAs, recognition, the BBB check 06:30 The SERP changed — ads crowd out organic 09:00 Under 2% start with AI — where AI fits 11:30 The study: 350 respondents in Atlanta 15:30 What matters most: experience and cost 18:30 Brand awareness: unaided vs. aided 20:00 TV builds the awareness, not digital 22:00 Ad-creative testing: Montlick tops Morgan 25:00 Creative closes the gap — brand wins the pick 26:00 Brand and performance are interdependent 31:24 When brand is absent, reviews decide 33:19 Would the findings hold elsewhere? 35:22 Full circle: demand drives rank 36:47 Wrap and next week (EP 271) ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

Four agency operators — Paula French of Search Influence, Joy Hawkins of Sterling Sky, Andrew Shotland of Local SEO Guide, and Matt McGee of SEO Savvy Agent — join Greg Sterling and Mike Blumenthal for Part 5 of the Near Memo brand-building series, closing out the run of solo interviews with a working session on what "brand" actually means for a local business and which tactics move the needle now that answers are generated, not just ranked. A local brand is prominence plus goodwill made legible to machines — a name people connect to a query and a set of third-party corroborations Google and AI can verify — so as the results page fills with ads and AI Overviews, the winning work shifts away from your own site and toward real-world demand, off-site mentions, and content only you can produce. ## How the panel defines a local brand Every guest was asked the cocktail-party question — "what does brand even mean locally?" — early on (03:21–05:25). The four answers stack into a usable working definition. Practitioner | Their definition of a local brand | The lever it implies ---|---|--- Matt McGee | **Prominence + goodwill** — people know your name and associate it with something good | Name recognition; do offline + community work, not just marketing that "looks the same" Andrew Shotland | **Brand-as-query** — do people connect your name to a search or LLM query | Drive branded search/LLM demand; measure it Mike Blumenthal (via John Jantsch, EP 265) | You have a brand whether you build one or not — it's how customers perceive you | Fulfill your promises or the brand is defined by your negative reviews Joy Hawkins | The entity AI actually recommends for "best [x] in [city]" | Earn third-party mentions; reviews alone no longer decide it ## Takeaways * **Pure brand mentions moved AI visibility ~400% in a week.** Andrew's startup client "that really doesn't have a brand anyone has ever heard of" got featured in Business Insider and Reuters; tracked prompts across ChatGPT and Perplexity rose about 400% and Google organic about 150% in a single week off a small baseline. _(18:47–19:41)_ * **Third-party mentions now outweigh review count for "best" queries.** The number of Google reviews used to be "the primary dictator" of who ranked; in AI results Joy sees off-Google mentions carry more weight, and she has "yet to see a fake listing or a lead gen listing rank in AI." _(07:37–08:00)_ * **LLM referral traffic is still 1–2% — but self-reported discovery is much higher.** Across B2B and consumer, direct LLM referrals sit at one to two percent of leads, yet when leads self-report, "I found you on AI" is far more common. People discover in an LLM, then validate on Google, so the click logs as a Google referral. _(13:20–14:25)_ * **Local listings are the cheapest brand-mention engine for AI.** Andrew's "best secret weapon" — one business on a hundred directories is a hundred brand mentions; update the non-local citation platforms too ("we are a hot water installer in Sherman Oaks"). _(43:28–44:34)_ * **A reusable "fast expert" mini-bio scripts what LLMs say about you.** Matt gives agents a two-to-three-paragraph bio — awards, career sales numbers — dropped identically onto the Google Business Profile, Zillow, and every citation site, so the machines repeat the framing you chose. _(44:35–45:00)_ * **YouTube is a top lead source and the most AI-proof play — on a multi-year horizon.** For Sterling Sky it's the number-one lead source, but it cost ~200 internal hours a month and produced no leads in year one; "AI can't do it, so you're not competing with anything automated." Roger Wakefield is the small-business model to copy. _(32:49–34:28)_ * **YouTube is the #2 most-cited domain in local AI answers.** In a study Andrew ran with rank-tracker Nozzle, YouTube came second across millions of local searches — and a phone-shot video can surface in an LLM faster than it would ever rank in Google or YouTube search.[1] _(35:42–36:37)_ * **Offline and QR brand-building is back.** Matt put banner ads with QR codes at the local-airport baggage claim for Cari McGee — captive audience, phone in hand — and trains the audience with a standing CTA: "for more information, Google _Cari McGee housing market_." _(06:55–07:33, 21:19–22:00)_ * **"Competitor jiu-jitsu" wins LLM queries cheaply — if the client will allow it.** Write comparison content against a rival (or an adjacent "non-competitor competitor") and you'll surface when someone searches that name in AI; it's routine in SaaS, underused in local. Joy's counter: her clients "would flip out" over litigation and outreach fears. _(30:04–32:12)_ * **Data-rich, niche, client-voiced content is the only content worth making.** Joy interviews clients on monthly recorded calls and writes from their words — a Noah Learner-built tool turns the transcript into copy — because "if you use AI or an SEO to write your content, you're gonna get the same crap that already exists on the web." Skip anything with 5,000 copies already online. _(28:50–29:22, 47:26–48:33)_ * **Original research + a credible partner still works the old-fashioned way.** Search Influence became a platinum partner of UPCEA and co-branded four rounds of original research, then spun it into landing pages, webinars, and derivative posts to build a "higher education SEO agency" foothold — a traditional content-and-partnership play that still pays. _(23:37–28:19)_ * **Scaled content and scaled links are now the risk, not the shortcut.** Joy's closing warning: the old cheat codes — scaled/AI content, scaled link building — are exactly what she's seen small businesses "get nailed for" this year, while effortful marketing gets rewarded. Ross Simmonds' framing: "back to foundational marketing." _(50:55–51:43)_ ### Related Reading * EP 265 — Decoding Local Branding with John Jantsch — Part 1 of the brand series; the "you have a brand whether you build one or not" thesis Mike cites here. * EP 266 — Cyrus Shepard: What Local Brand Actually Means to Google and AI — Part 2; the technical, brand-as-entity view. * EP 267 — You Can't Outspend a Bad Intake Experience: Brand-Building for Legal — Part 3; the legal-vertical deep dive. * EP 268 — AI Overviews, Brand Search & Local SEO: Cyrus Shepard on Earning the Click (from the archives) — the archive companion on why brand search correlates with rankings and what AI Overviews do to that ecosystem. * The State of Local SEO in 2026 — Joy Hawkins' data behind the panel's "organic squeeze": local-pack ads grew from 1% to 22% of tracked reports across 2025, and LSAs from 11% to 31%. * Ross Simmonds interview (Sterling Sky) — the conversation Joy references on-air; the case that real, foundational marketing is what's rewarded now. * Yelp licenses reviews to OpenAI for ChatGPT — the formal deal behind the panel's "Yelp is getting cited in AI" point. * Roger Wakefield — the small-business YouTube model Joy names. * Nozzle — the rank-tracking tool behind Andrew's local-AI citation study. ### Concepts * **Prominence + goodwill** — Matt McGee's plain-language definition of a local brand: people know your name, and the association is positive. * **Brand-as-query** — Andrew Shotland's mechanical test: whether people connect your name to a search or LLM query; the thing you can actually see move. * **AI local pack** — the AI Overview answer for "best [category] in [city]," sometimes a ranked list, sometimes a paragraph naming three or four businesses; often different from the classic local pack. * **The "fast expert" mini-bio** — a fixed 2–3 paragraph bio (awards + performance stats) reused verbatim across GBP, Zillow, and citations to control what LLMs say about a business. * **Competitor jiu-jitsu** — comparison content aimed at a competitor or adjacent business so you surface in AI answers to queries about _them_. * **Local listings as brand mentions** — treating directory citations as volume brand-mention signals for LLMs (100 listings = 100 mentions), not just NAP hygiene. * **"At bats" / .300 average** — Andrew's frame for AI tactics: cheap, high-volume experiments where a 30% hit rate is a win. ## Practitioner Notes * **The discover-in-AI / validate-on-Google pattern is a measurement problem Near Media already tracks.** The panel's repeated observation — leads self-report "found you on AI" far above the 1–2% referral logs — argues for Near Media's user-research video work (the Atlanta PI example Greg cites at 11:14) as the way to see the real path, since analytics will keep miscrediting Google. * **"Local listings as brand mentions" reframes citation audits.** If directory presence is now an LLM brand-mention lever rather than a NAP-consistency checkbox, a citation gap analysis becomes a brand-visibility deliverable — worth pairing with the Yelp–OpenAI reality that a thin Yelp profile is now a hole in the answer, not just a missing directory entry. * **The "fast expert" bio is a concrete GEO artifact clients can ship this week.** It operationalizes the brand-as-entity theme from EP 266: one controlled description, syndicated across profiles, is exactly the corroboration layer an LLM assembles an answer from. * * * 1. Editorial footnote (speaker-side): Andrew cites the Nozzle study as "15 million local searches" at 35:42 and "1.5 million local services queries" at 52:41 — correct the figure in show notes. A separately published OtterlyAI study reached a similar "YouTube #2" conclusion. ↩︎ ### Pick your starting point: 00:00 Welcome & the panel 03:21 What "brand" means locally 04:50 You have a brand whether you build one or not 06:38 Baggage-claim banners & out-of-home 07:37 How AI treats "best" queries 09:41 The organic squeeze 11:14 Discover in AI, validate on Google 15:04 Why ads are eating clicks 18:47 Brand mentions & the ~400% week 21:19 Matt's playbook: PR, QR codes, community 23:37 Higher-ed via UPCEA research 30:04 Competitor jiu-jitsu 32:49 YouTube as the AI-proof long game 43:24 Local listings as brand mentions 50:55 Scaled content & links are the new risk 51:45 Reviews, Yelp in AI & sign-off ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

> **Editor's note (replay):** This is a vacation-week re-run. The conversation was originally recorded and aired in April 2026 as **Near Memo EP 251** ; it is being republished now (EP 269) because it still speaks directly to our ongoing series on brand in local. A live panel on local digital brand strategy follows next week. Google can't tell what good content is on its own, so it watches what users do after they click — which means the durable levers are the ones that manufacture real-world demand and complete the user's task, not the ones you tune on a title tag. ## Cyrus's Three Click Signals Cyrus frames Google's post-click measurement as three nested signals, stated around _(05:06–06:54)_. This is the spine of the episode — everything else (brand search, AI Overviews, on-site engagement) is a way of feeding or proxying these. Signal | What it measures | The primary lever ---|---|--- **Clicks** | Did anyone pick this result at all — Google's raw guess being confirmed or rejected | An attractive SERP listing: title matching intent, review stars, high-contrast favicon **Long clicks / dwell time** | How long the user stays before returning; sliced by query type, language, location | Answer the query fast, then give people more worth engaging with (navigation, related content) **Last longest click** | The user's journey ended here — they didn't bounce back to search for something else | Genuine task completion: reviews, booking, pricing, "who will I work with" — owned on your property ## Takeaways * **Google was "gaslighting" us on clicks.** For years Google denied using click data while Bill Slawski documented the patents and reps were under a stated no-comment policy; the antitrust trial then confirmed it's one of the top signals alongside content and links. _(02:06–03:36)_ * **Three sources, one story.** The court testimony, the Google patents, and the API leak independently line up — the leaked endpoints match what surfaced in court, giving a rare cross-confirmed picture of what data Google collects. _(03:36–04:26)_ * **Last longest click is the conversion proxy.** It's Google's stand-in for "task completed" — the user got what they wanted and never returned to the SERP; Cyrus reframes it as "I've completed whatever I set out to do." _(06:36–07:15)_ * **Own the whole task, don't rent it.** If reviews or booking live on Yelp instead of your site or profile, the satisfying action happens on someone else's property — AJ Kohn's advice is to consolidate the data you can so users never have to search elsewhere. _(08:57–10:48)_ * **Brand search is a first-class ranking signal.** Strong branded query volume "always, always" correlates with higher rankings in Cyrus's correlation studies; Google's helpful-content guidance wants either an existing audience (brand) or a clear intended one. _(11:31–12:36)_ * **Rand proved clicks a decade before the leak.** At a MozCon keynote (~2013), Rand Fishkin had ~1,000 attendees run the same search and click one business — it shot to #1 within the hour, foreshadowing the DOJ documents by a decade-plus. _(12:36–13:35)_ * **Off-search demand counts too.** Google can read popularity signals from Chrome, so traffic driven by a TV spot, billboard, or radio ad registers as demand even when you're not ranking — "there must be some demand there." _(13:35–14:23)_ * **AI Overviews mostly benefit Google.** They cut traffic to information publishers (the cost of mediocre info is now zero) while keeping users on Google, whose AIO links often point back to more Google search. _(15:32–16:20)_ * **Google is guesstimating clicks at the edges.** For long-tail queries Google reportedly built lighter systems — Cyrus cites FastSearch and a "RankEmbedBERT"-type model [ear-check] — using ~10× less data plus 20 years of history to predict clicks and verify with far fewer real ones. _(16:20–17:08)_ * **In local, AIOs can increase downstream clicks.** Near Media's personal-injury research (presented at the Vegas legal-marketing summit) shows consumers use AIOs to orient, not to finish — they aren't lawyers, so they still click through, often via follow-up brand searches. _(18:07–20:33)_ * **Watch branded search as your AIO scorecard.** Google's new branded-query filter in Search Console makes it a one-click read: rising brand search likely means you're being cited in AI Overviews; falling brand search is the warning sign. _(20:33–21:23)_ * **Don't fake the click.** A too-clickable title that over-promises lifts CTR for a couple weeks, then traffic drops as users bounce — "you're making a promise with your title and the result has to deliver that promise." _(23:15–24:00)_ * **Navigation is an underrated engagement weapon.** Testing different nav links and wording measurably moves time-on-site; Cyrus's favorite GA4 report is organic engagement, which tracks with downstream rankings. _(24:00–25:18)_ * **Let people leave satisfied.** It's fine if a visitor gets a quick answer and goes — brute-force pop-ups meant to force a "last longest click" backfire; delay any video pop-up 30–45 seconds and make it dismissible. _(26:31–27:41)_ ### Related Reading * Zyppy Signal (Cyrus's Substack) — Cyrus's click-data and AI-search research, including the click-signals post discussed here. * Cyrus Shepard at Zyppy — bio and the firm's SEO testing work. * SEO by the Sea — the late Bill Slawski's patent analysis, referenced as the early source on Google's click-data patents. * Blind Five Year Old — AJ Kohn — the "own the data in one place" argument Cyrus cites. * The Near Memo — the full episode archive; this replay ties into the current brand-in-local series. * Google vs. DOJ, strategic SEO recap — background on the antitrust findings on click data. ### Concepts * **Clicks / long clicks / last longest click** — Google's three-tier read of post-click behavior; the last longest click (journey ended here, no return to search) is treated as the strongest signal. * **Dwell time** — how long a user stays on a result before returning to the SERP; sliced by query type, language, and location. * **Task completion** — Cyrus's reframe of a satisfied query: the user finished what they set out to do, whether or not money changed hands. * **Brand / branded search** — volume of people searching for you by name; a real-world demand signal Google reads as popularity and, per Cyrus, a reliable ranking correlate. * **Off-search demand signals** — popularity Google infers from sources like Chrome traffic, so demand created by TV, radio, or billboards still registers. * **Guesstimated clicks** — Google predicting click behavior with lightweight models (FastSearch / "RankEmbedBERT"-type systems) for long-tail queries, verifying with far less live data. * **Earn / get / end** — Cyrus's on-site sequence: earn the click (honest, attractive SERP listing), get the engagement (answer fast, draw them deeper), end the journey (satisfy intent so they don't return to search). ## Practitioner Notes * **Brand search as the AIO proxy Near Media already recommends.** The show's local research (personal-injury law) and Cyrus's advice converge: since AI Overview presence is nearly impossible to track at scale, use the Search Console branded-query filter as the standing scorecard for whether AIOs are feeding you demand. * **Consolidate reviews and booking onto owned surfaces.** For local operators, moving the satisfying action (reviews, booking link) onto the site or Google Business Profile — rather than sending users to Yelp — is the concrete way to earn the last longest click Cyrus describes. * **Editorial footnote for show notes:** the algorithm names in the AI Overviews segment ("FastRank," "BedBERT") are almost certainly mis-transcriptions of Google's long-tail systems (FastSearch and a RankEmbedBERT-type model) — confirm against the audio before quoting them. ### Pick your poison: 00:00 Replay intro 00:38 Welcome & guest — Cyrus Shepard 01:46 Clicks as a ranking signal: the backstory 03:04 The antitrust trial: Google confirmed it 04:26 API leak: three sources, one story 05:06 The three click types 06:36 Last longest click = task completion 07:28 Bringing it to local & GBP 08:57 Own the task: reviews & booking 11:31 Brand search as a ranking signal 12:36 Rand proved clicks at MozCon 14:23 AI Overviews: who really benefits 16:20 Guesstimating clicks for long-tail 18:07 Local: AIOs drive more clicks, not fewer 20:33 Brand search as your AIO scorecard 23:15 Earning, getting & ending the click ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### Local Search Still Starts with Google According to our research, somewhere between 60% and 70% of AI users have used AI for local search. There are questions about frequency and trust. But regardless of what industry you're in, AI is probably somewhere in your customer's journey – top and middle of the funnel most likely. And while Google has remained largely dominant in local, that doesn't mean nothing has changed. Consumers may start or end with Google but there are other channels in the mix, including social and AI. BrightLocal's recent consumer survey data show a significant majority of local searchers consult more than just Google in their buying process. Google is the starting point for 52% of local searchers and used by 71% at some point in the journey. Not surprisingly, mobile is the device where most (73%) local searches start. The majority of people, however, consult at least one channel beyond Google. The survey documents that the number of people using AI for local search has grown, but only a minority (18%) are comfortable contacting a business directly from AI without further investigation. Google is typically that site, which AI users visit for reviews, directions or contact details. AI is often used for discovery and establishes the competitive set (the "shortlist"). Google usually provides validation and "last mile information." Conclusion: Google remains a critical pillar of local search but it's not the only one. ****Where People Go After an AI Local Search Source: BrightLocal n=1,227 US adults who had looked for a local biz in past quarter ### This post is for subscribers only Become a member to get access to all content Subscribe now

Brand in legal is reputation and recognizability — so in a market where every firm has 700 reviews, a 4.9 rating, and the same "we fight hard" ad, the decision is won _before_ the direct-response moment, through intake, service delivery, and demand you manufacture in the real world and route back through Google. ## Gyi's Growth-Stage Segmentation Gyi's advice is gated on where a firm sits in its growth journey — "that's question number one for me always" _(22:25)_. The playbook is different at each tier. Tier | The problem it answers | Primary lever ---|---|--- **Startup** ("hung my shingle last week") | No compelling message, no reviews, no recognition — can't win the auction | Stay out of the "most-reviews-wins" contexts entirely; spend where recognition isn't the deciding factor **The 5-to-10** (most AttorneySync clients) | Can't outspend the top firms, but has a competitive review base | Differentiate on brand positioning + service delivery; manufacture recognition before the LSA moment **Top 3** (saturation advertisers) | Capturing knee-jerk, first-name-that-pops decisions | Hundreds of millions in offline spend — a game the other tiers shouldn't try to play ## Takeaways * **Brand in legal is two words: reputation and recognizability.** Recognizability is whether your name pops into someone's head; reputation is what that name makes them feel. "It's the thoughts... what they think about you." _(03:12–03:49)_ * **You can't outspend a bad intake experience.** The internet democratized reputation — unhappy people leave reviews and post on social. Amplifying bad service delivery with ad spend just "hurts your reputation and your conversion rates." Invest in intake _before_ dropping money on Google Ads and LSAs. _(07:21–11:37)_ * **Race to the phone is table stakes in PI.** LSAs prompt the consumer to contact several firms at once, so speed is baseline — but "race to the phone _with amazing service experience_ " is the real bar. Firms lose won cases in the first intake interaction. _(19:42–20:11)_ * **Get reviewed for your intake — even by people you reject.** A prospect you can't sign will still post "they didn't hire me, but they listened and pointed me in the right direction." Mike cites a firm that put a lawyer on non-active cases purely to deliver "sophisticated rejections," and it generated a stream of five-star reviews. _(13:29–15:00)_ * **Sign them up first, qualify later.** Gyi's deliberately controversial take: after a few basic qualifiers (accident within ~2 years, injuries), "text them a retainer and get them signed up now," then investigate and turn down later. Cherry-picking upfront loses the client — "by the time you've done your research, they've already contacted a couple other firms." _(16:25–17:07)_ * **Response-time tolerance is per-channel and demographic.** Text, web form, call, and email each carry different expectations; miss the window and the lead moves on. Google/LSA also watches back-end response time and dings slow responders — a point reinforced by ex-Google LSA specialist Eric Levine and by Aaron Weiche's mystery-shopper research on slow lead handling. _(18:20–19:42)_ * **The choice is made before the LSA, not inside it.** When every listing is a middle-aged white guy with 700 reviews, a 4.9, and 25 years in business, "by the time someone's looking at the LSAs, it's already too late." The winner is the one who "did backpacks at my school" or "spoke at my event." _(26:23–28:42)_ * **Manufacture brand demand and route it through Google.** The opt-in play: a community booth or restaurant co-marketing giveaway where the _only_ sign-up path is a Google Business Profile post linking to an unindexed page — "search for us, find our GBP, click through." It drives a brand search, drops a retargeting cookie, and builds an email list. Gyi and Mike concede it's "borderline click manipulation," but "it works very well." _(31:00–34:11)_ [See editorial note.] * **Track leading indicators, not last click.** Brand-building has no last-click attribution. Watch brand queries in Search Console and Google Ads, and run consumer-recognition surveys, instead of demanding an ROI number the channel can't produce. _(29:06–33:22)_ * **Content strategy is reprioritized SEO, not new SEO.** Long-tail blog pages are deprioritized; short-form video is up, because "no one wants to read" and Google cites its own YouTube in both classic results and AI. Turn lawyers into creators: record an hour, cut it into FAQs and positioning clips, distribute across YouTube, LinkedIn, Reddit, Quora, and legal directories — the sources the machines cite. _(35:38–38:23)_ * **Make the invisible work into content.** Michigan Auto Law's "3K for Free" campaign packages a state mini-tort rule (up to $3,000 for vehicle damage) into "three things you need to know." Josh Hodges, the Hamilton, Ohio "hometown lawyer," interviews local business owners until "everybody knows him" — digital PR as local celebrity. _(38:48–41:28)_ * **AI optimization is a PR game.** For AI answers, "it's mentions" — get people publishing about you, reviewing you, talking about you. But contextualize the hype: most local consumers still use Google, not AI, for bottom-of-funnel lawyer lookups, and GEO "visibility tracking" is shaky because OpenAI doesn't release the actual prompts. On July 30, a logged-out ChatGPT query for "car accident lawyer near me" returned Google Business Profile reviews and URLs still carrying GBP UTM parameters — "you don't think Google matters in ChatGPT world? Take another look." _(43:58–49:56)_ * **If you're a one- or two-channel marketer, your days are numbered.** CPA is climbing and PE-backed national players have the operational efficiency to tolerate higher cost-per-client. Diversify so you're "not beholden to any single channel." _(50:43–51:27)_ ### Related Reading * EP 265 — Decoding Local Branding with John Jantsch — Part 1 of the series: what "brand" means in the general context. * EP 266 — Cyrus Shepard: What Local Brand Actually Means to Google and AI — Part 2: the technical side of how Google and AI model a local brand. * AttorneySync — Gyi Tsakalakis's legal marketing firm and a partner in the Near Media legal research program. * Michigan Auto Law — "3K for Free" — the short-form-video campaign Gyi cites as a model. * Kruger & Hodges, "The Hometown Lawyers" — Josh Hodges's local-celebrity content strategy in Hamilton, Ohio. * Google's ATLAS study — Google research on how people use AI for local discovery, then switch to search to act. * Lunch Hour Legal Marketing Summit — the Nashville event where Greg is presenting new lawyer-discovery data. ### Concepts * **Reputation and recognizability** — Gyi's two-word definition of brand: whether your name comes to mind (recognizability) and what it makes people feel when it does (reputation). * **The 5-to-10** — the tier of firms just below the market's saturation advertisers; can't win on spend, so must win on differentiation and service. AttorneySync's core client. * **Sign-up-first intake** — routing prospects to a retainer after minimal qualification, then investigating and declining later, to cut "breakage" between first contact and signed client. * **Get reviewed for your intake** — treating the intake and even the rejection experience as a review-generation surface, not just the representation itself. * **Brand-demand routing** — manufacturing a branded Google search (via a GBP-only giveaway or "Google me" billboard) so offline attention converts into on-Google brand signals and retargetable website visits. * **Reprioritized SEO** — the same digital-PR and content fundamentals, reweighted: less long-tail blogging, more short-form video and third-party mentions the machines cite. ## Practitioner Notes * **The intake-quality thesis maps directly onto Near Media's legal research program.** AttorneySync and Conrad Saam are partners in it, and Gyi credits the user-behavior research with reprioritizing his own thinking — the survey and behavior data on how consumers judge firms is the evidence layer under his "you can't outspend bad service" argument. _(01:16–02:03)_ * **"Manufacture the brand search" is a testable claim, not a slogan.** Greg and Mike tie it to Google's own signals — brand queries in Search Console, satisfied-website-visit behavior, and the ATLAS finding that people research in AI but convert in search. That's a measurable sequence a firm can instrument. * **Editorial note:** the GBP-only giveaway funnel is described on-air as "borderline click manipulation" by the sGyi. We don't view it that way. Driving clicks to Google and having consumers engage with your GBP is a legitimate way to drive brand queries and rank. You can decide for yourself. ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### LSAs Now P-Max PPL Local Services Ads (LSAs) are merging into Google Ads and will run as Performance Max campaigns optimized for pay-per-lead goals. Google says, "This transition allows you to manage all your Google advertising in one unified platform while keeping the pay-per-lead model." The migration will roll out in phases over several quarters. Advertisers will receive an email 14 days ahead of their migration. PPL billing, category targeting and ad placements will remain the same. However, other things are changing. Everything is moving into the Google Ads platform (no old performance reports). Google will pre-populate campaigns with core GBP business details, which can't change without changing GBP. Photos and service areas will also be pre-populated but can be edited. Phone numbers can be customized for lead routing. Manual bidding (e.g., max CPL) is going away. Target CPAs will work differently and must be separated into separate campaigns for separate industries (e.g., plumbing vs. HVAC). Weekly budgets convert to a daily average but monthly billing will be capped (at that daily average × 30.4). No more BBB callouts, but other standard options remain: hours, payment methods, years in business, etc. Early local marketer reactions have been mixed: some view this as a net positive; others are upset about losing manual bidding. There's also concern about introducing greater complexity in the Google Ads/Performance Max environment. But from Google's POV, this certainly makes sense as the company seeks to rationalize its ad infrastructure and fully automate campaigns. ****LSA to P-Max Migration Timetable Chart: Claude ### This post is for subscribers only Become a member to get access to all content Subscribe now

A local brand is an entity in a database — a cluster of relationships Google and AI systems can verify — so as answers get generated rather than ranked, the levers that matter shift away from anything you can do on your own site and toward demand you generate in the real world and corroboration you earn from third parties. ## Cyrus's Four Pillars of Digital Brand Shepard recaps his own framework near the close _(45:49)_. Treated as the episode's spine: Pillar | What it answers | Primary lever ---|---|--- **Distinctiveness** | Can Google tell you apart from a generic query? | Naming, schema, consistent entity references **Relevance** | What are you known _for_? | Web pages, business description, review language, photos all naming the same thing **Reputation** | What do third parties say? | Reviews, cross-referral mentions, category-specific sources **Demand** | How many people seek you specifically? | Branded search, clicks, calls, direction requests, offline-to-digital conversion ## Takeaways * **Chrome, not search, is Google's actual moat.** Shepard's argument is that Google's value doesn't rest on the search engine but on the browser telemetry underneath it — every action by every user, on Maps or any website, whether or not they're on Google. Courts have been asked roughly six times to force Google to share that Chrome data with competitors; none has agreed. _(08:03–09:05)_ * **Ranking first no longer means being recommended.** The pre-AI game was position; today you can hold the number-one spot and still be omitted from an AI answer because nobody else is talking about you. "Ranking may not save you." The consequence is that third-party sites dismissed five years ago now carry more weight than your own optimization work. _(14:36–15:05)_ * **Google doesn't want content anymore — it wants evidence.** Google built a machine that produces content on any topic on demand, so the differentiated assets are the ones the machine can't generate: original research, product pages, tools, calculators, in-depth testing, first-person perspective. Shepard describes the residue of the old model as "zombie sites" carrying hundreds of thousands of informational pages as dead weight. _(21:58–23:00)_ * **NAP consistency has had an unexpected second life.** The decade-old local SEO fundamentals — consistent name, connected social profiles, schema, third-party references that resolve to one entity — matter again specifically because AI systems need the same entity-disambiguation signals traditional local search needed. Practitioners let these lapse on the assumption Google had gotten good enough to infer them. _(09:37–10:31)_ * **Email is a live brand channel for AI visibility.** Shepard cites the iPullRank experiment led by Garrett Sussman: brand mentions embedded in customer emails measurably increased how often those brands surfaced in Gemini and AI Overviews, via personalization. Greg's caveat from Near Media's own two-part interview with Sussman — the lift required opting into Google's personalization. _(17:03–18:00, 18:34)_ * **Amazon's email design is a tell worth inverting.** Amazon deliberately writes vague shipping notices — "your health and wellness product has shipped" — to keep Google from scraping purchase specifics out of Gmail. The tactical inversion for a local business: send the detail Amazon withholds, and let it be scraped. _(18:00–18:27)_ * **Click signals are real but nearly immovable.** Google evaluates roughly thirteen months of click data across thousands of searches for a given business, which is why gimmicks like volunteer driving-direction campaigns produce a short-lived bump at best. Shepard rates them workable only for a business starting from zero visibility, and expects an established business to need continuous repetition for no durable gain. _(34:34–35:36)_ * **The review flywheel has an observable trigger point.** A three-year-old coffee shop sitting at four or five reviews ran a counter QR code, hit roughly 20–25 reviews, and started ranking in Google Maps — after which foot traffic, branded search, and further reviews became self-reinforcing. The threshold is low enough to be reachable deliberately. _(38:25–39:10)_ * **Skip llms.txt and markdown; fix JavaScript instead.** Against a large chunk of current industry advice, Shepard sees only marginal evidence that markdown improves AI readability and doesn't consider llms.txt worth resourcing for most businesses. The real technical failure is rendering: AI systems either can't render JavaScript or won't, and defer to Google to do it. Serve HTML. _(32:40–33:30)_ * **Photos are an entity-extraction channel, not an alt-text exercise.** Google now pulls entities directly out of images, which makes a professional photographer one of Shepard's top recommendations for mid-sized clients — and makes photographing branded product in situ (the Lennox unit with the label visible, the Nike sneakers on the shelf) a way to assert both service and brand association. Photos with actual humans in them outperform generic product shots. _(43:15–45:00)_ * **Cross-business referral pages are the episode's most novel tactic.** Genuine "we recommend these local businesses" pages exchanged among complementary non-competing businesses — the electrician, plumber, and roofer — produce exactly the local third-party mention AI systems reward. Shepard calls it "gold" and rates it well above a directory listing; Greg notes he has not heard anyone else propose it. _(37:34–40:02)_ * **Own your pricing or Reddit will own it.** When a business publishes no pricing information, AI tools source it from third parties and frequently get it wrong. Where executives won't approve full transparency, Shepard's fallback is a published range — "we start at five thousand dollars" plus a call-for-quote — purely so the system has something authoritative to pull. _(27:46–28:20)_ ### Related Reading * Cyrus Shepard — LinkedIn * 17 Content Types to Survive Google's Zero-Click Future — Zyppy Signal * How Google Click Signals Drive SEO Rankings and AI Answers — Zyppy Signal * Ep 251: Google Lied About Click Data — with Cyrus Shepard — prior interview on the leaks and brand proxies * EP 265 - Decoding Local Branding with John Jantsch - Brand as measure of satisfying client needs * Ep 256: The Death of Keywords — Google's Personal Intelligence — the Sussman email research * Google and the Visual Future of Local Search, Part 1 — photo impact on GBP rank * Bing as a source in ChatGPT — Lily Ray ### Practitioner Notes * **Near Media's PI research is direct corroboration of Shepard's demand argument.** Greg's finding that prior brand awareness — usually built by offline advertising — dominates click behavior among personal injury searchers is the same claim Shepard makes from the Google side. Two independent vantage points converging on offline demand as the upstream variable is a stronger publishable position than either alone. Worth foregrounding rather than leaving as an aside at 16:31. * **The photo discussion has an existing Near Media data set behind it.** Shepard's entity-extraction argument at 43:15 maps onto the visual-future-of-local-search work already published. Linking them turns his qualitative recommendation into something with measured GBP rank impact attached. * **TrueSignal fills the gap Shepard names but doesn't solve.** He argues repeatedly that businesses fail to supply an evidence layer; TrueSignal productizes exactly that by publishing verified operational history as citable third-party data. The connection wasn't made on-air because the name escaped Mike mid-episode — worth making explicit in the post. * **The cross-referral tactic is a testable follow-up, not just a segment.** Greg states on-record he hasn't heard it proposed elsewhere. A small pilot — three complementary local businesses, reciprocal recommendation pages, tracked AI citation before and after — would be original research of the exact type Shepard says now earns visibility. ### Concepts * **Brand-as-entity** — a brand is a database entry; your site, your profile, and you personally are separate entities whose relationships you strengthen * **The demand side of brand** — branded search volume as a ratio of site visits; Shepard's "golden egg" * **Good / bad / satisfied clicks** — bad click bounces instantly; good click browses; satisfied click ends the search journey entirely * **Synthetic modeling** — Google using ML to estimate user behavior where real traffic data has disappeared to zero-click ("a synthetic circle jerk," per Mike) * **The map portal view** — local ranking as a deliberately bounded problem; AI appears to be widening those bounds * **Zombie sites** — large informational content libraries that now function as dead weight * **The evidence layer** — what AI systems reward at grounding and citation time, distinct from what's in training data * **Levels of AI visibility** — training data (would it recommend you offline?) vs. grounded retrieval via Google or Bing * **The snowball / flywheel effect** — reviews → Maps ranking → foot traffic → branded search → more reviews * **Leaving breadcrumbs** — varied directory descriptions that still resolve to one consistent entity * **The offline-to-digital bridge** — billboards, events, and QR codes only convert to brand signal if you deliberately build the handoff ### Full Transcript --> on-line Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### Local Websites in a Sea of Slop People increasingly feel that the "open web" is over. The web is now dominated by a small number of powerful "gatekeepers." Beyond this, more paywalls are going up, less traffic is being delivered by Google and AI slop is proliferating. Seemingly imminent AI agents threaten to reduce website visibility even further. Against this backdrop we wanted to explore the future of websites and local marketing with Raj Singh, VP of Product at Mozilla and creator of Solo, an AI-assisted website builder for SMBs. Solo launched in 2023 and has more than 50,000 customers on the platform. Singh argued websites aren't going away, but their role and function is changing. He said that SMBs told him they saw websites as a growth driver, with instilling trust as a secondary benefit. Those functions have arguably flipped as deception and fraud have exploded. Websites increasingly operate as a "source of truth" and communicate that a business is credible and real. More significantly, where and how website content is discovered and consumed is also changing. Zero-click search has been a fact of life for some time in local with GBP, which many use to make decisions without a site visit. AI search and agents may significantly broaden that trend. Singh is readying Solo for the agentic web. It now generates an llms.txt file for every site to enable AI access. Ultimately, he says, it's important to support every platform and channel where your customers are. Authenticity is also critical in a world of AI slop and distrust. AI can and should be judiciously deployed for improved SMB workflows, as well as marketing, provided humans remain firmly in the loop. There's a lot more in the full interview below. ****A Conversation about the Future of Websites ### This post is for subscribers only Become a member to get access to all content Subscribe now

### Gemini for GBP Is a Big Deal Last month Google announced new Gemini features for small businesses. Rolling out this month (except in Europe), it's a significant development. It's not just another lame Gemini add-on to an existing product (e.g., Workspace, Chrome). It could be "game changing" for SMBs. There are two overlapping elements: 1) connecting Gemini and GBP and 2) a new Gemini-powered "Business Notebook." Once Gemini is connected, it simplifies profile management and offers a new "plain-language" way to access performance insights. It also helps SMBs understand, manage and respond to reviews and update their GBPs. Soon you'll be able to publish review responses directly from Gemini. The second piece, Business Notebooks, is like NotebookLM for your business data. It automatically captures data from GBP (including reviews) and your website. Business owners can also upload non-public documents, which help it personalize recommendations. It will also proactively identify GBP action items and deliver customized suggestions about promotional campaigns. Google calls it a "thought partner." Only a minority of SMBs have claimed their GBPs, according to multiple recent surveys. These new tools may grow that number and deepen engagement for those already using Google Business Profiles. Google said access is currently limited to single location businesses but will be coming to agencies and multi-location brands. ****Webinar on New Gemini for SMBs Features ### This post is for subscribers only Become a member to get access to all content Subscribe now

### Facebook Local Search: Take 6 More than 15 years ago, people-powered "Q&A" or "answer engines" attempted to bring real-time first-person knowledge to user questions in contrast to search. Facebook briefly launched one of these, called Facebook Questions. The unifying theme of all these "Q&A" sites was: tap into a human network (or UGC archive) to receive first-person recommendations. These systems held great promise but they never worked quite well enough. Facebook's new AI search is a kind of successor to these early answer engines. It's conversational AI on top of Meta UGC. And while it's not exclusively about local, it's definitely another run at local search, which will be a primary use case if it's used. Facebook has made several unsuccessful attempts to deliver local search and discovery experiences (e.g., here, here, here). And while Meta AI has limited usage (see below) this _has the potential_ to be an effective local search tool. The unfortunately named "AI Mode" uses public posts from across Meta to deliver "real perspectives and experiences rather than a generic list of search results." The proposition is thus the same as these old Q&A sites. But search is no longer the same as this straw man implies. Google went all in on social in 2023 and has increasingly incorporated content from Reddit, YouTube and other UGC sources often to the detriment of quality and usability. ****Meta AI Search in Facebook ### This post is for subscribers only Become a member to get access to all content Subscribe now

### Siri AI Seems to Work The pre-recorded Apple keynote this week at the company's WWDC kickoff was dominated by Siri and Apple Intelligence. There were other announcements, but the main focus was AI and long-promised Siri improvements. Because prior Apple discussions of Siri and AI have largely been vaporware, the company took the unusual step of doing a "live demo" to showcase Siri's AI capabilities – as if to say, "yes, they're real." Siri can now search through iMessages, photos, and Apple calendar to answer questions, take actions inside (selected) apps, pull information and context from your screen, use the camera to search and identify objects, play music, and sync your conversation history across Apple devices with a new Siri app. It will have new "more expressive" voices and better dictation accuracy (let's hope). Siri will also use "world knowledge" (knowledge graph + web index) to answer questions. It cites sources and allows you to click-through. This is Apple Search effectively. There was nothing announced that Google hasn't already done with AI – and there were no significant "agentic" Siri features – but the key is that this is going to be the assistant most directly and deeply integrated with the iPhone, which means it will probably be the most personalized assistant out of the gate. If it actually works, which must still be seen, it will have massive distribution. Below I discuss how Siri AI could affect Google search and other AIs. It will be available later this year on the most recent devices, but not in the EU or China. ****Siri's New Capabilities Source: Apple ### This post is for subscribers only Become a member to get access to all content Subscribe now

The traditional concept of optimizing websites to earn user clicks is being fundamentally disrupted by Google’s native, infrastructure-backed AI capabilities. As detailed in this expert panel breakdown, Google is re-engineering its entire interface to process long, complex natural language queries and handle transactions directly within its own ecosystem. By combining personal data integrations with its massive proprietary database of hyper-local information, Google is building an ecosystem where user needs are predicted and fulfilled without requiring them to visit a third-party website. For digital marketers and enterprise brands, surviving this landscape means adjusting your strategies to focus on building broad web consensus, expanding your presence across multiple digital channels, and adapting to a world where search visibility is driven by algorithmic recommendations rather than traditional keyword matches. ### The Podcast Deets **Segment 1: The Core Infrastructure Shift & The Closed-Loop SERP** (00:00 – 10:13) The panel explores the fundamental architecture updates coming out of Google I/O. Rather than simply upgrading search components, Google is leaning heavily into its custom TPU hardware to drop AI processing costs while introducing interactive, ad-supported formats right inside conversational interfaces. This structural shift lays the groundwork for a zero-click ecosystem designed to answer queries, suggest solutions, and complete customer transactions entirely within Google’s native apps. This poses clear structural challenges for traditional content publishers and e-commerce websites that have long relied on standard outbound organic traffic. **Segment 2: Deep Dive on Predictive Personalization & Spark Agents** ( 10:14 – 20:52) This segment breaks down the launch of the Universal Commerce Protocol (UCP) alongside Google’s personal intelligence data layer. By integrating user history across Gmail, YouTube, photos, and calendar data, Google can deliver deeply personalized recommendations based on past purchases and real-world behavior. The panelists also analyze the strategic role of custom Spark agents, arguing that these new tools serve a secondary purpose: crowdsourcing user interactions to help train and refine Google's underlying AI models. **Segment 3: Ask Maps and Google’s Hyper-Local Defensive Moat** (20:52– 35:31) The conversation shifts to a deep-dive evaluation of Ask Maps—Google’s conversational interface built directly over its local mapping platform. The panel outlines how this update changes search from a simple navigational directory into a powerful discovery engine capable of answering complex, multi-variable geographic requests. The group emphasizes that this massive, human-verified database of physical locations serves as Google's ultimate defensive moat against standalone LLMs. However, the segment also highlights an ongoing challenge for the platform: the persistent difficulty of tracking accurate, real-time product inventory for both small businesses and major retailers. **Key Takeaways** * **Transactions Over Clicks:** Google’s strategic goals have clearly shifted from simply providing helpful information to executing actions natively within its own ecosystem, turning the SERP into a self-contained transactional platform. * **The Power of Proprietary Local Data:** While general text models can easily commoditize standard web content, Google's deep, real-world database of mapping and local business insights remains a core competitive advantage that competing LLMs cannot easily replicate. * **The Shift to Web Consensus:** As personalized AI overviews and conversational interfaces take center stage, relying solely on your own website content is no longer enough; brands must build broad consensus across third-party reviews, directories, and cross-platform channels to ensure AI systems trust and recommend their business. 👇 Watch by topic: 00:00 - Introductions: The Structural Pivot at Google I/O 03:47 - From Answers to Action: Monetizing the New Closed-Loop SERP 10:13 - Universal Commerce: Clawing Back Shopping Traffic from Amazon 14:26 - Personal Intelligence & Highly Personalized Search Buckets 17:57 - Google Spark Agents: Training the Core LLM Models 20:52 - Deep Dive: Ask Maps and the New Local Discovery Engine 27:13 - Google's Defensive Moat: Hyper-Local Data vs. ChatGPT 30:37 - The Local Inventory Gap and the Ghost of Pointy 35:18 - Final Thoughts: Preparing for the Next Era of Digital Marketing ### Related Links Cindy Krum, Founder & CEO of MobileMoxie - MobileMoxie BlogCindy Krum is the author of Mobile Marketing: Finding Your Customers No Matter Where They Are. She is the founder of MobileMoxie in Denver, CO.MobileMoxie BlogFounderGreg Gifford - SearchLab DigitalChief Strategy Officergreg@searchlabdigital.comGreg’s presentations on SlideShareLinkedIn profileX profile Need a dynamic, engaging speaker for your event? Book Greg to Speak Greg Gifford is SearchLab’s Chief Strategy Officer. He’s one of the most popular speakers in the SEO industry and presents all over the world on topics related to Local SEO. There’s hardly anything at SearchLab that […]SearchLab DigitalGreg GiffordKrystal Taing: VP of Solutions & Local SEO Expert | UberallKrystal Taing is a Google Business Profile Platinum Expert and VP of Solutions at Uberall with over a decade of SEO experience.Business Review ManagementConnect on LinkedIn Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

### Google's Ask Maps Listicles Even as Google is starting to crack down on listicles designed to manipulate AI, the company is generating its own lists for local discovery. It's creating home-grown listicles to answer questions in Ask Maps and probably soon AI Mode. Craig Burton explores this in a recent blog post. List content comes from a range of sources: reviews, blog posts, photos and video, editorial listicles and others. Burton argues Google's increasing use of lists requires a new way of thinking about local visibility. He calls it "evidence optimization" and contrasts that with GBP optimization, where marketers largely focused on maximizing their GBPs. Now you've got to optimize for a broader range of sources that AI is looking at, something many people have been advocating. In some ways this is another version of citation optimization, only more challenging. It's also consistent with the recommendation to diversify reviews beyond Google. Burton identifies several list categories, some of which are dynamic and some of which are curated: Top List, Local Gems, Trending, Publisher lists, Brand/editorial lists and creator lists. However the situation is dynamic. Many lists appear to be highly contextual (and eventually personalized). "Best X" searches have always generated lists, less formally. And Google has played with curated lists in the past. But this is something more sweeping. In his post, Burton offers a range of tactical advice for local discovery (list) optimization in Google AI. ### This post is for subscribers only Become a member to get access to all content Subscribe now

In this Duda webinar, Darren Shaw and Mike Blumenthal explore the paradigm shift occurring in local search as Google increasingly incorporates generative AI into Google Business Profiles and Google Maps. Through new features like "Know Before You Go" and the conversational "Ask Maps," local discovery is transitioning from a deterministic ranking model to a highly personalized, context-aware ecosystem. Blumenthal demonstrates how to leverage Ask Maps for deep competitive analysis to pinpoint market weaknesses and operational gaps. The speakers emphasize that while traditional SEO remains foundational "table stakes," long-term visibility in the AI era requires businesses to cultivate a deeply revered brand through comprehensive website data, widespread consumer sentiment, community engagement, and niche specialization. ### The Podcast Deets **. The Integration of Generative AI in Local Graph Signals [** 06:45**]** In this section, the speakers dissect the early layers of AI hitting Google Business Profiles (GBP), including automated business summaries, "Know Before You Go" cached metrics, and conversational Q&A modifications. Blumenthal highlights a critical shift: Google's LLMs look first and foremost at the "local graph" (the entity data within Google Maps, business profiles, and connected websites) before looking out to the wider web. They explain that AI has completely flipped the script on older local SEO advice; details hidden within service descriptions and even text crafted inside review responses are now being actively parsed to determine situational relevance for voice and AI chat prompts. **2. Deep Dive and Live Demonstration of "Ask Maps" [** 15:50**]** Blumenthal screenshares a live demo of "Ask Maps," demonstrating how conversational discovery handles multi-variable, long-tail queries that traditional map searches fail to process. Using complex examples—such as finding a New York City brunch spot equidistant via subway lines for three friends in different neighborhoods, or matching broad geographic solar installations—he showcases how the feature pulls dynamically from localized entity clusters. The segment contrasts traditional deterministic search results with the fluid, non-deterministic nature of AI, highlighting current UI complexities, verbosity issues, and the lack of native share links or tracking metrics. **3. Market Research Tactics and Future Brand Strategy [** 38:23**]** The final core segment pivots from technical mechanics into actionable business strategy, detailing how owners can treat Ask Maps as a powerful, free market research tool. By prompting the AI to compare a business's weaknesses against its primary competitors based on sentiment analysis across reviews, owners can reveal hidden operational or content gaps. To win visibility six to nine months down the line, Blumenthal urges businesses to look beyond individual keyword chasing and aim to become the most "revered" brand in their local market. This involves securing professional accreditations, building relationships with local media for PR, tracking organic sentiment across platforms like Reddit, and narrowing focus to dominate a specific industry niche. **Key Takeaways** * **Traditional SEO is Only Table Stakes:** Tweak-based optimization and standard local ranking factors are still mandatory to ensure data gets ingested by Retrieval-Augmented Generation (RAG) loops, but they are no longer sufficient to guarantee dominance. * **The Death of Uniform Rankings:** Local AI search is fundamentally non-deterministic and highly personalized; results shift dynamically based on historical user data, localized user proximity, explicit conversational variables, and default AI memory tracking. * **GBP Descriptions and Services Matter Anew:** While keyword stuffing a business description historically had zero impact on traditional map pack algorithms, comprehensive descriptions and thoroughly completed service fields directly influence whether an LLM flags a business as relevant. * **Leverage Conversational Competitive Audits:** Businesses can bypass costly research tools by directly prompting Ask Maps to compare specific competitor names, asking it to explicitly map out customer pain points, structural weaknesses, and localized operational flaws extracted from review data. * **Revered vs. Merely Known:** Future-proofing against AI requires an active community footprint—focusing heavily on media relations, localized corporate sponsorships, helpful non-promotional interactions on platforms like Reddit, and intentional niche specialization (e.g., dialing in on "engagement rings" over general jewelry). 👇 Watch by topic:00:00 - Local SEO/AEO Success in the AI Era 01:56 - Mike's background: family business, internet design, discovering local SEO 03:29 - Google Maps consolidation and the end of yellow pages 04:42 - First blog post, early local SEO community (Bill Slawski, Matt McGee) 06:00 - Collaboration in local SEO vs. the threat of large corporate entities 06:45 - AI features in Google local search: AI-generated business summaries 07:56 - No legal accountability for AI-generated content about businesses 08:21 - Intro to Ask Maps as the main webinar topic 08:52 - "Know Before You Go" feature explained 10:19 - GBP description now matters for AI visibility (not just rankings) 11:49 - Review responses as a relevance signal for AI results 13:51 - Mike's view: reviews, attention, and clever responses as ranking signals 15:50 - Ask Maps demo begins; Google Maps interface overview 18:54 - Traditional maps search vs. Ask Maps: deterministic vs. non-deterministic results 21:55 - Ask Maps example: complex brunch query with subway equidistance 23:57 - Ask Maps limitations: no inventory data, no event data, no agentic sharing 25:56 - Ask Maps as market research: competitive strengths and weaknesses via reviews 33:08 - Current drawbacks: Maps UX complexity, verbosity, no tracking 35:32 - Hallucination risk is low; Ask Maps is grounded in the local graph 38:23 - Strategy: local SEO as table stakes; becoming the most revered local brand 43:09 - Word of mouth, reviews everywhere, professional accreditations, PR, media relations 48:34 - Community involvement, Reddit, social media sentiment as new signals 53:14 - Niching down: be revered for one specific thing (Barbara Oliver engagement rings) 55:00 - Ask Maps is hidden; most clients don't know it exists yet 56:01 - Multi-location businesses and LLM visibility: optimize service descriptions, website as primary database 58:34 - Service area businesses: AI may level the playing field vs. brick-and-mortar 01:01:26 - Where Ask Maps pulls data: top vertical directories, complete listings 01:03:10 - Closing plugs: Whitespark local ranking grids, Near Media user research Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike

In this episode of Near Memo, Mike Blumenthal and Greg Sterling explore how Google’s rapidly evolving AI ecosystem is transforming search, advertising, and personalization. They begin with a major DOJ fraud case involving 15,000 fake Google Business Profiles and discuss whether Google’s platform design enables abuse at scale. The conversation then shifts to inferred demographics, with Mike sharing experiments showing how Gemini and ChatGPT can accurately infer income, lifestyle, politics, and buying habits without explicit opt-ins. Finally, they analyze the biggest announcements from Google I/O and Google Marketing Live, including AI Mode, multimodal search, AI-native ads, shopping integrations, and Google’s broader strategy to deepen user lock-in and dominate the AI era. ### The Podcast Deets **1. DOJ Fraud Case & Google Business Profile Abuse** (01:32 – 10:05) Mike and Greg unpack a sweeping DOJ complaint against Premium Home Service and its CEO over alleged fake listing and fake review schemes spanning thousands of fraudulent Google Business Profiles. The discussion examines how scammers exploit weaknesses in Google’s verification systems, whether Google’s platform design effectively enables this behavior, and why Section 230 protections may prevent meaningful accountability. The hosts also debate whether Google Search and Maps could someday face “defective product” legal theories due to systemic fraud exposure. **2. AI Personalization & What Google Already Knows About You (**10:15 – 16:26) Mike shares an experiment asking Gemini and ChatGPT to infer his demographic, political, professional, and socioeconomic profile using search history, memory, and publicly available information. The results were surprisingly accurate, including estimates of income, lifestyle preferences, and social class. Greg and Mike discuss how inferred personalization works even without explicit “Personal Intelligence” opt-ins and why AI-powered search experiences are becoming increasingly individualized and behaviorally targeted **3. Google I/O, AI Mode & The Future of Search Advertising** (16:26 – 34:09) The hosts break down the major AI announcements from Google I/O and Google Marketing Live, including AI Mode, Gemini Spark, multimodal search, agentic experiences, and AI-native ad formats. They analyze Google’s “good enough AI” strategy, its advantages from owning the full AI stack, and the company’s growing ability to personalize both organic results and advertising. The discussion also explores shopping integrations, AI commerce, Google Maps implications, and whether antitrust efforts have failed to meaningfully constrain Google’s expanding ecosystem power. ### Key Takeaways * Google already infers enormous amounts about users without explicit opt-ins. * AI personalization is becoming central to both search results and advertising. * Google’s AI ecosystem strategy increasingly aims to keep users entirely inside Google experiences. * AI-native advertising formats may unlock full monetization of AI search. * Google’s ownership of infrastructure, distribution, and data gives it major AI advantages. * The DOJ fraud case highlights ongoing weaknesses in Google Business Profile verification systems. * Google Maps and local search are likely to become deeply AI-driven and personalized. * Antitrust rulings so far appear unlikely to meaningfully slow Google’s AI expansion. * AI Mode represents a major interface shift toward conversational and multimodal search. * Agentic AI and transactional search experiences are rapidly approaching mainstream adoption. ### 👇 Watch by topic: • 0:00 - Introduction • 1:34 - The DOJ Fraud Case Against Premium Home Service • 10:15 - Inferred Personalization: What Google Knows About You • 17:00 - Google I/O and the AI-Powered Search Box • 25:37 - AI-Native Ad Formats and Future Monetization ### Related Links Department of Justice Files Complaint Against B.E.S.T. GDR, LLC, Doing Business As Premium Home Service, and its CEO Yosef Bernath for Violations of the FTC Act and Related LawsThe Department of Justice, acting on a referral from the Federal Trade Commission (FTC), along with the Attorney General of Illinois and the U.S. Attorney for the Northern District of Illinois, announced today the filing of a civil complaint to stop an Illinois company and its owner from deceiving consumers in connection with the sale of home repair and improvement services inDepartment of Justice Logo Office Of Public Affairs“Premium” Home Service – The questionable businesses of Yosef Shmuel Bernath"Premium" Home ServiceEP 255 - How Google’s Personal Intelligence is Quietly Revolutionizing (Everyone’s) Search ResultsIn this episode we are joined by Garrett Sussman (iPullRank) to discuss his provocative 12-month study on AI personalization. We dive deep into how Google’s “AI Mode” uses your Gmail, Photos, and Calendar to tailor results—and why “unopened emails” might be influencing what you see next.Near MediaNear MediaEP 256 P2 - The Death of Keywords: How Google’s Personal Intelligence is defining the future of Local SearchIn Part 2, Garrett Sussman joins Near Media to explore how Google’s AI personalization could transform local search, reviews, SEO, attribution, and marketing strategy — and what businesses should start doing now before AI-driven discovery becomes dominant.Near MediaNear MediaGoogle I/O 2026: All the news and biggest announcements of the year for Gemini AI, Android XR smart glasses and moreDon’t miss all of the updates coming to Google Gemini, Search, Workplace and almost every other application the company makes.YahooValentina PalladinoGoogle Marketing Live 2026: The Biggest Shift in YearsGoogle Marketing Live 2026 announced major updates to search, ads, creative and measurement. Here’s what’s changing and what it means for your business.Blayzer Digitalblayzerdigital Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### Near Memo Transcript: --> on-line

### Google Launches AI Powered Search Box AI Mode now has more than 1 billion users, according to Liz Reid today at Google I/O. Many SEOs had been predicting that AI Mode would become the new SERP. Google isn't doing that but it is much more deeply integrating AI into search with a new "intelligent search box," tightening the AIO and AI Mode connection and introducing new "search agents." The new search box, which Reid described as the biggest upgrade in 25 years, is supposed to go live today. It's not operative for me yet. One gets the sense that it will lead people more often into AI Mode. The new search box also offers AI powered search suggestions. Google described it as a "seamless experience," likely intended to overcome some of the fragmentation that Google introduced with all its disparate AI experiences. Reid also discussed search agents, starting with "information agents." These are the AI equivalent of Google Alerts, with more capabilities (e.g., apartment hunting, financial research). Eventually, more types of agents will be deployed. Google mentions local here: "We’re also expanding agentic booking capabilities in Search to a wide range of new tasks, including local experiences and services _."_ Google will find pricing and availability information (including calling the business) and then book or offer booking options. These agentic capabilities go live this summer for paid Gemini users. Many or all of these tools will also be available through a new Gemini agentic assistant called Spark. Source: Google I/O **Our take** * It's not yet clear how much this will change the search experience or user behavior. It's safe to say it will, however. * Until we can see these tools in action it's premature to prognosticate. Still, this appears to be a significant shift in search. * To the extent Google drives more users into AI Mode, however, it's risking ad clicks at the moment. ### Siri Search & Gemini Spark OpenAI is reportedly getting ready to sue Apple over its disappointing ChatGPT partnership. At the same time Apple is (finally) preparing to unveil an AI-upgraded Siri at its coming developer conference in June. There will apparently be a new stand-alone Siri app, which will emphasize privacy, but perform like ChatGPT or Claude and be built on Gemini. A more functional and broadly useful Siri could peel away search volumes from Google if it works. We'll have to see. But today at I/O, Google announced yet another AI brand, as mentioned, in the form of Gemini Spark. Spark is agentic assistant built on Gemini 3.5. Spark lives within Gemini – there's no Spark app – and will carry out actions and tasks on behalf of users, right now mostly around schedule and information management. To get the full value of Spark you'll need to share all your data and Google apps (Google docs, calendar, etc.) It appears users can also create their own "skills" and that Spark will work with third party tools, presumably through MCP/ connectors. However, Spark will only be available to those paying for Gemini Ultra. Normally $200 per month, Google has introduced a new $100 monthly plan. Spark appears useful, but primarily to those willing to opt in and share all their personal data with Gemini. Spark will also work on Gemini for macOS and the iPhone. Google demonstrated a number of consumer-facing agentic tools, in Search, Gemini and Shopping. They probably all share the same infrastructure and are just different ways in. But there's a lot of brand clutter going on. **Google's New Personal Agentic Assistant** Source: Google I/O **Our take** * No doubt many of these "agentic" features will be useful. It's hard to know how user friendly they are or how widely embraced they'll be. * A decade ago I would've been celebrating all these tools and capabilities and writing some fawning article about them. * Now they feel manipulative. It's hard not to see all this as just another Google data grab and way to keep users from going anywhere else. ### Gemini Only AI to Gain Trust YoY AI is facing a trust crisis, although that hasn't stopped usage. Unlike people in other countries, Americans strongly distrust AI. More than 60% of survey respondents say they have little or no trust in AI. The main concerns are misinformation, job displacement, data security and privacy questions, as well as overall negative societal impacts. A new AI Trust Report from Morning Consult captures all of those issues in some detail. But the most interesting finding for our purposes is the growth of trust in Gemini vs. most other AI brands. According to the survey, seven of ten leading AI companies lost trust over the past 12 months. The biggest losers were xAI, Perplexity and Meta. However, OpenAI was in there as well. The three companies to gain consumer trust were Anthropic (+1.4) and Apple Intelligence (+1.2), while Gemini gained a whopping 6 points. Similarweb has been tracking Gemini's market share gains, which are partly about quality improvements but much more about Google's massive distribution and ability to drive usage through its various channels: search, Chrome, Android, Workspace. In May Similarweb reported that ChatGPT had declined to 53.7% traffic share, while Gemini had grown to 26.7%. Significantly Claude is now number three with 8%. (There are other data somewhat less upbeat for Gemini.) Another important story, documented by Morning Consult, is that people are not simply choosing one AI tool over another; they're making choices about when to use each. Gemini is seen mostly as an extension of search and used primarily for "utility tasks," whereas ChatGPT and Claude are being used for a broader range of things. There's also a very large overlap between ChatGPT and Gemini usage. **AI Platform Trust** Source: Morning Consult **Our take:** * The key implication from these trust survey findings is that they may predict future behavior, which means further Gemini gains. * Copilot is one of the more trusted AI brands, but that's not turning into usage. Microsoft's "halo" isn't helping. However Google's is for Gemini. * OpenAI is in trouble. The hits their brand has taken since last year, together with this trust information, suggest further slowing. ### Plan for 'Google Zero' There's no way an objective observer could have come away from today's Google I/O keynote without a strong sense that Google is using all its technology, tools and tricks to increase user lock-in. The changes in Search and the SERP either announced or hinted at and the various agentic tools and features seem calculated to keep users on Google properties and to further reinforce Google dependence. In particular, Google's Robby Stein spoke about "generative UIs" in Search. These are dynamic and unique SERPs designed to answer specific questions or to provide graphical mini-apps that respond to a unique user need. As such they're extremely personalized. They may appear exclusively in AI Mode, but that's not clear because they didn't show actual live screens, just mockups. More generally these dynamic SERPs and other changes may further marginalize organic links. Therefore all marketers should plan and act as if they had to market without Google going forward. That's not to say you don't do SEO or advertise in appropriate contexts; it means you can't rely on Google. You have to work toward driving brand queries. The Condé Nast CEO Roger Lynch recently stated in an interview that they're actively planning for a future where search traffic is zero. For them subscriptions have become a primary alternative strategy. Google Zero is what most publishers will experience in a future where all third party content is merely an input or data for the company's AI systems and answers. **Dynamic UIs in Search** Source: Google I/O **Our take:** * Google has argued that declining publisher traffic is really just low value clicks. * It's difficult to see how all these new Google-centric AI experiences pass muster under the DMA's anti-self-prefercing rules. * Marketers must still play the Google game, but should diversify and build brands (long-term proposition). That will help in both search and AI. ### Recent Podcasts & Analysis * **Near Memo ep. 256** - Death of Keywords: Google’s Personal Intelligence is Defining the Future of Local Search – Part 2 (**Garrett Sussman**) * **Near Memo ep. 255** - How Google’s Personal Intelligence Is Quietly Revolutionizing Everyone’s Search Results – Part 1 (**Garrett Sussman**) * **5-Star Fraud: The FTC Targets Law Firms & Property Managers for Review Abuse **by Mike Blumenthal **LOCAL SEARCH & SEO** * Google releases **GBP optimization** guide for restaurants. * Checklist for troubleshooting Google **review disappearances**. * Why listing **24/7 business hours** is a suspension risk. * Beware the vindictive "**update for customers** " slam. * Yelp showing up frequently in Google **AIOs, AI Mode, AI Packs**. * Google testing an **offer callouts** in business profiles. * How to **add text to GBP photos** to improve AI understanding. **GENERAL SEARCH & SEO** * **ChatGPT** has a "hidden cached index" of webpages. * Risky strategies for **SEO and AI search** visibility. * **YouTube** may be a quick way into AI Overviews. * Study: **custom design** and images boosted SEO visibility. * Google now offers AI assistant/**LLM tracking** in GA. **ARTIFICIAL INTELLIGENCE** * **OpenAI finance** tool wants access to your bank accounts. * Google: **AI search optimization** is still SEO, critics disagree. * Ahrefs study on no **schema AI impact** and criticism. * Google extends **spam policies** to AIOs and AI Mode. **SOCIAL, ADS & COMMERCE** * **Meta** : search and social don't compete, but social's better. * **Reddit** less valuable for targeting than for brand insights. * OpenAI turning product feeds into ads on **ChatGPT**. * **LinkedIn:** no more AI slop; also: use our AI writing tools. **LAW, POLICY & PRIVACY** * US sues home services fraudster; scammed $79M with **fake GBPs**. * Musk loses court battle against nemesis Altman and **OpenAI**. * CA sues **Meta** over scam ads under false advertising law.

### Google Launches AI Powered Search Box AI Mode now has more than 1 billion users, according to Liz Reid today at Google I/O. Many SEOs had been predicting that AI Mode would become the new SERP. Google isn't doing that but it is much more deeply integrating AI into search with a new "intelligent search box," tightening the AIO and AI Mode connection and introducing new "search agents." The new search box, which Reid described as the biggest upgrade in 25 years, is supposed to go live today. It's not operative for me yet. One gets the sense that it will lead people more often into AI Mode. The new search box also offers AI powered search suggestions. Google described it as a "seamless experience," likely intended to overcome some of the fragmentation that Google introduced with all its disparate AI experiences. Reid also discussed search agents, starting with "information agents." These are the AI equivalent of Google Alerts, with more capabilities (e.g., apartment hunting, financial research). Eventually, more types of agents will be deployed. Google specifically mentions local here: "We’re also expanding agentic booking capabilities in Search to a wide range of new tasks, including local experiences and services _."_ Google will find pricing and availability information (including calling the business) and then book or offer booking options. These agentic capabilities go live this summer for paid Gemini users. Many or all of these tools will also be available through a new Gemini agentic assistant called Spark. Source: Google I/O ### This post is for subscribers only Become a member to get access to all content Subscribe now

In this second installment of Near Media’s conversation with Garrett Sussman, the discussion moves from theory into practical implications as the group explores how Google’s “personal intelligence” systems may reshape local search, SEO, customer discovery, reviews, attribution, and reputation management. Garrett explains how behavioral data, Gmail, direct mail, calendars, reviews, and customer affinities could influence AI-driven recommendations, while Greg Sterling and Mike Blumenthal examine the broader implications for marketers, privacy, and the future of SEO measurement. The episode argues that traditional keyword-centric SEO is giving way to a far more holistic model centered around customer understanding, omnichannel presence, and AI visibility. ### The Podcast Deets **1. How AI Personalization Could Transform Local Search (00:00–18:30)** The episode opens by connecting the themes from Part 1 — personalization, Gmail influence, and AI Mode — to local search and real-world commerce. Garrett describes a striking example where Google surfaced a shoe recommendation based on a USPS mail digest email containing a scanned postcard from a local running store. The group explores how AI systems could combine Gmail, Maps, Calendar, reviews, behavioral patterns, and location data to create highly individualized recommendations for restaurants, lawyers, retailers, and local services. The conversation also explores how Google may increasingly shift from explicit location intent (“near me”) to implicit behavioral understanding. **2. Why SEO Is Becoming More Holistic & Less Deterministic (18:30–36:00)** The discussion then turns to the future of SEO and measurement. Garrett argues that AI search is forcing marketers to move beyond keywords toward broader audience understanding and omnichannel visibility. The panel debates whether GEO/AEO differs meaningfully from traditional SEO and concludes that while SEO fundamentals remain important, marketers must now think far more holistically. The episode also examines the collapse of deterministic ranking tools and the growing importance of citations, brand mentions, reviews, PR, YouTube, and social influence in AI-driven discovery systems. **3. What Marketers Should Do Right Now (36:00–48:42)** In the final segment, Garrett outlines practical next steps for businesses preparing for AI-driven search. He emphasizes direct customer research, usability testing, persona modeling, and building “search twins” that simulate customer behavior across Gmail, social media, newsletters, and search. The group also discusses why businesses should obsess over consistency, structured business information, visual storytelling, and reputation management. The episode concludes on an optimistic note: despite the uncertainty and ethical concerns surrounding AI personalization, local SEO may actually become more strategic, creative, and customer-focused than ever before. ### Key Takeaways * Google’s AI systems may increasingly personalize local search results using Gmail, Maps, Calendar, reviews, browsing behavior, and even direct mail signals. * Traditional keyword-centric SEO is becoming less important than holistic customer understanding and omnichannel visibility. * Reviews may gain even more influence in AI-driven search experiences because LLMs can consume and synthesize large amounts of review sentiment. * Businesses should begin building “persona search twins” to simulate customer behavior and test AI search outcomes. * Measurement is becoming probabilistic rather than deterministic — marketers must think in terms of trends, visibility, citations, and influence rather than exact rankings. * Breaking down silos between SEO, PR, email, social, and branding is becoming increasingly critical in AI search environments. * Customer interviews and usability testing may become one of the most valuable competitive advantages in local marketing. * Visual storytelling, signage, images, menus, partnerships, and real-world brand presence may all become stronger AI signals over time. * Reputation management is becoming harder because AI systems may surface historical narratives and context in unpredictable ways. * Despite privacy and regulatory concerns, AI-powered personalization is likely to become a major force in local discovery over the next few years. * **👇 Watch by topic:** 00:00 Intro & recap of Google’s “personal intelligence” 02:20 Direct mail influencing AI recommendations 05:00 Local search implications of AI personalization 08:00 CRM, email marketing & behavioral targeting 10:40 Building “persona search twins” for AI testing 11:10 Google Business Profiles, reviews & AI discovery 15:00 Location, behavior & implicit personalization 18:30 AI recommendations, bias & consumer discovery 20:20 Why classic SEO thinking is breaking down 23:10 Is SEO becoming GEO/AEO? 26:00 Privacy, antitrust & AI personalization risks 31:00 Why keyword research is changing forever 34:00 Measuring AI visibility & citations 36:00 Breaking down marketing silos 38:20 Customer research & usability testing 41:00 The three things marketers should do now 45:00 Reputation management in the AI era 48:00 Final thoughts: why local SEO is entering a new era Related Links EP 255 - How Google’s Personal Intelligence is Quietly Revolutionizing (Everyone’s) Search ResultsIn this episode we are joined by Garrett Sussman (iPullRank) to discuss his provocative 12-month study on AI personalization. We dive deep into how Google’s “AI Mode” uses your Gmail, Photos, and Calendar to tailor results—and why “unopened emails” might be influencing what you see next.Near MediaNear MediaGoogle's Context Packing and Hyperpersonalization Explained | Garrett Sussman posted on the topic | LinkedInSEOs should start obsessing over context packing and hyperpersonalization. AI assistants are context machines. Google says consent is required, but we’re going from opt-in to opt-out before people are ready. That’s going to shock the system. The optimist in me says people will love it. For the uninitiated, Google’s Personal Intelligence doc describes context packing as finding the right pieces of your personal data and putting them into the model’s working memory. Doc in the comments. Gmail, Photos, Search history, YouTube, saved places, past conversations, receipts, confirmations, and the random paper trail of your life can all shape the AI output. We already know personalization from local search. I’m talking about this today with Mike Blumenthal and Greg Sterling on the Near Media podcast. Google can find relevant content in a massive corpus of documents. Your digital life history can probably predict intent most of the time, like an optometrist spotting cholesterol levels. Yeah, that’s real. Personalization has to thread the needle between helpful and creepy. Google calls out the limitations: Overpersonalization: One signal can become the whole personality of the answer. Mistaken preferences: Buying something for your kid, spouse, friend, or brother can make the model think it’s your thing. Incomplete information: The model may act like it has the whole story from a few scraps in the kitchen junk drawer. Timeline confusion: AI already struggles with time. Personal history gives it more ways to mess up past, present, and future. Relationship confusion: Names, emails, photos, and messages can still lead to the wrong human read. Missing life changes: Your life can change faster than the model’s understanding of you. ~Ferris Bueller Bad assumptions: A receipt can become proof that you only want Aviator Nation hoodies and Starbucks forever. Ignored corrections: You can correct the model, and it may still wander back like my daughter insisting pink and golden are rainbow colors. Speed vs. depth: More personal answers require more work, so speed becomes a tradeoff. Multimodal risk: Once photos, videos, and visual summaries enter the mix, the risk becomes wrong context with a screenshot attached. And this isn’t just Google. ChatGPT is now surfacing Gmail-connected personalization too. Brand new FAQ in the comments. Brands have to earn their way into the context layer before the search happens. Andrea Volpini wrote about this yesterday. Emails. Newsletters. Reviews. Communities. Videos. Receipts. Support interactions. Third-party mentions. Saved lists. Past purchases. This is the year of hyperpersonalization. Google I/O is in a couple of weeks. I’m betting we’ll see more Personal Intelligence news. The psych of this gets me excited. It’s messy now. It’ll get better. The ugly part is that we’re still at the whims of corporations we can’t trust to have our best interests at heart. Y’all notice that it references receipts? What??LinkedInGarrett Sussman Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### Near Memo Transcript: --> on-line

### ChatGPT Expand Ads, Offers Self-Service Driven by necessity, ChatGPT is rapidly expanding its ad platform. Earlier this week, OpenAI announced it was making ads more widely available. There are now two ways to buy, through partners (agencies, technology partners) and using a new self-service Ads Manager (US only). OpenAI is explicitly appealing to SMBs as well as brands. "Businesses can register as advertisers, add payment information, set budgets, bids and pacing, upload ads, launch and manage campaigns, and view performance in the portal." The company has also enabled improved measurement and CPC billing, but will continue to offer CPM, adding "over time [we] expect to support more ways for advertisers to bid and optimize for the outcomes they care about most." And a new AI ads report from Similarweb offers a first look at ChatGPT ad performance and pricing. The report estimates there are about 1K active ChatGPT advertisers. Significantly (vs. Google AI) only one ad appears per conversation "turn" (page/result). This is a major differentiator; how long it will last is another matter. Ads appear on the first answer ("turn 1") 44% of the time and on a later "turn" 56% of the time. The report shows a 0.50% median CTR at the bottom and 5.4% at the top. According to third party benchmarks ChatGPT ads outperform Google Display Ads but have lower CTRs generally than paid search ads. The report assumes a $60 CPM and $12 CPC. ****ChatGPT Ads: One Unit Per Page ### This post is for subscribers only Become a member to get access to all content Subscribe now

The search landscape is shifting from keyword matching to "Personalized Intelligence," where Google leverages explicit consent to access a user's Gmail, Calendar, and Photos. Research conducted by Garrett Sussman and Profound reveals that Google’s AI doesn't just find information; it infers a user's values and disposable income to steer them toward specific brand outcomes. ### The Podcast Deets **The Opt-In Ecosystem (** 03:11 - 10:59**):** A look at the "Personalized Intelligence" toggle that gives Google permission to scan your private documents. **The Persona Impact (** 12:22 - 21:07**):** How LLMs change their tone and recommendations based on whether a user makes $45k or $150k. **The Email Seeding Experiment (** 21:35 - 30:02**):** A breakdown of how sending specific emails can move a brand from the "bottom 10" to a top AI recommendation. ### Key Takeaways • Google’s Personal Intelligence is an opt-in feature that integrates Gmail, Photos, and YouTube history into AI search. • AI search platforms (like Gemini and ChatGPT) change product recommendations based on explicit and implicit persona data, such as salary. • Gmail content is a significantly stronger signal for AI personalization than Google Photos. • Even unopened emails can influence the recommendations provided in Google's AI Mode. • Google’s AI is moving toward an "agentic" model, acting as a partner that "solves" problems before a user even finishes their query. * **👇 Watch by topic:** 00:00 – Introductions and the Personalization Shift 03:11 – Defining Google’s "Personalized Intelligence" 12:22 – The 12-Month Profound Study: How Persona Impacts Results 21:35 – Seeding Experiments: How Gmail Influences AI Recommendations 30:02 – The Privacy Paradox: Family Names and "Creepy" Data Related Links Implications of AI Mode and Personal Context - 2025 Google I/O Hot TakesWhat did we learn about AI Mode and Personal Context at Google I/O 2025 and what does it mean for the future of SEO and Content Strategy?iPullRankGarrett SussmanSEOs should start obsessing over context packing and hyperpersonalization. AI assistants are context machines. Google says consent is required, but we’re going from opt-in to opt-out before people… | Garrett SussmanSEOs should start obsessing over context packing and hyperpersonalization. AI assistants are context machines. Google says consent is required, but we’re going from opt-in to opt-out before people are ready. That’s going to shock the system. The optimist in me says people will love it. For the uninitiated, Google’s Personal Intelligence doc describes context packing as finding the right pieces of your personal data and putting them into the model’s working memory. Doc in the comments. Gmail, Photos, Search history, YouTube, saved places, past conversations, receipts, confirmations, and the random paper trail of your life can all shape the AI output. We already know personalization from local search. I’m talking about this today with Mike Blumenthal and Greg Sterling on the Near Media podcast. Google can find relevant content in a massive corpus of documents. Your digital life history can probably predict intent most of the time, like an optometrist spotting cholesterol levels. Yeah, that’s real. Personalization has to thread the needle between helpful and creepy. Google calls out the limitations: Overpersonalization: One signal can become the whole personality of the answer. Mistaken preferences: Buying something for your kid, spouse, friend, or brother can make the model think it’s your thing. Incomplete information: The model may act like it has the whole story from a few scraps in the kitchen junk drawer. Timeline confusion: AI already struggles with time. Personal history gives it more ways to mess up past, present, and future. Relationship confusion: Names, emails, photos, and messages can still lead to the wrong human read. Missing life changes: Your life can change faster than the model’s understanding of you. ~Ferris Bueller Bad assumptions: A receipt can become proof that you only want Aviator Nation hoodies and Starbucks forever. Ignored corrections: You can correct the model, and it may still wander back like my daughter insisting pink and golden are rainbow colors. Speed vs. depth: More personal answers require more work, so speed becomes a tradeoff. Multimodal risk: Once photos, videos, and visual summaries enter the mix, the risk becomes wrong context with a screenshot attached. And this isn’t just Google. ChatGPT is now surfacing Gmail-connected personalization too. Brand new FAQ in the comments. Brands have to earn their way into the context layer before the search happens. Andrea Volpini wrote about this yesterday. Emails. Newsletters. Reviews. Communities. Videos. Receipts. Support interactions. Third-party mentions. Saved lists. Past purchases. This is the year of hyperpersonalization. Google I/O is in a couple of weeks. I’m betting we’ll see more Personal Intelligence news. The psych of this gets me excited. It’s messy now. It’ll get better. The ugly part is that we’re still at the whims of corporations we can’t trust to have our best interests at heart. Y’all notice that it references receipts? What??LinkedInGarrett Sussman Building Personal Intelligence: A step towards truly personal AI Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **Full Transcript -- > **on-line

In December 2025, the FTC issued warning letters to 10 companies determined to have likely violated the Trade Regulation Rule on the Use of Consumer Reviews and Testimonials, which became fully effective on October 21, 2024. After FOIA requests these letters, detailing the specific firms and their alleged offenses, were recently published on the FTC website. The "on alert" group includes three personal injury law firms, one CPA, and six property management firms. ### Compliance Requirements Within 5 days of their receipt of these letters the companies needed to provide proof of the following: **1. Immediate Mandatory Compliance** Each business was required to respond via email to the assigned FTC staff member within 5 business days of receiving the letter. Their response was required to include: * **Point of Contact** : Identification of the specific person or entity in charge of the company's review policies and compliance. * **Reciept Confirmation** : Proof that the warning letter was been delivered to that person or centralized entity. * **Remediation Plan** A detailed description of the specific actions the business will take to ensure future compliance with the Rule. **2. Remedial Action** The FTC "advised" these businesses to immediately cease all prohibited conduct and take corrective steps, such as: * **Removal** : Deleting problematic reviews and testimonials from websites under their direct control. * **Third Party Outreach** : Making a "best effort" to have non-compliant reviews removed from third-party platforms like Google or Yelp. * **Process Revision** : Ending "gatekeeping" or sentiment-based incentive programs (e.g., gift cards or rent discounts for 5-star ratings). **3. Risk of Formal Legal Action** If no response was received by the FTC or if the FTC gathers further reports of non-compliance, the FTC signaled its intent to file of a federal lawsuit. Because these businesses are now on formal notice, any continued violations carry significant legal risk. Under the Consumer Review Rule, the FTC is authorized to seek substantial monetary damages with penalties of up to**$53,088 per violation**. ### Target Firms and Alleged Offenses Here are the firms and the banned review practices of which they were accused: Business Name| City| Prohibited Practice| Category| ---|---|---|---|--- Provident CPAS PLC| Tulsa, Ok| Providing a $100 fee discount to customers in exchange for the customer posting a 5-star Google review.| CPA| Glasheen, Valles & Inderman, LLP| Lubbock, Tx| Providing a $50 gift card incentive to employees in exchange for each 5-star review obtained from family or friends who were not clients and had no actual experience with the firm.| Personal Injury Craig Swapp and Associates| Sandy, UT| Providing a $50 bonus to employees for each 5-star review from non-clients (family/friends), and soliciting reviews at community events from individuals without actual experience with the firm. +1| Personal Injury Emerson Straw PL| St. Pete, FL| Providing a $50 gift card incentive to employees in exchange for each 5-star review obtained from family or friends who were not clients and had no actual experience with the firm.| Personal Injury Somerset Apartment Management, LLC (d/b/a Sandhurst Apartment Management)| Charlotte, NC| Conditioning entry into a raffle for a 50-inch TV on a tenant's submission of a 5-star review, and allowing unlimited raffle entries for each positive review. | Property Management Weidner Property Management LLC| Kirkland, WA| Conditioning entry into a raffle for a choice of a $500 gift card or $500 rent discount on a tenant's submission of a 5-star review.| Property Management Hortenstine Properties, LLC (d/b/a Lincolnshire Apartments)| Mattoon, IL| Providing a $15 lease credit to tenants in exchange for the tenant posting a 5-star Google review of the apartment complex.| Property Management Tec Management, L.C. (Hilltop Towers)| South Ogden, UT| Conditioning a $25 account credit and entry into a raffle for a $300 gift card and 1-year Sam's Club membership on a tenant's submission of a 4- or 5-star review.| Property Management 10 Stars Property Management LLC| Pinellas Park, FL| Providing lease fee discounts to prospective and current tenants in exchange for their publishing a 5-star Google review.| Property Management Allview Real Estate| Newport Beach, CA| Providing $10 Starbucks gift cards to tenants in exchange for their publishing 5-star Google or Yelp reviews.| Property Management ### Analysis and Takeaways The law firms, while not mom and pops, are mostly local and a few are regional in scope. None are national in scope. Emerson Straw LLP, a boutique firm, appears to have 7 employees and one main office. Glasheen, Valles & Inderman Injury Lawyers operates 7 locations across Texas and New Mexico with fewer than 40 employees. The largest, Craig Swapp & Associates has 14 offices across Utah, Idaho and Washington with fewer than 100 employees. The property management firms were similar with sizes ranging from a few employees to the largest several hundreds. While I presume that the FTC is not going out and actively looking for offenders, you are not too small to be targeted if you have been reported to them for review programs that violate "The Rule". ### Types of Offenses The three law firms all were targeted for incentivizing employees (not a problem) to solicit reviews from non customers (the problem) including friends, family and community events. The similarity in their incentive programs are so similar, one has to assume that the idea was promoted at some conference or in a "mastermind group" of some sort. The property management firms were called out for conditioning their incentives in exchange for a specific star rating. Incentives, in and of themselves, are NOT against the rule but if incentives are used, then the review must clearly state that they were in place and they can not be predicated on receiving a certain rating. This makes it illegal to incentivize Google or Yelp reviews as there is no mechanism to indicate the incentives. As a note, while incentive are not illegal under the FTC Rules they are against the Terms of Service at most review sites. ### The Outcome The firms that were singled out by the FTC were obligated to quickly put together a remediation plan, attempt to get the offending reviews removed and promise not to ever do this again. In theory, the FTC would call them out publicly from their bully pulpit to insure broader compliance. However, that really hasn't happened to date. The publicity around these cases were limited to the December press release noting that the letters were sent. However, garnering the names of the offenders and the specific allegations of their behaviors required that a Freedom of Information Act request be filed. And to date there has been no public mention of these firms, let alone public shaming. ### The Future of Reviews The FTC’s current enforcement strategy relies on a "bully pulpit" approach, yet the noise has been surprisingly quiet. Identifying these offenders required a Freedom of Information Act (FOIA) request; the FTC has not engaged in any proactive "public shaming." If the only consequence for a violation is being asked to submit a "remediation plan," review abuses are likely to continue. With the FTC providing minimal publicity and Google struggling to catch sophisticated fraud, a further decline in public trust regarding review integrity seems inevitable. * * * You can download the complete set of letters here: FTC Warning LettersWarning letters to each offender including violationsmaterial-for-release-2026-00567.pdf17 MBdownload-circle

The "Ask Maps" conversational UI represents a shift in local search, prioritizing natural language and hyper-personalization based on user history and saved labels. However, this AI interface often surfaces fragmented or outdated data from "orphaned" URLs and old citations, making it imperative for businesses to maintain "content freshness" through frequent updates to service pages and reviews. ### The Podcast Deets & Take-Aways **Segment 1: The New Paradigm: Conversational Search in 2026** Timestamps: 00:00 – 10:20 This segment defines the scope of the 2026 search landscape, contrasting the "listicle" model with the new synthesized, recommendation-driven conversational interface. It demonstrates that natural, complex, relationship-based prompts (like "halfway between") are now effectively resolved by Google AI, validating the panel’s predictions from preceding years. **Segment 2: Hyper-Personalization: Building the Competitive Moat** Timestamps: 10:20 – 18:28 The panel shifts focus to the core strategic asset Google leverages in Ask Maps: implicit and explicit personalized data. They establish that user-defined labels are the single most powerful tool a business can utilize to capture repeat business, defining the new boundaries of customer retention and competitive defense against other large language models. **Segment 3: Data Integrity and the Threat of AI Hallucinations** Timestamps: 18:28 – 23:20 This segment provides the reality check for local businesses. The panel probes the dangers of data entropy, proving that forgotten web pages, old blog posts, and outdated citations are the primary sources of AI "hallucinations." They establish that data hygiene is no longer just about NAP (Name, Address, Phone) consistency; it is about content integrity across the entire historical domain footprint. **Segment 4: The Strategic Response: The Freshness Mandate and "Vibe Coding"** Timestamps: 23:20 – End The discussion concludes with actionable strategies for SEO professionals and business owners in the Ask Maps era. It moves beyond theory to define "Freshness" as the new foundational tactic. The segment makes a convincing case that a "set and forget" GBP profile or website is no longer viable in an index that prioritizes current activity over historic domain authority.Key Takeaways: **👇 Watch by topic:** 00:00 - Introduction to Ask Maps & The Panel 01:42 - Live Demo: Ask Maps Desktop Interface 05:13 - Hyper-Personalization & User Search Behavior 14:19 - Google Maps vs. Apple Maps & ChatGPT 18:28 - Data Integrity: Where AI Gets Its Information 21:35 - The Freshness Mandate: Staying Relevant in AI Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **Full Transcript** --> On-line

### Yelp: 57% Using AI for Local Search Yelp recently upgraded its AI assistant, expanding capabilities and integrating third party tools (e.g., ZocDoc, DoorDash), to enable users to "take action." Simultaneously, the company released a survey of 2,200 US adults about AI local search usage. It found that 65% of respondents used AI search in the past 6 months, and 57% "use AI tools to find local businesses at least once a month." However, Yelp believes the study's true headline is "only 15% of respondents 'trust [AI] a lot.'" Accordingly, nearly two-thirds of respondents said they look up other sources to validate AI findings. The survey is silent on the specific other sources, but think: Google reviews. This is what our research has discovered; people use AI for local and then validate on Google. Survey respondents complain about a lack of transparency or visibility into AI sources as the chief reason for their distrust or skepticism. The survey goes on to report that people really want trusted citations, especially human experiences and reviews. (Yelp believes it has an inherent trust advantage.) The survey also presented competing AI local search scenarios (restaurants, HVAC): one with text only and no citations and the other with rich information, including reviews, and booking widgets. Of course "respondents preferred the AI answers that featured authentic human content and trusted sources." Gen Z was the most interested in seeing what was behind AI answers, but so was everyone else. Source: Yelp, Morning Consult ### This post is for subscribers only Become a member to get access to all content Subscribe now

This week’s Near Memo explores the legal and regulatory pressures squeezing Google and local businesses alike. Mike shares FOIA results revealing the specific small businesses the FTC is targeting for incentivized reviews, while Greg analyzes Yelp’s motion to fast-track their antitrust case following the historic Mehta ruling. We also look at the technical expansion of LSAs and why "Ask Maps" might be the conversational interface that finally replaces the traditional local pack. ### The Podcast Deets & Take-Aways 1. **The Review Pressure Cooker:** Analysis of new Google guidelines regarding "undue pressure" in review solicitation and the (non?) risks of using QR codes and employee names. 2. **FTC's Small Business Review Crackdown:** A breakdown of FOIA data showing which small-to-mid-sized firms (legal/property management) for review fraud and incentivized "friend and family" reviews the FTC is targeting. 3. **LSA Expansion & Scraping:** Google's move to bring Local Services Ads to restaurants and the technical shift toward scraping landing pages to auto-generate ad content. 4. **The Yelp vs. Google Endgame:** Yelp’s strategic use of the "Mehta" antitrust ruling to fast-track their own litigation and how the potential rise of "Ask Maps" as the new local UI may make any Yelp victory hollow. ### Related Links Why Are My Google Reviews Disappearing? What Businesses Need to Know About Googles New Review Policies and Algorithm UpdatesIn 2024, Google blocked or removed more than 240 million policy-violating reviews. That is over 40% more than the year before, according to Google’s own annual spam report.LinkedInClaudia TominaFTC Warning Letters About Review Abuses10 letters to the offending companies and details about the review violations committedNear MediaNear MediaLegal Hallucinations, Review Changes, 16% Mobile CTRs, Legal Ads in AI ModeThis is Near Media’s new monthly legal marketing newsletter.Near MediaGreg SterlingRestaurants now on Local Services Ads. Piloting in Atlanta, Boston, Miami, San Jose Haven't seen it in the wild yet. Curious to hear your feedback. | Eric LevineRestaurants now on Local Services Ads. Piloting in Atlanta, Boston, Miami, San Jose Haven’t seen it in the wild yet. Curious to hear your feedback.LinkedInEric LevineYelp Vs Google FilingsRecent filings in the Yelp vs Google Anti-trust fileNear MediaNear Media **👇 Watch by topic:** 00:00 - Introduction & Near Memo Kickoff 01:07 - The Truth About Google’s "Review Pressure" Rules 06:51 - Yelp's New Solicitation Survey 10:11 - FTC Warning Letters: Who’s Getting Caught? 14:47 - LSAs for Restaurants: Will They Actually Work? 19:05 - Yelp vs. Google: The "Mehta" Ruling & Antitrust Update 30:27 - The Future: Ask Maps & AI Conversational UI Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **Full Transcript** of the Near Memo EP 253 – > on-line

FTC Warning LettersWarning letters to each offender including violationsmaterial-for-release-2026-00567.pdf17 MBdownload-circle Business Name| City| Prohibited Practice| Category| ---|---|---|---|--- Provident CPAS PLC| Tulsa, Ok| Providing a $100 fee discount to customers in exchange for the customer posting a 5-star Google review.| CPA| Glasheen, Valles & Inderman, LLP| Lubbock, Tx| Providing a $50 gift card incentive to employees in exchange for each 5-star review obtained from family or friends who were not clients and had no actual experience with the firm.| Personal Injury Craig Swapp and Associates| Sandy, UT| Providing a $50 bonus to employees for each 5-star review from non-clients (family/friends), and soliciting reviews at community events from individuals without actual experience with the firm. +1| Personal Injury Emerson Straw PL| St. Pete, FL| Providing a $50 gift card incentive to employees in exchange for each 5-star review obtained from family or friends who were not clients and had no actual experience with the firm.| Personal Injury Somerset Apartment Management, LLC (d/b/a Sandhurst Apartment Management)| Charlotte, NC| Conditioning entry into a raffle for a 50-inch TV on a tenant's submission of a 5-star review, and allowing unlimited raffle entries for each positive review. | Property Management Weidner Property Management LLC| Kirkland, WA| Conditioning entry into a raffle for a choice of a $500 gift card or $500 rent discount on a tenant's submission of a 5-star review.| Property Management Hortenstine Properties, LLC (d/b/a Lincolnshire Apartments)| Mattoon, IL| Providing a $15 lease credit to tenants in exchange for the tenant posting a 5-star Google review of the apartment complex.| Property Management Tec Management, L.C. (Hilltop Towers)| South Ogden, UT| Conditioning a $25 account credit and entry into a raffle for a $300 gift card and 1-year Sam's Club membership on a tenant's submission of a 4- or 5-star review.| Property Management 10 Stars Property Management LLC| Pinellas Park, FL| Providing lease fee discounts to prospective and current tenants in exchange for their publishing a 5-star Google review.| Property Management Allview Real Estate| Newport Beach, CA| Providing $10 Starbucks gift cards to tenants in exchange for their publishing 5-star Google or Yelp reviews.| Property Management

Yelp MSJ re Issue PreclusionYelp MSJ re Issue Preclusion.pdf384 KBdownload-circle Discovery Letter re Google ResponsesDiscovery Letter re Google Responses.pdf231 KBdownload-circle Discovery Letter re Yelp ResponsesDiscovery Letter re Yelp Responses.pdf295 KBdownload-circle

💡 Sign up for our Legal Marketing Newsletter here. If you want deeper behavior insights, tactical and brand intelligence, talk to us about our legal market research program. ### Legal Hallucination & Humiliation Lawyers in an Oregon case involving vineyard ownership were recently sanctioned more than $100,000 by a federal judge for AI errors and fabrications in their court filings. This case joins a growing list from around the US where lawyers have been shamed and penalized for AI errors in legal documents, extending even to the most expensive "white shoe" firms. While it's not clear which system generated the hallucinated material, legal research vendors LexisNexis and Thomson Reuters both make strong promises about the accuracy of their products. Specifically, LexisNexis makes the claim its tools are effectively hallucination free. Researchers at Stanford and Yale decided to test those claims. They compared Lexis+ AI, Ask Practical Law AI (Thomson Reuters) and GPT-4 on 200 legal queries. What they found was that none of these tools in fact had _eliminated_ hallucinations, which still showed up in a substantial number of cases – even when RAG was involved. Lexis+ AI answered more questions and delivered accurate responses "about 65% of the time." Remarkably, Thomson Reuters’ tool punted on about 60% of queries – simply not providing an answer – and generated accurate responses <20% of the time. Researchers found the failures were both significant and subtle: use of irrelevant sources, bad case authority, incorrect citations and others. The less obvious errors (vs. outright fabrication) were harder to detect and potentially more dangerous accordingly. The temptation to turn over research and writing to AI is great, but as this Oregon case shows, you do so at your own peril. ### This post is for subscribers only Become a member to get access to all content Subscribe now

This episode explores three major shifts: Google’s tightening review enforcement and what signals drive removals, Meta’s potential rise past Google in digital ad share despite ethical concerns, and the growing but under-measured impact of AI on search behavior. Together, these trends reveal a rapidly evolving landscape where traditional SEO, attribution, and customer journeys are being fundamentally redefined. ### The Podcast Deets #### **1. Google Review Crackdown (** 00:00–14:50**)** Google’s updated policies targeting incentivized reviews, especially those tied to employee names and unusual volume patterns at a store location. Enforcement appears algorithmic, combining multiple signals. Businesses must rethink review generation strategies and normalize volume. #### **2. Meta vs Google + Ad Market Shifts (** 14:50–23:15**)** Meta is projected to surpass Google in ad share, even amid legal and ethical controversies. Rising costs and declining ROI clarity are pushing marketers to diversify channels, highlighting growing the conflict of marketers dealing with Facebook despite their unethical behavior. #### **3. AI Search & the Future of Discovery (**23:15–End**)** AI tools like ChatGPT and Ask Maps are reshaping search behavior in ways analytics don’t capture. Google’s infrastructure advantage positions it strongly, while OpenAI may pivot enterprise. The future of local search will be conversational and intent-driven and is likely driven by Google. ### Key Takeaways * Google's newest review enforcement **to prevent in-store pressure** is **multi-signal** * Review volume spikes + named employees = risk factors * Meta’s dominance persists despite serious ethical issues * Ad ROI is becoming harder to measure and justify * AI is already changing behavior—but **analytics lag reality** * Google has structural advantages in consumer AI * Ask Maps signals a shift to **conversational local search** * GBP content must evolve to answer complex, intent-rich queries ### Related Links Google review enforcement over the past 6 months or so has been up or so it seems from reports of missing reviews in the GBP forums. It seems to be up even further in April (red is a projection to… | Mike BlumenthalGoogle review enforcement over the past 6 months or so has been up or so it seems from reports of missing reviews in the GBP forums. It seems to be up even further in April (red is a projection to month end) Is this due to the new Google review guideline prohibiting merchants from requesting that specific content, like staff member name, be included? From a technical viewpoint employee name is an easy proxy. But it occurs at some low volume IRL whether the business requires it or not. Google also says that “content exhibiting unusual volumes or patterns of review contributions that are indicative of efforts to manipulate a place’s rating” are prohibited. But there are times when review volumes go up like a grand opening. Google adds that “merchants should not require or pressure users to leave ratings or write reviews while on the premises, nor should they request that specific content be included”. This indicates to me that there are three proxy signals that Google is using to target in-store pressure: location, volume and staff names. While we haven’t nailed down the specifics my gut says that review removals for in store pressure is some combination or aggregate value of the three signals. what does this mean in practice? -Service Area business might be less likely to get this “penalty” (operative word is might) -It is probably good practice to figure out a different way to incentivize employee garnered reviews other than their name -If you are having an on-site event, be somewhat restrained in review gathering And as always you should keep track of all reviewer names and review content should some block of reviews goes missing so that you are able to request help from support. They are passive-aggressive about not helping in these situations without that data.LinkedInMike BlumenthalProhibited & restricted content - Google Business Profile HelpFake &amp; Misleading Content &amp; Behavior&nbsp; Fake &amp; Misleading Content &amp; ReviewsGoogle Business Profile HelpMeta Has Made Child Exploitation a Cost of Doing BusinessMeta has not only survived multiple scandals, its advertising revenue continues to creep up.AdweekMark RitsonMeta to Surpass Google in Digital Ad Revenues for First Time EverMeta will overtake Google in the US and worldwide April 13, 2026 (New York, NY) – This year will be a watershed moment in the digital ad industry, as a major upset will take place. According to our latest Emarketer forecast for ad spending, Meta will overtake Google in terms of total digital ad revenues […]EMARKETERDouglas ClarkIAB/PwC Internet Advertising Revenue Report: Full Year 2025Discover key insights from IAB’s Full Year 2024 Internet Ad Revenue Report. Download now to stay informed!IABApr. 16, 2026 **👇 Watch by topic:** 00:00 Intro & Episode Overview 01:00 Google Review Policy Updates 04:30 Review Removal Signals & Missing Reviews 08:00 Point-of-Sale Reviews & Incentives 12:55 Ethical Review Strategies 14:50 Meta vs Google Ad Market Shift 18:00 Rising Costs & ROI Challenges 20:30 Meta Legal Issues & Ethics Debate 23:15 Section 230 & Product Liability Theory 26:50 AI Search Behavior Is Changing 28:15 Ask Maps & New Discovery Patterns 31:00 Who Wins AI? Google vs OpenAI vs Meta 34:00 Future of Search & Monetization Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **Full Transcript** of the Near Memo EP 252 – > on-line ###

### Ask Maps Is the Future I was recently in Oaxaca, Mexico for a short vacation with friends and posted on LinkedIn about some of my real-world experiences with Ask Maps, ChatGPT (with precise location), and on-site restaurant review solicitation. ChatGPT with precise location is worthy of its own discussion. But Ask Maps is a more important development in the world of local search. From its January, 2025 debut as "Ask Maps about This Place" on individual GBPs, Google has now fully integrated its local graph – "information from over 300 million places, including reviews from our community of more than 500 million contributors" – behind a conversational UI, which is much more useful than the traditional search bar. I used Ask Maps a lot in Mexico for important queries such as, "Find me the best rated rooftop bar within walking distance that is open late and also has a decent food menu." Since my return I've also used it multiple times for more mundane and practical local searches. I had almost entirely abandoned Google Maps. Now I'm using it again. Other than for simple brand and directions searches, Ask Maps offers an overall better experience. I would predict it will gradually become more prominent over the next 12 months. Local marketers will need to start thinking not just about data and factual information but about customer intent, key decision criteria and the user journey more directly in managing their GBPs. You really need to get much more deeply into the mind of the customer/know the customer well. Many SEOs argue that AI Mode is the future of the SERP, but I'm not convinced. However, Ask Maps definitely seems like the future of local search on Google Maps. ****Ask Maps: Local Graph Behind Flexible UI Source: Google ### This post is for subscribers only Become a member to get access to all content Subscribe now

Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **The Podcast Deets** ### **1. The Truth About Click Data & Ranking Signals (**00:00–13:30**)** The episode opens with a deep dive into Google’s long-denied use of click data, now confirmed through court documents and leaks. Cyrus explains key concepts like **long clicks and “last longest click”** , showing how Google measures satisfaction. The discussion ties this to local SEO, where conversions, engagement, and actions (calls, directions) mirror these signals. ### **2. AI Overviews, Brand & The Future of SEO (**13:30–End**)** The second half explores how AI Overviews are reshaping search behavior. While reducing clicks for publishers, they increase reliance on **predicted engagement and brand signals**. The group discusses how AI drives **brand discovery and follow-up searches** , and Cyrus outlines tactical strategies for earning clicks, improving engagement, and optimizing for **task completion instead of traffic**. ## **Key Takeaways** * **Google uses click data heavily** —especially “last longest click” as a proxy for satisfaction * **Task completion > traffic** is the new SEO north star * **AI Overviews reduce clicks but increase brand importance** * **Brand searches are a critical ranking signal** (and a proxy for AIO visibility) * **Bad clicks hurt rankings** —misleading titles backfire * **Engagement metrics (time, interaction) correlate with rankings** * **Own the full user journey** —don’t send users to third-party platforms * **Optimize for what happens after the click** , not just the click itself **👇 Watch by topic:** 00:00 Introduction & Cyrus Shepherd joins 01:15 The click data controversy explained 04:20 Clicks, long clicks & “last longest click” 07:30 Local SEO, conversions & task completion 11:00 Brand search as a ranking signal 13:30 AI Overviews: impact on clicks & traffic 16:30 How Google predicts clicks with less data 19:00 AI Overviews + brand search behavior 22:45 How to earn better clicks (titles, UX, engagement) 25:00 Engagement metrics & GA4 insights 26:30 Bad UX, popups & failed task completion 27:30 Final thoughts & where to follow Cyrus Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### Related Links How Google Click Signals Drive SEO Rankings and AI AnswersThe AI SEO Marketer’s Playbook for improving user click signals in a way Google rewardsZyppy Signal with Cyrus ShepardCyrus ShepardHow AI Overviews Shift Traffic From Publishers to Google - ZyppyGoogle’s AI Overviews are seemingly designed to shift user clicks away from websites and nudge them towards Google ads. Zero-click answers leaveZyppyCyrus ShepardCyrus S. - Zyppy SEO Consulting | LinkedInI&#39;m an SEO consultant, online marketer, content publisher, speaker, and writer. Several… · Experience: Zyppy SEO Consulting · Education: University of Southern California · Location: Astoria · 500+ connections on LinkedIn. View Cyrus S.’s profile on LinkedIn, a professional community of 1 billion members.LinkedInCyrus S.Cyrus Shepard, Founder of Zyppy SEOI’m Cyrus Shepard, a data-driven SaaS SEO consultant who grows qualified website traffic with evidence-backed testing and clear, actionable straZyppy ### **Full Transcript** of the Near Memo EP 251 --> on-line

Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** **1. AI Hype vs Reality (** 00:00–04:44**)** The conversation opens with growing executive concern about AI and “agentic commerce.” However, real-world data shows AI contributes less than 1% of traffic and revenue for most brands. The takeaway: don’t panic—but pay attention. **2. What Actually Drives Visibility (** 04:44–13:10**)** The discussion shifts to what’s working now. Intent pages—originally built for SEO—are now powering visibility in AI tools. The shift from keywords to intent, combined with insights from reviews & photos, is shaping the next generation of content strategy. **3. The Future of Local Search (** 13:10–End**)** AI isn’t replacing Google—it’s changing behavior. Websites are becoming data sources, brand awareness is driving clicks, and review diversification across platforms like Yelp and TripAdvisor is becoming essential. The future rewards foundational SEO, not shortcuts. ### **Key Takeaways** * AI traffic is currently **negligible ( <1%)** for most brands * “Agentic commerce” is **overhyped (for now)** * **Intent pages are a major competitive advantage** in AI search * Reviews are not just reputation—they are **content + ranking signals** * Brand awareness is increasingly critical in a **zero-click world** * Websites are evolving into **data sources for AI systems** * Review diversification (Yelp, TripAdvisor, etc.) matters more than ever * Long-term SEO (“the garden”) beats short-term tactics **👇 Watch by topic:** 00:00 Intro – AI and multi-location marketing 00:18 Are clients worried about AI visibility? 01:10 The “agentic commerce” panic 02:05 What actually matters for AI SEO 03:05 AI traffic reality ( less than 1%) 04:44 AI vs Google behavior by category 05:46 Ask Maps and discovery changes 07:42 Intent pages driving AI visibility 10:04 From keywords → intent 11:53 Using reviews as content strategy 13:10 Zero-click search and attribution 14:00 Measuring AI → offline conversions 15:07 Are websites becoming data sources? 16:05 Brand awareness and marketing shift 18:49 Long-term SEO strategy (“the garden”) 21:45 AI hype vs Google dominance 23:26 Review diversification strategy 25:06 Yelp, TripAdvisor, and local authority 26:14 The future of local search (3–5 years) 27:43 Final takeaways ### Related Links Steve Wiideman“SEO STEVE” WIIDEMAN WEANS BUSINESSES AWAY FROM OLD-SCHOOL AGENCIES WITH A REVOLUTIONARY, SELF-EMPOWERING SEO ROADMAP Specializing In Strategic Planning For Multi-Location And Franchise Brands, Wiideman Consulting Group Founder Has Created Powerful Key Strategies for SKECHERS, Public Storage and Linksys Steve Wiideman is a big believer in a famous quote by Jim Rohn, the influential entrepreneur, […]Wiideman Consulting Group Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript** of the Near Memo EP 250 --> On-line

### Local Ads Coming to Apple Maps Apple will be launching local ads in Maps in the next few months, according to a report from Bloomberg. Apple Business Connect (soon Apple Business) listings must be claimed to participate. This is something that has been coming as part of Apple's broader ads expansion. Like Google Ads, local marketers will bid on search queries. Apple investors are hungry for growth and ads is a category ripe for expansion. As a result, Apple is putting ads in more places: App Store, News, Podcasts, Apple TV and now Maps. According to third party estimates, Apple's ad revenue is projected to reach almost $9 billion this year. But given Apple's large and affluent audience, and likely demand, local ads could become a fast-growing source of revenue. Apple's services business, which includes ads, exceeds $100 billion and contributes more than 25% of total company revenue. Ads could push that percentage even higher. Yet by putting ads in more places, Apple is becoming more like Google and Meta, companies it has criticized for turning users "into the product." Apple is trying to thread the needle and maintain privacy while delivering performance to marketers. The company's site says that Apple Maps ads will use only contextual signals (search terms, approximate location, map area) but no location history, no behavioral targeting, no age/gender targeting and no data will leave your device. Local ads will be available in the US and Canada at rollout. ****Apple's New Local Ads on Maps Source: Apple ### This post is for subscribers only Become a member to get access to all content Subscribe now

Multi-location SEO is less about algorithms and more about execution inside complex organizations. In this episode, the team explores how brands struggle with internal alignment, why testing beats tactics, how reviews and imagery drive visibility, and how search behavior is shifting toward long-tail and conversational queries. Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** ### **Segment 1 (** 00:00 – 07:30**)** **Foundations & The Scientific Approach to SEO** Steve Wiedemann outlines his background and approach to SEO as a discipline rooted in testing and experimentation. The conversation highlights the difficulty of documenting SEO due to constant change and the need to rely on fundamentals. ### **Segment 2 (** 07:30 – 17:00**)** **Why Multi-Location SEO Breaks Down** The group explores the real challenges of scaling SEO across large brands—organizational friction, legal constraints, dev bottlenecks, and franchise complexity. Education and alignment emerge as the biggest barriers. ### **Segment 3 (** 17:00 – 25:30**)** **What Actually Drives Performance: Content, Reviews & Behavior** The discussion shifts to practical execution: imagery, user-generated content, reviews, and long-tail queries. They highlight how real-world customer behavior—not keywords—should drive strategy. ### **Key Takeaways** * Multi-location SEO is primarily an **organizational challenge** * Testing beats tactics—always * Reviews and imagery are **core ranking + conversion signals** * Franchisee participation is low—but highly impactful when it happens * Long-tail and conversational queries are increasing * SEO success requires **consistent execution, not hacks** **👇 Watch by topic:** 00:00 Intro & Steve Wiedemann background 02:30 Why SEO is hard to “teach” (and constantly changing) 05:00 The scientific approach to SEO testing 07:30 Biggest multi-location SEO challenges 12:40 Photography, reviews & user-generated content 17:00 Getting franchisees to actually take action 20:10 Common SEO mistakes brands make 22:00 Long-tail search behavior & menu-driven queries 25:30 The shift toward conversational search ### Related Links Steve Wiideman“SEO STEVE” WIIDEMAN WEANS BUSINESSES AWAY FROM OLD-SCHOOL AGENCIES WITH A REVOLUTIONARY, SELF-EMPOWERING SEO ROADMAP Specializing In Strategic Planning For Multi-Location And Franchise Brands, Wiideman Consulting Group Founder Has Created Powerful Key Strategies for SKECHERS, Public Storage and Linksys Steve Wiideman is a big believer in a famous quote by Jim Rohn, the influential entrepreneur, […]Wiideman Consulting GroupBooksWe offer high-end search engine optimization strategy, consulting, training, and guidance, with core competencies in multi-location and e-commerce SEO & SEM.WiidemanRyan Jones - Detroit SEORyan JonesRyan Jones on Search Engine Journal Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript** of the Near Memo –> on-line

Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** **1. 📉 The Data Shift: Impressions Down, Conversions Stable (00:00–13:45)** BirdEye’s analysis shows a 54% drop in impressions but only a ~5% decline in actions. This suggests that AI and improved search efficiency are compressing the funnel—reducing exploratory searches while preserving intent-driven conversions. **2. 🧠 AI, LLMs & the New Discovery Layer (13:45–30:00)** AI Overviews and LLMs are increasingly shaping brand discovery. Visibility is becoming binary—either you’re cited or you don’t exist. Google still owns the “last click,” but discovery is fragmenting across AI interfaces. **3. 🗺️ Ask Maps & the Future of Local Search UX (30:00–End)** Google’s new Ask Maps feature introduces conversational, intent-rich search within Maps. It enables complex queries (e.g., midpoint searches, multi-constraint queries), signaling a major shift from keyword-based search to task-based discovery. ### **Key Takeaways** * **Category search dominates (86%)** → brand is less important than visibility * **Impressions ≠ performance** → top-of-funnel shrinking, bottom stable * **AI is compressing the funnel** → fewer searches, faster decisions * **LLM visibility is binary** → cited or invisible * **Google still owns conversions (for now)** → especially via Maps * **Ask Maps is a major UX shift** → task-based, conversational local search * **Reviews are evolving** → from content → structured AI input * **GBP is not “set it and forget it”** → ongoing optimization required for higher visibility * **Ask Maps is a new paradigm in Local search** → constrained, accurate LLM local search will change user and Google behaviors. **👇 Watch by topic:** 00:00 – Intro & BirdEye Report Overview 02:15 – 86% of Searches Are Category-Based 05:00 – AI Overviews & Brand Discovery Shift 07:00 – GBP Impressions Down 54% (But Why?) 10:30 – The “Last Click” Still Belongs to Google 13:45 – Industry Differences: Calls vs Directions vs Website Clicks 18:30 – Why Most Businesses Still Fail at GBP Basics 23:00 – “Set It and Forget It” Is Dead 26:30 – Ask Maps: Google’s New AI Interface 30:00 – How Search Behavior Is Changing (Longer Queries) 33:30 – LLM Accuracy Problems & Data Trust Issues 36:30 – Reviews in an AI World (Structured Data Wins) 39:00 – Apple Maps vs Google Maps: Early Signals ### Related Links State of Google Business Profile 2026 | Birdeye UKDiscover what drives visibility, engagement, and customer actions on Google Business Profile. Benchmark your locations against top multi-location brands.Birdeye Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript** of the Near Memo –> on-line

Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** ### **1️⃣ How the Ranking Factors Study Works** **Timestamp:** 00:00–07:20 Darren Shaw explains the methodology behind the Local Search Ranking Factors survey. Dozens of experienced local search experts evaluate more than 180 potential ranking factors, scoring their impact across multiple areas including Local Pack rankings, organic results, conversions, and AI search visibility. ### **2️⃣ Reviews, Behavioral Signals & Traditional Local Ranking** **Timestamp:** 07:20–24:30 The panel discusses the growing importance of reviews—particularly review recency—and how Google likely uses them as a proxy for real-world engagement. They also debate whether behavioral signals like clicks, engagement, and brand demand may now outweigh traditional SEO signals such as links. ### **3️⃣ AI Search and the Future of Local Discovery** **Timestamp:** 24:30–43:30 The conversation shifts to the rise of AI-driven search interfaces such as ChatGPT and Gemini. The group explores how LLMs evaluate businesses differently—placing greater weight on mentions across trusted sites, curated lists, and industry citations rather than traditional link signals. ### **Key Takeaways** **1. Reviews remain one of the most powerful ranking signals** * Volume, rating, and **recency** all matter. **2. Behavioral signals may be Google’s strongest advantage** * Clicks, engagement, and brand demand may increasingly drive rankings. **3. Link signals appear to be declining** * Content engagement and real-world mentions may matter more. **4. AI search prioritizes reputation across the web** * Expert lists, industry sites, and brand mentions play a larger role. **5. Google still has a structural advantage** * Google Business Profile data + browser behavioral data give it unmatched insight. ### **👇 Watch by topic:** 00:00 Introduction – Darren Shaw & the Ranking Factors Study 01:10 Methodology of the Local Search Ranking Factors Survey 04:30 What's New in the Latest Survey 07:20 AI Search and the Future of Local Discovery 14:00 Review Signals and Why Review Recency Matters 18:30 Review Spam and Google's Enforcement Challenges 20:40 Surprising Findings and Divergent Opinions 24:30 The Decline of Link Signals 27:00 Behavioral Signals and Google's Data Advantage 30:20 AI Ranking Factors and Expert Lists 36:50 What Businesses Should Actually Focus On 41:10 Darren Shaw's AMA and Final Thoughts Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript -- > ****on-line** #

### The Return of Yahoo Local Back in the day, Google and Yahoo went head-to-head for local supremacy. Yahoo had a successful mapping product as well as listings and proprietary reviews. It also operated the largest SMB web and e-commerce hosting service (Yahoo Small Business, now part of Verizon). Then things fell apart. Yahoo outsourced search and maps and let local languish. Yahoo has been rebuilding since CEO Jim Lanzone took over (e.g., Yahoo Scout). A couple of weeks ago I stumbled upon a seemingly new Yahoo local experience. It's interesting and has good potential, but it's kind of buried. It leads with content ("pros share winter painting tips for inside and outside your home") rather than search or listings. There are five featured categories: moving, roofing, plumbing, painting, awards. There are also in-depth SMB roundups with titles like "best roofers in Boulder Colorado." Those articles contain profiles of well-rated contractors. In those articles are lead-gen links ("get estimate") though it's not clear if they're paid – though probably. If you scroll all the way down, you'll find a directory. You can either click and browse or search for services across a wide range of locations and categories. The SERPs have a clean UI, though the profile pages are somewhat thin. Yelp is the review source and the business listings are probably from a third-party vendor. Yahoo has an opportunity to offer something fresh and useful. We'll see whether they do. **Content Front and Center on Yahoo Local** Source: Yahoo ### This post is for subscribers only Become a member to get access to all content Subscribe now

In this Near Memo panel discussion, Greg Sterling and Mike Blumenthal bring together Eric Levine (Leadwise HQ), Claudia Tomina (Reputation Arm), Matt Casady (Sterling Sky), and Crystal Horton (Google Business Profile Platinum Product Expert) to examine how agencies are building services around Google Local Service Ads. The conversation explores ranking factors, pricing models, fraud challenges, lead quality issues, and how agencies set expectations with clients when managing LSAs in competitive markets. Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** ### **1️⃣ How Google Local Service Ads Actually Work (****00:10 – 14:30****)** The discussion begins with an overview of Local Service Ads and why they have become such an important part of Google’s local search ecosystem. Former Google employee Eric Levine explains that LSAs operate differently from traditional Google Ads, relying heavily on trust and responsiveness signals rather than pure bidding power. The panel explores the three primary ranking factors behind LSA visibility: reviews, service radius, and responsiveness to incoming leads. Reviews influence both consumer trust and algorithmic ranking, while responsiveness measures how consistently businesses answer calls and respond to inquiries. The panel also discusses how LSAs are increasingly connected to Google Business Profiles, meaning reputation management and local SEO signals may indirectly influence ad performance. ### **Key Takeaways** * LSA rankings are primarily driven by **reviews, service radius, and responsiveness**. * Unlike traditional PPC ads, LSAs cannot simply be **bought to the top position**. * Google Business Profile signals and reputation management likely influence LSA performance. * Businesses must manage **reviews and lead response processes** , not just ad settings. ### **2️⃣ Fraud, Lead Quality, and Managing LSAs for Clients (****14:30 – 33:00****)** The panel then turns to the practical challenges agencies face when managing LSAs for clients. Fraud remains a persistent issue in some verticals, particularly in industries like locksmiths, garage doors, and legal services. The group discusses how Google has strengthened verification processes, including identity verification and tighter integration with Google Business Profiles. Despite these improvements, agencies still encounter fake listings and difficulties removing fraudulent competitors. The conversation also covers lead quality and the removal of Google’s lead dispute feature, which previously allowed advertisers to contest invalid leads. Without that mechanism, agencies must focus on expectation setting with clients and careful campaign management to ensure LSAs remain profitable. ### **Key Takeaways** * Fraud and fake listings still occur in certain **high-risk industries**. * Google has implemented **stronger verification processes** , but enforcement remains inconsistent. * The removal of the **lead dispute feature** makes bad leads harder to challenge. * Agencies must set **realistic expectations with clients** about lead quality and competition. ### **3️⃣ Building an Agency Model Around Local Service Ads (****33:00 – End****)** In the final segment, the panel explores how agencies are turning LSAs into a service offering. Different pricing approaches are discussed, including flat monthly management fees, consulting retainers, and hybrid models that combine LSA management with broader local SEO services. The panel agrees that LSAs have evolved from a simple DIY product into something that often requires specialized expertise, particularly in competitive industries. The discussion concludes with a look at the future of LSAs as Google integrates AI-driven search experiences. Rather than disappearing, LSAs may become embedded within AI-powered recommendations that guide consumers to trusted service providers. ### **Key Takeaways** * Agencies typically charge **flat monthly management fees or consulting retainers** for LSAs. * Successful LSA management requires coordination between **marketing, operations, and reputation management**. * LSAs may evolve alongside **AI search experiences** , not disappear. * Brand recognition and trust signals may become **increasingly important as discovery shifts to AI tools and social platforms**. ### **👇 Watch by topic:** 00:00 Introduction to LSAs and the panel 01:30 Eric Levine’s background working on LSAs at Google 04:00 Fraud in Local Service Ads and verification challenges 07:30 Reporting fraud and removing fake LSA listings 10:20 The real LSA ranking factors (Reviews, Radius, Responsiveness) 14:40 Do users actually click beyond the top LSA results? 18:00 Competing in crowded markets like personal injury law 21:30 LSA pricing and cost per lead across industries 24:30 When LSAs don’t work for a client 27:00 How agencies manage and bill for LSAs 30:00 DIY vs agency management for LSAs 33:00 Handling bad leads and the removal of lead disputes 37:00 Multi-quote leads and how Google charges for them 40:30 Photo best practices for LSAs 44:30 Service area targeting strategies 48:00 The future of LSAs in AI search 52:00 Brand search, AI discovery and the evolving funnel 57:30 Final thoughts Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript -- > ****On-line**

### Lawyer AI Adoption Nears 50% According to recent survey data from the Thomson Reuters Institute, 41% of law firms and 47% of corporate legal departments have now adopted generative AI. The data were collected in Q4 2025 among 1,500 professionals in law, accounting, corporate risk and government. It was an extensive multi-country study but with the largest respondent groups coming from the US, UK and Canada (results aren't segmented by country). Among the AI adopters, 55% of lawyers reported using AI at least daily. Top use cases were: legal research (80%), document review (74%), document summarization (73%), brief or memo drafting (59%), correspondence drafting (55%) and contract drafting (49%). The majority of law firms (62%) and corporate legal departments (72%) said they believed AI should be utilized in their work. Yet there's also ambivalence and concern about potential negative impacts on the profession, including job displacement (see chart below). The survey reflects something of a disconnect between lawyers and clients about when AI use is appropriate. The bulk of AI platforms being used by firms constitute the usual suspects (i.e., ChatGPT, Gemini, Claude), some enterprise-focused AI tools (e.g., Copilot), and specialized industry AI software (e.g., Harvey). Yet less than a quarter of lawyers say they're tracking an ROI. When they are, the metrics are mainly cost savings and employee adoption. Source: Thomson Reuters **Summation:** * Baker McKenzie is laying off between 600 and 1,000 business employees because of AI. It's likely the start of a wave of major firm layoffs. * As AI usage increases so do errors. A database tracking hallucinations in legal filings identified nearly 1,000 instances so far. That will only grow. * Privilege problem: US v. Heppner (S.D.N.Y.) held that attorney-client and work product privileges might not attach to AI uploads and answers. ### The Secret World of LSA Rankings Near Media's recent podcast interviews with former Googler Eric Levine about LSAs contain a wealth of tactical information (links below). Levine was part of the original LSA team. He spent six years at Google focused on LSAs in different roles. Now he's founded an agency that specializes in LSAs, which are now present in more than 100 business categories and sub-verticals. Near Media data show that when LSAs appear (especially on mobile) they not only grab clicks but drive a significant percentage of all conversions. Levine offered best practices advice during the discussion. Among his recommendations, Levine says, service areas should be defined as broadly as realistically possible. He recommends automated (not manual) bidding for Lead Maxxing (as Gen Z might say). Images matter: you need four to six high-quality images – there are more specifics around photos in the interviews. He recommends opting-in to all CTAs: messaging, calls, and booking if available. Of course reviews are critical and local marketers should maintain a steady review cadence. He adds that responsiveness across customer channels will impact placement. Google rewards businesses that quickly engage and the company pays attention to behavioral signals (clicks, calls) – no surprise there. Levine explains that IVR systems (because they delay contact) are a hidden rankings killer. Marketers should also use callouts (e.g., years in business), up to six. He adds that LSAs work best for high-volume "near me" categories and intent-based service queries. Don't use LSAs if you're "too niche" or your services don't line up with Google's established LSA categories. **LSAs Now in 100+ Categories** Source: Google **Summation** * Here's our two-part Eric Levine Interview: Part 1 & Part 2.**** * There were a lot of subsequent questions from that session, so we're bringing Levine back in a forthcoming roundtable with additional guests. * LSAs are part of Google's larger "pay to play" creep. The only way to mitigate is to build your brand locally offline, and on other digital channels. ### AI for Lawyer Discovery One of the things we're tracking is how consumers are (or aren't) using AI as an alternative to Google for lawyer discovery. In our two recent national surveys we asked people where they would _turn first_ and what would they _rely on most_ to find an attorney. Basically, word-of-mouth and Google rank #1 and #2. AI is much farther down the list for bottom of the funnel lawyer discovery. But this finding doesn't tell the whole AI story. People are inclined to use AI to learn about the issues in their case. That's currently separate from lawyer finding, but it may not be for long. The differences between informational research and bottom of the funnel attorney lookups may quickly erode. As Google shows more AI Overviews (AIOs) and exposes more people to AI Mode, the distinction between "traditional" Google and AI becomes less meaningful. Our data show that people generally like and value the AI experience. And in user testing, we don't see AIOs "stealing" organic clicks – that's a job for LSAs. Instead the AIO often boosts lawyer awareness, which may prompt a click on a follow-up search. In our January survey people expressed openness to using AI Mode for lawyer finding. So despite the low adoption of AI today as a lawyer discovery tool, legal marketers must prepare for a near-term future in which the funnel is more compressed and informational legal research very quickly turns into lawyer finding. Source: Near Media Legal Consumer Survey Wave 1 **Summation:** * We're in the midst of a backlash of sorts among many SEOs and digital marketers, who point to low AI referral traffic. That's too simplistic. * AIOs are an AI Mode onramp. Yet Google tends not to show AIOs for "accident lawyer [city name]." Instead: LSAs, PPC ads and Pack ads. * Consumers see AI and conventional search as complementary and use them together. The future involves more blending, obviously. ### Need for Review Diversification BrightLocal's 8th annual consumer review survey reaffirms two things: 1) reviews are the top-driver of consumer decisions and Google continues to be the top review site, although it declined 12 points YoY. Beyond these, there are a number of other findings worth calling out. If you look at the chart below, what you see is a growing list of places users look for local recommendations, including ChatGPT, Apple Maps and AI Mode. And if you roll up AI into one category, it's now the #3 source for local recommendations. Consumer expectations of reviews/ratings are also rising; people want more reviews and higher ratings. Some of that, as Mike and I previously discussed, is a function of review inflation/fraud. The survey reflects that consumers are generally aware of review fraud and looking at multiple sources partly as a hedge against fake reviews. While consumers often can't detect fake reviews – because only a minority actually read review text – they know the problem exists and want fraudsters punished (97%), with some even calling for "jail time" (16%). The tactical takeaway is that marketers must double-down on reviews (recency matters a lot) and invest energy in accumulating reviews on other platforms, especially Yelp and to some degree relevant legal directories. More broadly, legal marketers must also be aware of the citations and sources being used by AI, with a focus on ChatGPT and Gemini/AI Mode. **Top Review Sites 2026** **Summation:** * If you focus on one thing, Google/GBP remains the most important. But there's more going on in the market and you shouldn't ignore it. * Surveys like this are helpful but legal marketers need vertically specific insights to avoid expending resources and time inefficiently. * One thing that can balance a weaker review profile is brand awareness. However, building local awareness is a long-term proposition. 💡 If you like this, sign up for more here. If you want deeper tactical and competitive insights, talk to us about our legal market research program. ### Sidebar * How to access AI search prompts inside Google Search Console. * You can't track AI ranking reliably – or can you? * Call buttons****migrating to ads and away from Local Pack. * How to respond to more aggressive Google local monetization. * Google may soon replace your GBP and landing page content with AI. * Review platforms appear more often in higher intent AI answers. * Review removals and changes in Google review guidelines. * Google nudging reviewers/local guides to provide more detail. * Yelp's annual (review) trust and safety report. * ChatGPT ads: brand awareness opportunity for lawyers? * Anthropic’s legal plugin (one of several) prompted huge SaaS sell-off. Learn more about Near Media's Private Market Intelligence**** program, combining **quantitative, qualitative and customized behavioral research**.

Episode 245 examines Google’s expanding AI integration into local search, evidence that LSAs — not AI Overviews — are draining clicks, new restrictions on scraping review data, and new data from Yelp and Bright Local showing emerging consumer frustration with review fraud. The episode explores how trust, monetization, and AI personalization are reshaping local marketing. Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** ### **1️⃣ Google AI & Click Economics (00:00–14:00)** Google is replacing structured GBP data with AI-generated summaries, nudging users into AI Mode, and expanding monetization pathways. Aleyda Solis’ study and Near Media research show ads — especially LSAs — are siphoning clicks. ### **2️⃣ Data Moats & Scraping Crackdowns (14:00–22:00)** Google restricts scraping, disrupting SerpAPI. The move reinforces Google’s data advantage against ChatGPT and Perplexity while signaling an increasingly closed ecosystem. ### **3️⃣ Reviews, Inflation & Consumer Backlash (22:00–36:00)** Yelp’s Trust Report and BrightLocal’s 2026 study show persistent fraud, rising consumer anger, review inflation in legal, and AI becoming the third source of recommendations. ### **Key Takeaways** * Google is integrating more AI in the Local Brand Panels * LSAs are the real click siphon — not AI Overviews. * Google is tightening control over review data. * Review inflation is undermining trust. * Consumers want harsher penalties for fraud. * AI search is increasing awareness but not eliminating click behavior. * Local marketers must diversify trust signals beyond Google. ### **👇 Watch by topic:** [ 00:00](https://www.youtube.com/watch?v=Fobx3u0Vk_0) Introduction & Episode Overview 01:30 Google Replacing GBP Content with AI 08:30 AI Mode & Monetization Signals 10:30 Are AI Overviews Killing Clicks? 14:00 LSAs as the Real Click Drain 16:00 Google Blocking Review Scraping 19:00 Yelp Trust & Safety Report 22:00 BrightLocal 2026 Review Survey 25:00 Consumer Anger Over Review Fraud 27:00 Legal Vertical Review Inflation 31:00 AI as the Third Source of Recommendations 35:00 Multi-Platform Validation Behavior 36:00 Final Thoughts ### **Related Links** Google Maps ‘limited view’ was hiding reviews, images for signed-out users U: Fixed]Google Maps is more than just a navigation app, as it contains a treasure trove of data about locations across…9to5GoogleBen Schoon[[Google Maps Reviews API] Low success rateSerpApi’s Status Page - [Google Maps Reviews API] Low success rate.[Google Maps Reviews API] Low success rate **Search Isn’t Just Turning to AI, it’s being Re-Monetized: Text Ads Are Taking a Bigger Share of Google SERP Clicks** **2025 Yelp Trust & Safety Report****** **Local Consumer Review Survey 2026: Star Ratings Keep Rising, Old Reviews Don’t Cut It** Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript -- > ****On-line**

In this wide-ranging conversation, Rand Fishkin shares new research on AI brand visibility and challenges assumptions about rank tracking, attribution, and zero-click search. While AI responses are inconsistent, aggregate brand presence can be measured statistically. However, as discovery shifts to platforms and attribution erodes, marketers must rethink how they build brand and measure impact. Interested in sponsoring this podcast or our newsletters please reach out to mblumenthal@nearmedia.co E-mail Mike ### **The Podcast Deets** ### **1️⃣ AI Visibility & Ranking Reality (****00:00–15:00****)** Rand discusses his experiment testing whether AI brand tracking is possible. While individual AI responses are inconsistent, brand frequency can be measured statistically across large samples. Rank tracking, however, is unreliable. The segment explores AI randomness, long-tail discovery, and how finite local categories behave differently from broad product categories. ### 2️⃣ Zero-Click, Discovery & Agentic Commerce (15:00–31:00) The conversation shifts to zero-click journeys and whether AI will disintermediate websites entirely. Rand argues that commerce isn’t disappearing—but discovery is moving onto platforms. The group debates UX differences between Google and generative AI and explores whether agentic commerce will meaningfully reshape transactions. ### 3️⃣ The Attribution Crisis & Brand Strategy (31:00–40:00) The final segment tackles marketing measurement. With AI platforms providing little referral transparency, the illusion of perfect attribution is breaking down. Rand explains why paid platforms retain power due to behavioral data, and why local businesses should focus on selective channel strength rather than being everywhere. ### **Key Takeaways** * AI rank tracking is unreliable; brand frequency tracking is possible. * Zero-click journeys are growing, but sales aren’t disappearing. * Discovery is shifting from owned media to platforms. * Attribution models are weakening as AI disintermediates clicks. * Paid platforms retain advantage via behavioral data. * Local businesses should focus narrowly, not universally. * “Don’t build on rented land” is no longer viable advice. ### **👇 Watch by topic:** 00:00 – Intro & 2026 Predictions 02:00 – Can You Track Brand Visibility in AI? 09:00 – AI Randomness & Long-Tail Brand Discovery 15:00 – Is AI More “Democratic” Than Google? 20:00 – UX Debate: ChatGPT vs Traditional Search 24:30 – Zero-Click, Discovery & Website Disintermediation 29:00 – Agentic Commerce & The Future of Transactions 31:30 – The Collapse of Attribution & Measurement Illusions 35:00 – Google’s Paid Media Advantage 37:30 – Where Should Local Businesses Build Brand? 39:30 – Final Takeaways ### **Related Links** NEW Research: AIs are highly inconsistent when recommending brands or products; marketers should take care when tracking AI visibility - SparkToroThe Problem: For the last few years, companies have been investing inordinate sums into AI tracking and AI visibility for their brands and products.SparkToroRand FishkinThe 5 Big Trends that Will Dominate Marketing in 2026 - SparkToroI find most end-of-year prediction and crystal ball-gazing listicles to be trash. They don’t prove their assertions, use anecdotes rather than data atSparkToroRand FishkinRand Fishkin’s Bio, Speaker Rates, Contact Details | SparkToroHow to book Rand Fishkin to speak at your event, tweet about your groundbreaking ICO (kidding! Please don’t), or get in touch via email.SparkToro Sign up for our new Legal Marketing Newsletter. Once a month, no fluff, just good ideas. Sign Up ### **Full Transcript -- > ****On-line**

Join us for our next **webinar on February 24th** : Before the Click: What Really Drives PI Clients to Choose One Lawyer Over Another. It's presented by GatherUp – next Tuesday. We'll unpack proprietary behavioral data that shows **how consumers search for a PI lawyer today** , what truly influences their choices, and how AI-driven results are reshaping competitive visibility. **Near Media’s user-behavior research pulls back the curtain on what real clients think and do before they ever land on your website.** While the research focuses on Legal, many of the findings are broadly applicable to other categories. ****We'll see next Tuesday!**** Sign Up **David, Mike & Greg**