Local search ranking factors

Local search used to mean Google, and Google is still where most of it happens. But in 2026 customers also find local businesses through ChatGPT, Apple Maps, Bing and Copilot, Perplexity and Claude, and each of them picks businesses in its own way. This guide starts with Google's local algorithm: how it turns a search into results, the signals it ranks on, and the named updates from Pigeon to the August 2026 spam update. It then takes each of the other platforms in turn, keeping what each company documents apart from what we can only infer.

Google's local ranking model: three official factors, and a fourth we see

The Business Profile help centre lists three inputs the local algorithm uses to rank results: relevance, distance, and prominence.¹ That framing is real and load-bearing, and we will treat each one in depth below. What it understates, especially in 2026, is the role of a fourth class of signals Google reads continuously but does not name in the public help: behavioural data from the searches and interactions users have with listings.

What changed in local search, 2024 to 2026

If you were last seriously across local search in 2022 or 2023, the most important shifts to take in are these:

• Google's AI answers now sit beside the Map Pack. AI Overviews, AI Mode, Ask Maps and the Gemini app can all name local businesses, and Gemini uses public information from Google Maps to find places.⁴ How these features choose which businesses to name is not published, but they work from the same Maps and Business Profile data as the Map Pack, so a strong profile helps in both.
• Google matches meaning, not only words. Systems such as BERT and neural matching match the concepts in a search rather than its exact words.⁵ Google has also published research on multi-vector retrieval (MUVERA), but has not said whether it is used for local results. The effect is visible either way: a profile can match searches that never use its exact words.
• Query fan-out splits searches in AI answers. AI Overviews and AI Mode may use a "query fan-out" technique, issuing multiple related searches across subtopics and data sources.⁶ A query like "best italian restaurant for date night" can become several searches (italian restaurant near me, romantic restaurant, dinner). Google has not said the Map Pack works this way, but a listing that matches more of those sub-queries has more chances to be picked up in the AI answer.
• Schema.org keeps adding types that matter for local.⁷ v30.0 (March 2026) added the Credential type and equivalence annotations to GS1, Dublin Core, and Open Graph; v29.4 (December 2025) added OnlineMarketplace and ConferenceEvent. Together they give Google more direct typing for regulated professions and specialised commerce.
• Local search now happens outside Google too. ChatGPT added opt-in location sharing for local recommendations in March 2026,⁸ and Yelp licensed its reviews and listings to OpenAI in July.⁹ Apple is bringing ads to Maps search results in the US and Canada,¹⁰ and Copilot answers from Bing. Each platform has its own section further down this guide.
• Photo quality appears to beat photo count. Google does not document how it assesses profile photos, but a listing with 12 high-quality recent photos tends to do better than one with 60 mixed-quality photos.
• Behavioural signal weight has grown materially. Compared with a few years ago, behavioural signals appear to carry materially more relative weight. This is the single biggest practical shift in the algorithm.

Google's search ecosystem: how a local search becomes results

Google shows local businesses in more places than the Map Pack: Google Search, Google Maps, AI Overviews and AI Mode, Ask Maps and the Gemini app. Each of them has to do roughly the same seven things, in less time than it takes to blink, although Google does not publish whether they share one pipeline. The chips on each stage show which products it applies to, the badge shows how much of it Google documents, and the technical name is there for anyone who wants to dig deeper:

1. 1

   1Google works out what you asked for

   Applies toGoogle SearchGoogle MapsAI Overviews and AI ModeAsk Maps and Gemini

   Google breaks the request into its parts and works out what each part means. "best italian rest. near me open now" becomes, roughly, four instructions: what kind of business (an Italian restaurant), how good (the best), when (open right now) and where (close to you). In Search and Maps people type a few words. In AI Mode, Ask Maps and Gemini they write whole sentences and follow-ups, and AI Overviews and AI Mode may split one request into several related searches.⁶ Search uses systems such as BERT and neural matching for this kind of work.⁵

   DocumentedTechnical nameQuery understanding

   
2. 2

   2Google decides whether this is a local search

   Applies toGoogle SearchAI Overviews and AI Mode

   On Google Search, not every search needs a map. "Italian recipes" is someone cooking at home, so no businesses appear. "Italian food" usually means someone wants to eat out, so a map of nearby places does. In Google Maps the question never arises, because every search is about places. In AI Overviews and AI Mode, the same call decides whether the answer names businesses at all. Google does not publish how it makes it.

   Partly documentedTechnical nameLocal intent detection

   
3. 3

   3Google picks the spot to search around

   Applies toGoogle SearchGoogle MapsAI Overviews and AI ModeAsk Maps and Gemini

   "Near me" needs a centre point. Google can use your device's location if you allow it, your IP address (a rough area), Home and Work addresses you have saved, and places you have searched from before.¹¹ On a phone sharing its location, that is usually where you are standing. On a laptop it is often rougher, unless the browser shares your location. In Google Maps, the part of the map on screen matters too: move the map and the search area moves with it. If you name a place ("italian restaurant in Toronto"), Google centres the search there instead.

   DocumentedTechnical nameGeo-anchor selection

   
4. 4

   4Google gathers a shortlist

   Applies toGoogle SearchGoogle MapsAI Overviews and AI ModeAsk Maps and Gemini

   Before ranking anyone, Google gathers the businesses that could fit. How it does so is not published. Relevance is defined as how well a Business Profile matches the search, and Google's systems match meaning as well as words, so a "trattoria" can match "Italian restaurant". On our reading, in Search and Maps the shortlist is mostly decided by your categories, what your profile says you offer, and whether you are within reach of the searcher. The AI features can also draw on web pages and reviews found by their extra searches, so what others write about you counts there too. A business that misses the shortlist cannot appear, however good its reviews.

   Our readingTechnical nameCandidate retrieval

   
5. 5

   5Google puts the shortlist in order

   Applies toGoogle SearchGoogle MapsAI Overviews and AI Mode

   For local results in Search and Maps, three documented factors carry the weight: relevance (how well you match), distance (how far away you are) and prominence (how well known you are, including your reviews and links from other websites).¹ Many practitioners, us included, think how people respond to a listing counts too, but Google has not confirmed that. The balance shifts with the search. For "coffee near me", being close matters most. For "children's dentist for nervous patients", the right specialism counts for more than a few extra streets. AI answers do not rank a list: they name a few businesses, and Google does not publish how it picks them.

   Partly documentedTechnical nameRanking (relevance, distance, prominence)

   
6. 6

   6Google tidies the list

   Applies toGoogle SearchGoogle Maps

   Next, Google cleans up the list. It removes listings that break its rules for representing a business, such as fake locations.¹² When very similar businesses sit at the same address or close together, often only one shows. Practitioners call this the Possum filter, a name Google has never used or confirmed, and it is a common reason a real business seems to vanish. Results can also vary with the person searching, mostly because of their exact location and device, and sometimes their own search history.

   Partly documentedTechnical nameFiltering and de-duplication

   
7. 7

   7Google chooses where to show it

   Applies toGoogle SearchGoogle MapsAI Overviews and AI ModeAsk Maps and Gemini

   Finally, the result is shown wherever the searcher is: the Local Pack on a search page, the full list behind it, the Google Maps app, an AI Overview or AI Mode answer, Ask Maps, or the Gemini app. The order is often similar across these but rarely identical, which you can see by checking the same search in each. The section on where Google shows local results compares all seven.

   Partly documentedTechnical nameSurface selection

   

Google's local ranking factors, in rough order of impact

Google publishes its three factors but not how much any single signal counts. What follows is our reading of the rough order for Map Pack ranking, drawn from Google's documentation and the ranking changes we see across the businesses we work with. Treat it as a guide to where effort usually pays back first, not as measured weights:

Relevance on Google, in depth

Relevance is how the algorithm decides whether your listing should be a candidate for a specific query at all, and Google's category guidance is where candidate-set membership starts.¹³ The signal stack inside relevance, in approximate descending order of impact:

Distance on Google, in depth

Distance is the most-misunderstood pillar because the phrase implies a simple straight-line measurement. In practice, distance is a relevance-weighted radius modified by user signals and query intent. Four mechanics are worth understanding:

Prominence on Google, in depth

Prominence is how well-known your business is to Google and to its users. It is the slowest pillar to move and the one with the highest ceiling. The signal stack:

Behavioural signals on Google: the unconfirmed fourth factor

Behavioural signals are the part of the local algorithm that has grown the most in the past three years. Google does not name them in its public documentation, but the patterns are visible in any decent before-and-after testing across listings with otherwise-identical signal stacks. The behavioural signals the algorithm reads:

These signals compound with the other three pillars. A high-CTR listing for a category-relevant query gets a relevance bump above what its raw signal stack would predict. A high-direction-request listing gets a prominence bump. The compounding is the reason listings that "shouldn't" rank sometimes do, and listings that "should" rank sometimes don't.

Behavioural signals have become materially more influential since 2022. Time and again, a listing with strong engagement signals outranks a listing with stronger raw signals but weak engagement. It is one of the clearest patterns we see, and one of the most under-optimised.

The filters Google applies

After candidates are scored, several filters operate on the ranked list before it is shown to the user. These filters are the cause of most "we should be ranking but we aren't" diagnoses we run for agencies:

• Possum filter (September 2016, modified by Hawk in 2017). De-duplicates listings sharing the same address or very similar names and categories within close proximity. After Hawk, the filter only triggers when the listings are very close together; mid-distance siblings are no longer always filtered. The most common cause of unexpected ranking absence in multi-location businesses with multiple locations in the same building or business park.
• Vicinity filter (December 2021). Reduced ability of distant businesses with keyword-stuffed names to rank for proximity queries. The filter applies a sharp distance falloff past a category-dependent radius.
• Spam filter (continuous). Fake businesses, obvious lead-generation listings, and listings flagged through the report-a-problem flow. Suspension is an upstream binary, not a filter.
• Quality filter (continuous). Incomplete or stale listings are demoted within candidate sets. Listings with no photos, no description, no services listed, and no recent posts can rank, but they will lose head-to-head to a more complete peer with similar raw signals.
• Personalisation re-ranking (continuous). Search history, past clicks for similar queries, and preferred businesses re-order results for the individual searcher. This is why two people standing next to each other can see different Map Packs for the same query.

Where Google shows local results

Google shows local businesses in seven main places. Each looks different and picks or orders businesses in its own way, so it helps to know what each one is and what moves you in it:

Google's AI answers: what is documented

Four of the seven places above are AI features: AI Overviews, AI Mode, Ask Maps and the Gemini app. They pick businesses differently from the Map Pack. Where the Map Pack shows a ranked list, an AI answer names a few businesses in prose, often for a more specific question. This is what is documented about them, and what is left out:

Google's local algorithm history: the named updates that matter

Local algorithm changes happen continuously, but a handful of named updates produced step-change effects that still shape how the system behaves today. The core and spam update names are Google's own and appear on its ranking updates record.¹⁵ Possum, Hawk, Bedlam and Vicinity are community names: Google acknowledged the changes behind Bedlam and Vicinity, but never Possum or Hawk.

Local search beyond Google

Google, across Search, Maps and its AI features, is still where most local searches start. But it is no longer the only place a customer finds a business. People ask ChatGPT for a plumber, search Apple Maps on an iPhone without choosing it, and get answers from Copilot that come from Bing. These platforms do not share Google's algorithm, and most publish far less about how they rank. Here is how they compare, with a section on each below:

ChatGPT

ChatGPT answers local requests like "find me an emergency plumber in Denver" with a short list of businesses, often on a map. It has no business listing of its own for you to claim, which changes where the work goes: into the sources it reads about you.

Apple Maps

Apple Maps is the default map on every iPhone, so it answers many "near me" searches without the searcher ever choosing it. Apple publishes almost nothing about how it orders results, but it does document the tools a business controls.

Bing, Bing Maps and Copilot

Bing matters twice over: for people who search it directly, and because Microsoft Copilot answers from Bing's results.

Perplexity

Perplexity is a search engine that answers in prose, with a numbered source on each claim. It runs its own index rather than borrowing Google's or Bing's, and for local questions it brings in licensed review and listings data. There is no Perplexity business listing to claim, so the work goes into the sources it reads.

Claude

Claude, Anthropic's AI assistant, answers local questions from web searches it runs itself, and in its apps it can show suggested places on a map. It is the one platform outside Google that documents using Google Maps, which makes your Google Business Profile matter here too.

For a deeper look at how AI assistants choose which businesses to name, see our guide to AI search for local business.

Common myths and what is actually true

How to test and instrument

The local algorithm responds to changes on timescales ranging from hours (some behavioural and proximity changes) to weeks (review velocity changes, content updates) to months (link and citation changes, brand-mention compounding). Reliable instrumentation is the difference between knowing what is working and guessing:

• Geo-grid rank tracking. Measure rank for your target queries at multiple geo-points around your service area, not just a single point. Rankings vary continuously across metres of geography; a single-point average is misleading. Our Geo-Grid Rank Tracking feature is purpose-built for this.
• Mobile and desktop separately. Proximity weighting differs between mobile (GPS-precise) and desktop (IP-coarse). Track both for any geography you care about.
• Before-and-after testing on single levers. Change one thing at a time, wait for the algorithm to absorb the change (a week is usually enough for GBP-field changes), then measure delta. Multi-lever changes are diagnostically useless.
• Behavioural metrics from GBP Performance. Pull the Performance API into a warehouse and watch impressions, calls, direction requests, and website clicks over time. The API has no click-through metric, so work out actions per impression yourself. These leading indicators predict ranking changes before rank tracking catches them.
• Cross-platform monitoring. Map Pack rank is one number. Track Google's surfaces separately (Map Pack, the full list, Maps, AI Overviews, AI Mode), then check the other platforms your customers use. For ChatGPT and other AI assistants, measure how often you are named across a set of prompts rather than a position: their answers change from one run to the next.

The audit checklist

• Primary GBP category is the narrowest accurate option Google offers; review against the full searchable list
• Three to six secondary categories that each genuinely describe additional work you do
• Services list populated with 10 to 30 entries, each with a brief description
• Every applicable attribute ticked; reviewed at least quarterly
• 20+ recent photos across exterior, interior, team, products, and work-in-progress
• Description uses the full 750 characters, leads with what you do and who you serve
• Every review from the last quarter has a reply within 48 hours of being posted
• The top 10 questions a customer would ask are answered somewhere Google can read: services, attributes, or an FAQ page on the site
• Posts published within the last four weeks
• Special hours scheduled for upcoming public holidays and closures
• Website has LocalBusiness schema markup with consistent NAP
• sameAs links from authoritative identifiers (Companies House, regulator IDs, Wikidata if applicable)
• Citation footprint consistent across the high-trust local directories for your country
• Backlinks from at least a handful of local publications, partners, or industry bodies
• Geo-grid rank tracking running for your top three commercial queries
• GBP Performance API or Insights reviewed monthly for behavioural-signal trend changes
• Mobile and desktop ranking tracked separately for the same queries
• AI Overview and AI Mode visibility checked for your top three queries each month
• Apple Business place card claimed, with hours, photos and categories matching your Google profile
• Bing Places listing claimed and matching your Google profile
• robots.txt allows the search crawlers of the platforms you want to appear in: Googlebot, Bingbot, OAI-SearchBot, PerplexityBot and Claude-SearchBot
• Listings complete and consistent on the review sites used in your country and sector (Yelp matters most where it is widely used, as in the US and Canada)
• How often ChatGPT names you checked monthly across a set of realistic local prompts

Where local search is heading

Looking at the trajectory of Google's past four updates and the other platforms' launches this year, the practical expectations for the rest of 2026 and into 2027 are:

• Continued weight shift toward behavioural signals. Every update since 2022 has nudged behavioural weight higher. Expect this to continue.
• Tighter entity-record requirements. Listings without clean schema, sameAs convergence, and consistent NAP will increasingly underperform listings with the same raw GBP signals but cleaner entity records.
• AI surfaces becoming a larger share of impression counts. AI Overviews and AI Mode are expanding their query coverage; the Map Pack remains the dominant surface but its share of total local impressions has declined.
• Reviews continuing to gain weight, but conditioned on authenticity. Review spam detection has tightened materially in 2024 and 2025. Volume and velocity matter, but only if the reviews look organic.
• More local discovery outside Google. Location sharing in ChatGPT, licensed review data, and ads and suggestions in Apple Maps all point the same way: more platforms competing to answer "near me". Each one reads its own sources, so a business kept accurate only on Google will be described less well elsewhere.
• Lower returns from citation-volume tactics. Building citations on long-tail directories has been losing weight for years. The trend is continuing.

Where to go next