# Alternate business proposal: North Star via accurate AI presence

**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.

---

## North Star (what every section serves)

From [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md): **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 so every section ladders to BUSINESS_PLAN North Star intent + concept Presence/Recommendability. Evidence from Localeze research and r/localseo; statistics are practitioner-reported, not independently replicated.

**Research brief appended 2026-10-01; North Star preamble added same day** — verbose evidence synthesis below (section RESEARCH BRIEF). PDF export: [`docs/research-brief.pdf`](./research-brief.pdf). Does not replace North Star / Problem / Solution / Differentiators / Open questions; amends Capture with this date stamp only.

---

## RESEARCH BRIEF

**Title:** GEOLOCAL Research Brief — evidence behind North Star Presence + Recommendability  
**Date:** 2026-10-01  
**Branch:** `planning-selfserve-audit`  
**Relationship:** Append-only 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** (r/localseo / agency anecdotes), **vendor** (product marketing or vendor research), **primary study** (named methodology / published research). Flag **UNVERIFIED** where community claim or secondary paraphrase lacks a clean primary cite.

### North Star (how to read this brief)

The North Star for GEOLOCAL is **agent-era local SMB discoverability**: local businesses **discovered and recommended by AI assistants**. That intent (from [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md)) is scored on this branch as two outcomes:

- **Presence** — being present and accurately represented wherever a local business is discovered (majors, umbrellas, directories, and the copies assistants lean on).
- **Recommendability** — would an AI pick you for a real buyer ask, and would it **get you right** (not just mention you)?

Everything below is evidence gathered **in service of that North Star** — what practitioners and vendors say moves Maps/calls vs what actually bears on AI Presence and Recommendability, what is waste, and where the proof is thin. It is not a general local-SEO encyclopedia; read every finding against “does this help an owner become discoverable and accurately recommended by agents?”

### Pain

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 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 the 3-Pack, disagree with each other, persist only ~half the time on re-prompt, and frequently misstate 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 Presence (are you on the claim surfaces assistants lean on?) or Recommendability (would an assistant pick you *and* describe you accurately?). Selling “cleanup ROI” as if aggregator correction alone moves AI answers lacks published pack/call studies.

Practitioner snapshot (r/localseo; full thread fetch often blocked — treat as **practitioner-reported**, not replicated):

| 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 |

**Pain for this proposal:** the North Star fails when (a) the shop is missing from AI answers, (b) the shop is named but facts are wrong, or (c) the owner has no accuracy-aware audit + ticket path across Lane A umbrellas and Lane B majors.

### What works (with evidence)

#### Maps / GBP / reviews (dense call evidence — mostly practitioner + expert surveys)

| 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)

| 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/) |

**What “works” for North Star Presence + Recommendability (synthesis, not a causal claim):**

- **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 a “what works” practice (BrightLocal Maps 66% vs AI 32–38%; SOCi selectivity).

### What doesn't / waste

| 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 web AI Presence/Recommendability | Product fact (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 |

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.

### AI visibility vs classic local SEO framing

| 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** (this proposal’s North Star) |

**Framing rule for GEOLOCAL:** 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

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.

### Implications for this proposal (North Star Presence + Recommendability; Lane A/B)

Architecture already in this file / concept SoT:

- **Two-lane:** Lane A umbrellas (Data Axle, Localeze, Foursquare) + Lane B majors (GBP, Apple Business Connect, Bing Places, Yelp).
- **North Star:** Presence + Recommendability; [`BUSINESS_PLAN.md`](./BUSINESS_PLAN.md) intent = discover + recommend by AI.

**Build toward:**

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 Lane A and 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.
8. **Evidence hygiene in GTM** — label practitioner vs primary study; flag UNVERIFIED (e.g. Foursquare 60–70%); keep this brief as the evidence appendix so the commercial ladder stays North-Star-tight.

**Success check (unchanged intent):** re-prompt the same asks; Presence and Recommendability improve (found, recommended, facts correct) — not “Localeze score went up,” not “we submitted 400 citations.”