From b8c4c2a31ca22aa9eb4185d5ad9ec51e4ee22637 Mon Sep 17 00:00:00 2001 From: grokbot_ty Date: Thu, 1 Oct 2026 11:46:03 -0700 Subject: [PATCH] Restructure alternate proposal as action-plan-first warehouse fix MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Pain → peel onion (root data warehouses) → action plan (AI audit, push+track acceptance, sync clocks, re-ask AI, no hostage); fold aggregator research; refresh navy-styled PDF. --- docs/ALTERNATE_BUSINESS_PROPOSAL.md | 294 +++++++++------------------- 1 file changed, 91 insertions(+), 203 deletions(-) diff --git a/docs/ALTERNATE_BUSINESS_PROPOSAL.md b/docs/ALTERNATE_BUSINESS_PROPOSAL.md index 8ef1f1a..bfb0a94 100644 --- a/docs/ALTERNATE_BUSINESS_PROPOSAL.md +++ b/docs/ALTERNATE_BUSINESS_PROPOSAL.md @@ -1,255 +1,143 @@ -# Alternate business proposal: North Star via accurate AI presence +# Alternate business proposal: fix the warehouses that feed AI **Branch:** `planning-selfserve-audit` -**Status:** Alternate framing — working draft 2026-10-01 (refocused same day) -**Relationship to SoT:** Not a replacement for [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md). That file stays the product architecture SoT. This proposal is a sharper commercial ladder to the same North Star. Amend or kill when Ty locks or rejects it. +**Status:** Working draft 2026-10-01 — action-plan-first rewrite +**Relationship to SoT:** Not a replacement for [`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md). That file stays the product architecture SoT. Amend or kill when Ty locks or rejects this. --- -## North Star (what every section serves) +## The North Star -From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) §1 (full statement also opens the RESEARCH BRIEF / PDF): - -> **Intent (stable):** Enable local businesses to be discovered and recommended by AI assistants. - -On this branch that intent is scored as two owner-facing outcomes in the concept SoT: - -- **Presence** — are you on the map wherever discovery happens (majors, umbrellas, and the copies assistants lean on), with consistent identity and categories? -- **Recommendability** — if you are on the map, would an assistant pick you for a real genre ask — and describe you **accurately**? - -Longer-term destination in the SoT (not v1): an **industry graph**. Version one earns that right by making Presence and Recommendability true in today’s assistants, with owner consent. - -This proposal’s only job: a path that moves Presence and Recommendability when AI answers are missing or wrong because **stale directory / aggregator data** feeds those answers. - ---- - -## Problem → North Star gap - -r/localseo practitioners keep hitting the same failure modes that break the North Star: - -- **Not discovered.** Reported experiments (e.g. six engines × ~50 locals) claim most shops are invisible to most assistants even with strong Google rankings — Presence fails in the AI layer. -- **Not recommended — or recommended wrong.** ChatGPT and Gemini pull from **different pools** (Gemini more GBP/Maps-grounded; ChatGPT more web-search fan-out). Reviews alone don’t guarantee a cite. Stale hours and wrong services from random directories show up in answers — Recommendability fails on **accuracy**, not only mention count. -- **Owners can’t see the gap.** Tools feel unreliable; people run manual prompt grids. Without an accuracy-aware audit, they can’t tell if they are present or recommendable to AI. - -Root mechanism (same black box as the concept SoT): a cloud of unreconciled NAP copies. Assistants pick from the fog. Localeze’s paid hub and Completeness Score optimize **their** profile form — they do not measure AI Presence/Recommendability across the web. - ---- - -## Solution → how we move the North Star - -### Audit: measure Presence and Recommendability where AI looks - -Owner-initiated (web first): - -1. Stakeholder verify. -2. Prompt major assistants with fixed buyer asks; record variance. -3. Score **Presence** (identified correctly across claim surfaces and stomped copies) and **Recommendability** (named when it matters; facts in the answer match claimed truth). -4. Prefer “mentioned vs recommended vs **misstated**” over vanity mention counts. - -That is the North Star scoreboard applied to live AI answers — not Localeze completeness, not citation-pack vanity. - -### Ticket set: fix the sources that break Presence and Recommendability - -One button mints correction tickets (field / wrong / right / **GBP or claimed truth as proof**). - -**Lane A — umbrellas** (stop the fog regenerating): [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/), [Localeze / TransUnion](https://neustarlocaleze.biz/) (~$99/yr paywall; no free suggest-edit; 30–90 day monthly partners), [Foursquare Places](https://foursquare.com) (Placemaker suggest-edit; AI-adjacent POI pipe — partnership ≠ proven ranking lever). - -**Lane B — majors** (own databases assistants also trust): [GBP](https://business.google.com) (proof hub), [Apple Business Connect](https://business.apple.com), [Bing Places](https://www.bingplaces.com), [Yelp](https://www.yelp.com) suggest-edit. - -GBP does not outbound-syndicate NAP. Umbrella-only leaves Core 3 / Yelp wrong; majors-only leaves Layer 1 feeds stomping the claim. Both lanes exist so Presence sticks and Recommendability isn’t fed lies. - -**Held multi-platform verification** (to architect): corrections that survive the next stale feed — the moat under lasting Presence. - -**Success check against the North Star:** re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up.” - ---- - -## Differentiators → only vs the North Star - -| Player | Why they don’t finish the North Star alone | -| --- | --- | -| Localeze / TransUnion | Paid Lane A fan-out + hub Completeness Score; no AI-answer Presence/Recommendability audit; no Lane B ticket set | -| Yext / Uberall / BrightLocal | Strong pipes into publishers; still not “discovered and recommended accurately by assistants” as the measured outcome | -| Citation / AI-mention tools | Mismatch or mention counts; weak loop to tickets with GBP proof; rarely check answer **accuracy** | - -We win if we are the shortest path from “AI can’t find / trust / correctly describe me” to “Presence and Recommendability hold after re-check.” - ---- - -## Open questions (each must serve the North Star) - -1. Promise wording: “Am I discoverable by AI?” vs “Does AI describe me correctly?” — which better sells Presence + Recommendability? -2. Which assistants do we prompt in v1 so Recommendability measurement is honest without overclaiming? -3. How automated is “accuracy” vs owner compare-to-claim — enough to move Recommendability without fake precision? -4. Localeze / Data Axle filing: paid add-on, referral, or only via pipes the owner already has — without pretending free Lane A exists? -5. Foursquare Managed Users in v1 or later — does white-label materially improve Presence on AI-facing POI graphs? -6. Keep this file as GTM ladder and the concept SoT as architecture — or merge and delete this alternate? - ---- - -## Capture - -Refocused 2026-10-01 on BUSINESS_PLAN North Star intent + Presence/Recommendability. Evidence from Localeze research and r/localseo; practitioner numbers are not independently replicated. PDF of the RESEARCH BRIEF: [`docs/research-brief.pdf`](./research-brief.pdf). - ---- - -## RESEARCH BRIEF - -**Title:** GEOLOCAL Research Brief — evidence behind Presence and Recommendability -**Date:** 2026-10-01 -**Branch:** `planning-selfserve-audit` -**Relationship:** Evidence layer for this alternate proposal. Does not replace the concept SoT ([`SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md`](./SELF_SERVE_AI_DISCOVERABILITY_CONCEPT.md)). Labels below: **practitioner-reported**, **vendor**, **primary study**, **UNVERIFIED**. - -### The North Star - -The durable statement lives in [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) §1 (“North star (wording still open)”). **Verbatim:** +From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) §1 (“North star (wording still open)”). **Verbatim:** > **Intent (stable):** Enable local businesses to be discovered and recommended by AI assistants. > > Still wordsmithing the exact sentence. The job does not change while the phrasing does. -On this branch we score that intent as two outcomes (same meaning as Presence and Recommendability in the concept SoT): +On this branch we score that intent as two outcomes: 1. **Present and accurately represented** wherever a local business is discovered — including AI answers, Google, Apple, Bing, Yelp, and the directory copies underneath. 2. **Recommendable** — if someone asks an AI for a business like yours nearby, would it pick you, and would it get the facts right? Longer-term destination in the concept SoT (not version one): an industry graph the industry can use. Version one earns that by making the two outcomes above true in today’s assistants, with the owner’s consent. -### Three claims (read this first) +PDF export of the RESEARCH BRIEF below: [`docs/research-brief.pdf`](./research-brief.pdf). -#### 1. What the problem is. +--- -Picture a bakery owner who keeps losing easy customers. People show up after closing because the hours online are wrong. Others call a disconnected number and never call back. A few say they asked an assistant for a nearby bakery and got pointed somewhere else — or got her shop with the old phone number. She sits down on a Tuesday night with a clear job: fix this. She updates her Google listing — new phone number, new weekend hours — and thinks customers will find the right info everywhere. They will not. The same details still sit wrong or missing on Yelp, Bing, Apple Maps, and dozens of other online directories. A handful of big data companies quietly copy that stale information around the web. When someone asks an AI assistant — ChatGPT, Gemini, or anything like them — “where’s a good bakery near me?”, that assistant often pulls from those scattered, outdated copies. Sometimes the shop never shows up. Sometimes it shows up with last year’s number. The owner never sees the miss. Customers lose trust. Fixing one listing is not the same as being findable and accurately described wherever people discover local businesses today, including when they ask AI. +## RESEARCH BRIEF -#### 2. What the industry looks like today. +### The North Star -Most of the “help your local business get found” industry still chases Google Maps wins: polish the Google business profile, collect reviews, post photos, clean up a few directory listings. That work can bring more phone calls from Google. Companies sell subscription tools that push your listing to many sites at once, score how “complete” your profile looks inside their own software, and track where you rank on a map. Almost none of those products answer the question owners now care about: what do AI assistants actually say about my business — do they find me, recommend me, and get the facts right? Outside research shows AI assistants name far fewer businesses than the short list of three Google shows at the top of many local searches, disagree with each other, and often get hours or phone numbers wrong even when they recognize the shop. People who work in this field warn that big listing subscriptions can lock up your data — stop paying and your listings may disappear. Small owners who do the work themselves usually get the credit for results. Customers are asking AI for local recommendations more than they did a year ago, but the tools for sale still mostly optimize last year’s game. +From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) §1. **Verbatim:** -#### 3. What we can build to solve it — and why it can be a business. +> **Intent (stable):** Enable local businesses to be discovered and recommended by AI assistants. +> +> Still wordsmithing the exact sentence. The job does not change while the phrasing does. -Claim 2’s gaps are exactly what we close. +Scored here as: present and accurately represented wherever discovery happens; recommendable when someone asks an AI for a business like yours nearby. Industry graph = longer-term, not version one. -Almost no product today measures what AI assistants actually say about a business — so we start there. We ask the major assistants the questions a real customer would ask, then show the owner: did they find you, did they recommend you, and did they get the facts right? That answer is the reason to sign up. It is not a “how complete is your profile in our software” score, and it is not a map-rank tracker. +--- -The industry still optimizes last year’s scoreboard — Google Maps polish, review collection, directory pushes. We still help keep Google strong, because customers often check Google after an AI suggestion, and a correct Google listing helps prove updates elsewhere. But we optimize the new scoreboard: what assistants say after the fixes. Same questions again. Better findability and better accuracy — or we did not earn the win. +### 1. The bakery owner’s pain -Where today’s tools push listings into many sites and leave the owner filling forms (or locked into a subscription that can take listings with it when they cancel), we file the corrections for them on the places that actually feed those AI answers — the big data companies that copy into directories, and the major sites people and machines trust (Google, Apple, Bing, Yelp, and similar) — while the owner keeps control of their truth. No “stop paying and your listings vanish” product. +Picture a bakery owner who keeps losing easy customers. People show up after closing because the hours online are wrong. Others call a disconnected number and never call back. A few say they asked an assistant for a nearby bakery and got pointed somewhere else — or got her shop with the old phone number. -That is a business because owners already pay for tools that chase Maps wins and completeness scores, customers are asking AI for local recommendations far more than a year ago, and almost nobody sells the missing piece: measure what AI says, fix the sources that make it wrong, then measure again. +She sits down on a Tuesday night to fix this. She updates Google — new phone, new weekend hours — and thinks customers will find the right info everywhere. They will not. The same details still sit wrong on Yelp, Bing, Apple Maps, and dozens of directories. When someone asks ChatGPT or Gemini “where’s a good bakery near me?”, the assistant often pulls from those scattered, outdated copies. Sometimes the shop never shows up. Sometimes it shows up with last year’s number. She never sees the miss. -### Pain +Fixing one listing is not the same as being findable and accurately described wherever people discover local businesses today — including when they ask AI. -Local owners and agencies are stuck between two scoreboards that do not agree. +--- -1. **Maps / pack pain is familiar and call-dense.** Practitioners report large call and pack swings from Google Business Profile (GBP) completeness, primary category, review velocity, and citation cleanup — but those wins are often Maps-lane attributions, not proof that ChatGPT / Perplexity / Gemini will name the shop correctly. -2. **AI pain is selective and accuracy-fragile.** Primary studies (SOCi LVI 2026, Insites Jul 2026, BrightLocal Local AI Visibility Study Sep 2026) show assistants recommend far fewer locations than Google’s short list of three at the top of many local searches (the “3-Pack”), disagree with each other, persist only ~half the time on re-prompt, and frequently misstate name, address, or phone (NAP) even when they “know” the business. -3. **Owners cannot see the gap.** Localeze Completeness, citation-count tools, and vibe-check prompt grids do not measure whether you are present on the surfaces assistants lean on, or whether an assistant would pick you *and* describe you accurately. Selling “cleanup ROI” as if aggregator correction alone moves AI answers lacks published pack/call studies. +### 2. Peel the onion — the root problem -Practitioner snapshot (r/localseo; full thread fetch often blocked — treat as **practitioner-reported**, not replicated): +The industry still sells last year’s scoreboard: polish Google, collect reviews, push listings, score how “complete” your profile looks inside their software, track map rank. That can bring more Google calls. Almost none of those products answer: what do AI assistants actually say about my business — do they find me, recommend me, and get the facts right? -| Claim (label) | Source thread (reddit) | Notes | -| --- | --- | --- | -| ~73% of shops invisible to 4+ of 6 AI engines (50-business observational audit) | [1slake5](https://www.reddit.com/r/localseo/comments/1slake5/) | **Practitioner-reported** observational audit; small n; methodology not independently reviewed | -| 15+ consistent listings claimed ~3× more likely to be recommended | same cluster / community commentary around 1slake5 | **Practitioner-reported**; correlation ≠ causation | -| GBP completeness 82→100% → calls 14→26/mo (~86%), visibility 40→70% in ~12 days | [1r23zji](https://www.reddit.com/r/localseo/comments/1r23zji/) | **Practitioner-reported**; community pushback: may be reclassification / seasonality, not pure completeness lift | -| Primary category swap: page 2 → top 3 in ~3 weeks | [1viqf8z](https://www.reddit.com/r/localseo/comments/1viqf8z/) | **Practitioner-reported** Maps/pack outcome | -| Delete ~40 bad citations: accuracy 74→98%, calls doubled ~6 weeks | [1v7s2ae](https://www.reddit.com/r/localseo/comments/1v7s2ae/) | **Practitioner-reported**; no controlled AI re-prompt evidence in thread | -| GBP opt ~70% calls ~2 months; AI mentions stayed flat (comment) | [1o08abp](https://www.reddit.com/r/localseo/comments/1o08abp/) | **Practitioner-reported**; useful as “Maps ≠ AI” anecdote | -| Reviews 11→34, page 2→pack, calls doubled ~6 weeks; attribution fight (~80% reviews?) | [1vn7vjv](https://www.reddit.com/r/localseo/comments/1vn7vjv/) | **Practitioner-reported**; heavy confounding | -| HVAC: review velocity as biggest lever | [1vtlpki](https://www.reddit.com/r/localseo/comments/1vtlpki/) | **Practitioner-reported** vertical anecdote | -| Citations-only → top 8–15, not top 3 | [1m4woes](https://www.reddit.com/r/localseo/comments/1m4woes/) | **Practitioner-reported**; citations help floor, not crown | -| Yext “data hostage” / listings vanish when stop paying; Synup cancel fee ~$1,980; Uberall enterprise-only | community threads (vendor pricing folklore) | **Practitioner-reported** / vendor-commercial; verify contracts case-by-case | -| DIY gets RESULT credit; paid pipes for scale past ~30 locations / 100+ | community consensus | Product implication: ownership & tickets > hostage syndication | -| AI evidence thin vs Maps call evidence dense; ListingsAPI: no win case study | community meta | Aligns with BrightLocal / Insites gaps below | +Peel further. Wrong hours and phone numbers do not mainly come from one bad Yelp page. A handful of **business-data warehouses** collect and copy your facts into directories, maps, navigation apps, and the pools assistants read from: -### What works (with evidence) +- [Data Axle](https://www.data-axle.com/marketing-solutions/local-listings-management/) (formerly Infogroup) +- [TransUnion Digital Business Profile / Localeze](https://neustarlocaleze.biz/) +- [Foursquare Places](https://foursquare.com/products/business-listings/) (absorbed Factual in 2020) +- Plus networks agencies buy as bundles: GPS Network, YP Network, Active Sync Plus ([BrightLocal’s aggregator list](https://www.brightlocal.com/learn/data-aggregators/)) -#### Maps / GBP / reviews (dense call evidence — mostly practitioner + expert surveys) +Vendors profit from the mess: paid “completeness” hubs, annual trusted-stamps, syndication subscriptions that degrade when you cancel. Updating Google or Yelp alone often fails because those warehouses keep shipping the old version downstream. Acxiom’s directory products retired end of 2019; Factual is not a separate claim surface anymore — don’t build around dead names. -| Lever | Evidence | Label | URL / cite | -| --- | --- | --- | --- | -| Primary GBP category, proximity, keywords in GBP title, address in city, open-now | Whitespark LSRF 2026 “pack kings” (survey posted ~Nov 6, 2025) | **Vendor / expert survey** (47 experts, 187 factors) | [whitespark.ca/local-search-ranking-factors](https://whitespark.ca/local-search-ranking-factors/) | -| GBP completeness / category / reviews → pack & calls | Multiple r/localseo case posts (table above) | **Practitioner-reported** | reddit threads linked above | -| Review volume & recency | Insites dual-surfaced avg **133.4** Google reviews vs **10.7** for neither; Whitespark: review recency underrated | **Primary study** + **expert survey** | [Insites benchmark](https://insites.com/blog/the-ai-visibility-benchmark-how-10000-local-businesses-actually-perform-in-chatgpt-and-perplexity/); Whitespark LSRF 2026 | -| Dedicated service pages, best-of lists, industry domains, unstructured citation *quality*, review-site authority | Whitespark LSRF 2026 AI-factor discussion | **Vendor / expert survey** | same LSRF page | -| DIY ownership of GBP + majors | Community: DIY gets RESULT credit | **Practitioner-reported** | r/localseo meta | +--- -#### AI visibility (thinner causal evidence; stronger descriptive studies) +### 3. Action plan — what we build -| Finding | Evidence | Label | URL | -| --- | --- | --- | --- | -| AIO ~68% vs local packs ~39% on Whitespark local-business query set (540 queries; May 12, 2025) | Whitespark / Miriam Ellis prevalence study | **Primary study** (vendor-affiliated) | [whitespark.ca case study](https://whitespark.ca/blog/case-study-the-prevalence-of-ai-overviews-in-local-search/) | -| Maps mention 66% vs AI surfaces 32–38%; ChatGPT ∩ Google AIs ~19–20%; persistence ~50%; top citations GBP 28.6%, Yelp 9.5%, Facebook 2.2% | BrightLocal Local AI Visibility Study, Sep 16, 2026 (~200k prompts) | **Primary study** (vendor) | [brightlocal.com/research/local-ai-visibility-study](https://www.brightlocal.com/research/local-ai-visibility-study/) | -| Consumer AI local recs 6%→45% YoY; 23% used AI in last local search; of AI users ~18% contact direct, ~75% check Google/Maps | BrightLocal LCRS 2026 / related AI-trust cuts | **Primary study** (survey) | [LCRS](https://www.brightlocal.com/research/local-consumer-review-survey/); [LCRS AI trust](https://www.brightlocal.com/research/lcrs-ai-trust/) | -| ChatGPT recommends **1.2%** of brand locations vs **35.9%** in 3-Pack (~30× selective); ~45% retail overlap traditional vs AI | SOCi LVI 2026 (~350k locations / 2,751 brands) | **Primary study** (vendor) | [soci.ai news](https://www.soci.ai/news/in-ai-driven-discovery-few-brands-are-chosen-most-disappear/); [LVI PDF](https://golocal.soci.ai/rs/355-TUF-572/images/SOCi%202026%20LVI.pdf) | -| GBP detail match ChatGPT ~55.58% / Perplexity ~48.05%; never recommended unbranded 40.88% / 68.79%; “corroboration, not optimisation”; schema/llms.txt barely correlate | Insites 10k US locals (Jul 2026) | **Primary study** (vendor platform) | [Insites benchmark](https://insites.com/blog/the-ai-visibility-benchmark-how-10000-local-businesses-actually-perform-in-chatgpt-and-perplexity/); [schema piece](https://insites.com/blog/schema-wont-save-you-what-10000-local-businesses-taught-us-about-ai-visibility/) | +**A. AI-assistant audit as the scoreboard.** +Ask the major assistants the questions a real customer would ask. Show the owner: did they find you, recommend you, get the facts right? That is why someone signs up — not a completeness score inside our software, not a map-rank tracker. -**Synthesis (not a causal claim):** +**B. Push verified facts into the root warehouses and the trusted sites — and track acceptance.** -- **GBP excellence remains mandatory** — proof hub, Maps call volume, and the strongest public corroboration signal assistants echo. -- **Consistent, high-quality presence on majors + umbrellas** supports corroboration (Insites framing) more than vanity citation spam. -- **Review velocity and volume** correlate with both pack and dual-AI surfacing; still confound-heavy for AI alone. -- **Measuring AI separately from Maps** is itself useful practice (BrightLocal Maps 66% vs AI 32–38%; SOCi selectivity). +| Warehouse / surface | What we do | +| --- | --- | +| Data Axle | Free claim/verify for roughly 1–10 locations; Premium / Submission API + teleresearch when scale or hard changes need it | +| Localeze / TransUnion | Paid manage when required (~$99/year per location); treat Completeness as “fill the hub so partners trust the row,” not an AI audit | +| Foursquare | Prefer **owner claim** (authoritative). Use Placemaker / partner APIs with **edit-status tracking** when we submit at scale — community votes alone are not owner authority | +| Google, Apple, Bing, Yelp | Still correct the big sites people and machines trust; Google is useful proof and where many customers double-check after an AI suggestion | +| BrightLocal-class reseller pipes | Optional scale lane — not the identity of the product | -### What doesn't / waste +We do not stop at “submitted a form.” We track whether the warehouse accepted the update. -| Anti-pattern | Why it fails / is waste | Label | -| --- | --- | --- | -| Recurring listing fees for ≤~10 locations | Whitespark commentary: prefer one-time / DIY at small scale | **Vendor / practitioner** | -| Mass low-DA citation blasts | Expert consensus: noise; citations-only stalls at ~top 8–15 | **Expert survey + practitioner** | -| Selling citation-cleanup-only as pack/AI ROI | **No clean published cleanup-only → +X% pack/calls study** (Whitespark: no clean citation-cleanup-only ROI study; BrightLocal: no published aggregator-correction → +X% pack/calls study) | Gap — do not sell | -| Yext-like ongoing pipe as the product | “Data hostage” / listings vanish when stop paying; Synup cancel-fee folklore (~$1,980); Uberall enterprise-only | **Practitioner-reported** commercial risk | -| Assuming Uberall breadth / older directory studies = cleanup ROI | Older breadth studies ≠ correction ROI | **Vendor historical** — not cleanup ROI | -| Treating Localeze Completeness Score as an AI audit | Completeness = hub fill-in (Primary / Discovery / Connect / Hours), not a web AI Presence/Recommendability check | Product fact (umbrella / Lane A) | -| Schema / llms.txt as the AI lever | Insites: barely correlate with recommendation | **Primary study** | -| Citations-only strategy for top 3 | Practitioner: top 8–15 ceiling without GBP/reviews/on-page | **Practitioner-reported** | -| ListingsAPI as proven win path | Community: no win case study found | **Practitioner-reported** / **UNVERIFIED** absence | -| Equating OpenAI↔Foursquare partnership with ranking lift | Partnership / Places adjacency ≠ proven ranking lever | Architecture caveat | -| Foursquare “60–70% of ChatGPT local” | Often phrased as “reports suggest” | **UNVERIFIED** — do not treat as fact | +**C. Respect sync clocks and trusted-freshness.** +When tools push through common pipes: Data Axle can move in real time; Foursquare often weekly (and one-way); Localeze often monthly. Localeze partners commonly publish in **30–90 days**. Trusted / managed stamps often last about **twelve months** — then freshness decays unless you renew or re-verify. We schedule for that, and we tell the owner the wait. -BrightLocal framing remains useful commercially: **Google first, LLMs second** — not because AI doesn’t matter (LCRS 6%→45%), but because Maps still mentions tracked businesses more often and most AI users still verify on Google/Maps. +**D. Re-ask AI after syncs; chase the upstream feed still poisoning copies.** +Same assistant questions again. Better findability and accuracy — or we did not earn the win. If one surface is still wrong, diagnose which warehouse is still feeding it and chase that source. -### AI visibility vs classic local SEO framing +**E. No cancel-hostage product.** +Owner keeps control of their truth. We are not a subscription that vanishes or corrupts listings when they leave (industry pattern documented when heavy syndication stops — see Whitespark’s Yext-cancel audit). -| Dimension | Classic local SEO (Maps / pack) | AI visibility (assistants / AIO) | -| --- | --- | --- | -| Unit of success | Rank in 3-Pack / Maps; calls from GBP | Named in answer; facts correct; re-prompt persistence | -| Selectivity | 3-Pack ~35.9% brand appearance (SOCi) | ChatGPT ~1.2% recommended (SOCi) — ~30× tighter | -| Overlap | Pack ranking helps but does not guarantee AI mention | BrightLocal: Maps 66% vs AI 32–38%; intersection ChatGPT∩Google AIs ~19–20% | -| Stability | Rankings move; calls are measurable | Persistence ~50% on re-prompt (BrightLocal) | -| Top “citations” / sources | GBP, site, reviews, citations | GBP still top among directories (~28.6%), but business sites dominate unique domains; Yelp/Facebook trail | -| Expert factor mix (Whitespark LSRF 2026) | Pack kings: primary category, proximity, GBP title keywords, in-city address, open-now | AI lean: best-of lists, service pages, industry domains, unstructured citation *quality*, review-site authority | -| Consumer behavior | Call / direction intent | LCRS: AI recs mainstream (45%); many still verify on Google/Maps | -| Accuracy failure mode | Wrong hours on GBP hurt conversion | Assistants misstate NAP ~half the time even when “aware” (Insites) | -| Product scoreboard | Rank + calls | Presence + Recommendability | +**Why this is a business.** Owners already pay for Maps tools and listing packs that ignore the warehouse layer. Customers ask AI for local recommendations far more than a year ago. Almost nobody both fixes the root data companies and proves it by re-checking the assistants. -Classic local SEO is necessary infrastructure and the densest call evidence; AI visibility is a **separate, more selective** surface that needs its own audit (mentioned vs recommended vs **misstated**). Do not collapse them into one vanity “AI SEO” score. +--- -### Gaps and caveats +### Supporting research (named sources) -1. **Causal AI ROI from cleanup is unpublished.** No BrightLocal / Whitespark / SOCi paper cleanly shows “aggregator correction alone → +X% AI mentions or pack.” Do not invent one. -2. **r/localseo numbers are observational.** Threads like 1slake5 (73% invisible…), 1r23zji (completeness→calls), etc. are **practitioner-reported**; n is small; confounders (season, category change, ads) are real. Community already flags reclassification risk on completeness wins. -3. **Vendor studies have incentives.** BrightLocal, Whitespark, SOCi, Insites sell adjacent products. Still useful as **primary study** descriptive baselines — cite methodology, don’t treat as gospel. -4. **Foursquare 60–70% of ChatGPT local = UNVERIFIED** when phrased as “reports suggest.” OpenAI partnership ≠ ranking lever. Managed Users timing remains an open question in the proposal. -5. **Localeze Lane A mechanics:** ~$99/year per location paywall; **no free suggest-edit**; Completeness Score = hub sections (Primary / Discovery / Connect / Hours), not a web AI audit; monthly partners often **30–90 days** — set owner expectations. -6. **Uberall / Yext / Synup commercial caveats** are practitioner folklore until contract-checked; still inform “don’t build a hostage pipe.” -7. **ListingsAPI:** no community win case study located — treat as non-proof, not proof of failure. -8. **Insites “corroboration, not optimisation”** undercuts schema/llms.txt-first GEO pitches; aligns with ticketed accuracy across real claim surfaces. -9. **SOCi sample is multi-location brands** (~350k locations). SMB single-shop dynamics may differ; still the clearest public selectivity ratio (~30×). -10. **BrightLocal citation mix** (GBP 28.6% / Yelp 9.5% / Facebook 2.2%) is study-specific; website citations dominate unique domains — own site still matters. +**Data Axle.** Free Local Listings for about 1–10 locations (claim, phone-verify, unlimited updates marketed free). Ten-plus: Premium / Excel / Submission API; human teleresearch on hard changes; files can take hours to ~2 weeks; failed listings can sit ~90 days. Partners include search, directories, nav, virtual assistants; marketing now cites AI-driven search. ([Data Axle Local Listings](https://www.data-axle.com/marketing-solutions/local-listings-management/); [Yext × Data Axle](https://www.yext.com/integrations/publishers/data-axle) — real-time.) -### Implications for this proposal +**Localeze / TransUnion.** ~$99/year per location (public SMB / BrightLocal writeups). 80–90+ distribution partners. Completeness Score = hub attribute fill-in for partner confidence — not a web/AI audit. Most partners process updates in **30–90 days**; Google sync can be minutes if linked. Cancel → they stop certifying owner-managed accuracy to partners. ([BrightLocal aggregators guide](https://www.brightlocal.com/learn/data-aggregators/); [Duda Localeze FAQ](https://support.duda.co/hc/en-us/articles/360060106534-Neustar-Localeze); [TransUnion LP](https://www.transunion.com/lp/digital-business-profiles).) Yext → Localeze cadence: **monthly**. -#### Build toward +**Foursquare Places.** Owner claim via Foursquare for Business. Community Placemaker suggest-edit needs votes before apply. Developer `suggest/edit` + status APIs (and Managed Users for higher-trust product edits). BrightLocal notes Places used by ChatGPT and powers apps like Uber / Nextdoor. Yext → Foursquare: **weekly**, one-way. ([Placemaker docs](https://docs.foursquare.com/data-products/docs/get-started-with-the-placemaker-tools); [suggest-edit API](https://docs.foursquare.com/fsq-developers-places/reference/place-suggest-edit); [BrightLocal](https://www.brightlocal.com/learn/data-aggregators/).) -1. **Accuracy-aware AI audit** — fixed buyer prompts; score Presence (found on claim surfaces / stomped copies) and Recommendability (named when it matters; facts match claimed truth). Prefer mentioned vs recommended vs **misstated** over vanity mention counts. -2. **One-click tickets for umbrellas (Lane A) and majors (Lane B)** — field / wrong / right / GBP-or-claimed-truth as proof. Umbrella-only leaves Core majors wrong; majors-only leaves Layer-1 feeds stomping the claim. -3. **GBP excellence still required** — densest call evidence; strongest corroboration signal; proof hub for tickets. Do not market AI audit as a GBP substitute. -4. **Don’t sell fake cleanup ROI** — no published aggregator-correction → +X% pack/calls or AI-mention study. Sell measurable re-prompt improvement and accurate tickets, not guaranteed pack jumps. -5. **Measure AI separately from Maps** — BrightLocal / SOCi / Insites all show divergence. Product dashboards must not blend pack rank into an “AI score.” -6. **DIY result credit ⇒ product posture** — make tickets and ownership easy; avoid Yext-like hostage pipes where Presence evaporates when the subscription stops. Paid pipes remain relevant for scale (~30+ / 100+ locations per practitioner consensus), as optional scale lanes — not the identity of the product. -7. **Localeze / Foursquare honesty** — charge/time expectations (Localeze paywall, 30–90 day partners); Foursquare Placemaker suggest-edit is real; OpenAI adjacency is not a ranking claim; Managed Users later. +**BrightLocal.** Reseller into Data Axle, Localeze/Neustar, Foursquare, GPS Network, YP Network, Active Sync Plus (~15 credits per aggregator submission; Active Sync Plus higher). Campaigns often finish in ~7 business days; marketing promise that most go live/updated within ~38 days or free extras. Aggregator “trusted” window ~12 months. Not a root warehouse — a paid pipe. -#### Success check +**Yext cancel pattern.** [Whitespark audit](https://whitespark.ca/blog/what-happens-when-you-cancel-yext/): large share of listings wrong or gone after cancel; Yext says it leaves data and does not “steal” listings — publishers still fall back to other feeds. Product implication: don’t build a hostage pipe. -Re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up,” not “we submitted 400 citations.” +**Retired.** Acxiom directory/local-search products EOY 2019. Factual → Foursquare April 2020. + +**AI vs Maps (why the scoreboard must be AI).** BrightLocal Local AI Visibility: Maps mention ~66% vs AI surfaces ~32–38%. SOCi LVI 2026: ChatGPT recommends ~1.2% of brand locations vs ~35.9% in Google’s short list of three. Insites: assistants often misstate name/address/phone even when they “know” the shop; schema/llms.txt barely correlate. No clean published study that aggregator cleanup alone → +X% AI mentions — do not invent one. Sell measurable re-ask improvement. + +**Practitioner snapshots (r/localseo — observational, not replicated):** ~73% shops invisible to 4+ of 6 AI engines in a 50-business audit ([1slake5](https://www.reddit.com/r/localseo/comments/1slake5/)); GBP completeness call lifts are Maps-lane attributions with confounders; “Maps opt, AI flat” anecdotes exist ([1o08abp](https://www.reddit.com/r/localseo/comments/1o08abp/)). + +--- + +### What we are not selling + +| Anti-pattern | Why | +| --- | --- | +| Completeness score as the product | Localeze hub fill-in ≠ what AI says | +| Citation blast / cleanup-only as AI ROI | No clean published cleanup → AI-mention study | +| Schema / llms.txt as the lever | Insites: barely correlates | +| Hostage syndication | Cancel degradation (Whitespark); trusted stamp expiry | +| Foursquare “60–70% of ChatGPT local” as fact | **UNVERIFIED** when phrased as “reports suggest” | +| Dead umbrellas (Acxiom, standalone Factual) | Retired / merged | + +--- + +### Open questions + +1. Promise: “Am I discoverable by AI?” vs “Does AI describe me correctly?” +2. Which assistants in v1? +3. How automated is accuracy vs owner compare-to-claim? +4. Localeze / Data Axle: pay inside product, refer, or use pipes the owner already has — without pretending free Localeze control exists? +5. Foursquare Managed Users in v1 or later? +6. Keep this file as GTM ladder beside the concept SoT, or merge later? + +--- + +### Capture + +2026-10-01 — restructured action-plan-first (pain → warehouse root → plan) after aggregator-layer research. Concept SoT unchanged.