Google Maps Has 72 Ranking Signals — Use Them Right

TL;DR: Google Maps rankings run through a multi-stage architecture — Geostore entity scoring, semantic matching, geographic retrieval, and on-device reranking — not a single algorithm. Researchers exposed 72 Oyster Rank signals, 793 data providers, and 446 local intent types. Operators in high-CAC verticals who treat local search as a checklist will leave qualified leads on the table.

The Listing Is an Interface — the Entity Is What Ranks

Most operators managing a Google Business Profile believe they are editing their Maps presence. They are not. They are submitting evidence to a much larger system called Geostore, which is Google’s internal representation of geographic objects. A business exists inside Geostore as a Feature — a structured object that contains identity, geometry, category data, Knowledge Graph references, and ranking information assembled from multiple sources.

What you edit in Google Business Profile becomes one data point among potentially hundreds. Your business name might draw from one provider, your phone number from another, your category assignment from a third. Geostore’s conflation engine then decides which value wins when those sources disagree. This is why edits sometimes revert, why incorrect attributes persist, and why duplicate information can survive months of manual corrections. The edit doesn’t overwrite the entity — it enters a queue competing against other evidence Google has already collected.

For operators running law firm local campaigns, iGaming affiliate funnels, or any business with a physical footprint, this distinction matters immediately. If Google’s canonical entity carries the wrong address or an outdated phone number sourced from a third-party directory, no amount of GBP editing will fix it without addressing the underlying data conflict.

Oyster Rank: 72 Signals, 25 Already Deprecated

Google’s internal entity-level ranking system is called Oyster Rank. Researchers recovered a full enumeration of 72 signals from a non-public Geostore binary. The list includes Google reviews, web query volume, listing impressions, listing opens, direction requests, website clicks, chain membership, Wikipedia signals, popularity, prominence, landmark data, and road usage.

Twenty-five of those 72 values are explicitly marked deprecated. The research team recovered signal names, not weights. Knowing that SIGNAL_GOOGLE_REVIEWS exists in the Oyster Rank vocabulary confirms reviews are part of the scoring schema. It does not tell you how much a 4.7-star rating with 300 reviews outperforms a 4.2 with 50. That coefficient data sits outside the recovered scope.

The more important takeaway is that Oyster Rank characterizes the importance of an entity inside Geostore — it is not the Maps ranking algorithm. After Oyster Rank scores the entity, a user query still passes through query understanding, semantic matching, candidate generation, geographic filtering, quality evaluation, and reranking before a result appears. A separate on-device scorer with eight signals across 13 tiers runs entirely offline, distinct from both Oyster Rank and server-side Places ranking. There is no single formula.

Operators who want a structured view of where their local presence stands should start with a full marketing audit that covers data consistency across providers, not just GBP completeness scores.

Geographic Retrieval Is Dynamic, Not Radius-Based

A widely used local SEO mental model assumes Google scans a fixed radius around the user and ranks whatever businesses fall inside it. That model is wrong. Testing conducted across thousands of queries showed that the geographic footprint shifts significantly based on query type and local density.

A dense query like “pharmacie” in Paris produced a small candidate area. The same query in a rural environment expanded dramatically. A brand query for a major chain pulled candidates from a much wider area than an unbranded category query. When researchers stripped geographic weighting entirely from the same engine across 5,083 calls and 86,584 results, median candidate distance moved from 6.87 km to more than 4,000 km. The non-geographic ordering remained highly stable, which means geography is not simply reordering a fixed list by proximity — it is changing which candidates enter retrieval in the first place.

For operators in iGaming acquisition or other verticals where a local office or licensed entity anchors campaigns, proximity is still a real factor. But “I’m closer, I should rank higher” is an incomplete strategy. Query semantics and entity prominence interact with geography before distance becomes a tiebreaker.

Web Pages Are Entity Evidence, Not Just Ranking Documents

One of the most consequential findings in the research is that Google’s web index and Maps are connected through a shared entity layer. Geostore Features can link to the Knowledge Graph through a machine ID (MID). Web documents can carry the same MIDs. A layer called webref associates web pages with entities and stores topicality, confidence, geographic metadata, and document-level scores.

This changes the function of a location page or store locator. Its job is not limited to ranking for “personal injury attorney Chicago.” The document is also evidence about the underlying entity — which place it describes, how much of the page is actually about that entity, how confident Google should be in the association, and whether the page qualifies as a useful reference for that entity.

Web SEO and local SEO are far less separate inside Google’s infrastructure than their interfaces suggest. A law firm with ten office pages that are thin, duplicated, or poorly structured is not just underperforming in organic search — it is providing weak entity evidence to the same system that determines Maps prominence. Operators running paid local campaigns alongside organic often discover that weak entity signals suppress both channels simultaneously.

What This Means for High-CAC Vertical Operators

Forex brokers, legal operators, iGaming platforms, and crypto exchanges all share one characteristic: a qualified local lead is worth several hundred to several thousand dollars. A Maps architecture this complex creates both a risk and an opportunity.

The risk is treating local SEO as a one-time setup task — claim the GBP, add photos, collect reviews, done. The Geostore architecture means that data from 793 providers is constantly being evaluated against your claimed attributes. If your NAP (name, address, phone) data is inconsistent across directories, data aggregators, and your own site, the conflation engine may deprioritize your edits in favor of older third-party data it trusts more highly.

The opportunity is in the semantic layer. Google uses GConcepts — a shared conceptual vocabulary — to connect queries to entities through categories, attributes, service modes, cuisines, and other descriptors. A query for “ramen” in the research pulled results across ramen restaurants, Japanese restaurants, Asian restaurants, and related concepts. Operators who structure their entity data, location pages, and review content around the full semantic neighborhood of their service — not just their primary GBP category — can expand their candidate footprint without changing their physical location.

For crypto operator acquisition or forex brokers building local credibility in regulated markets, this semantic depth in entity representation is an underused lever. Structured data, entity-consistent content, and citation cleanup across authoritative providers all feed the same system.

Operators running geo-targeted paid campaigns alongside local organic should also audit whether their landing pages reinforce or dilute entity signals. A paid click landing on a page with no address schema, inconsistent brand references, or thin location content is burning budget against weak entity foundations.

Finally, the on-device scorer is a signal that Maps is becoming a more local, more conversational product. Operators who build strong entity foundations now — consistent data, semantically rich location pages, review profiles with topical depth — are positioning for a Maps environment that will reward entity prominence more explicitly as conversational search matures. Running AI-assisted lead qualification at the point of Maps-driven inbound can capture that intent at the moment conversion is most likely.

Originally reported by Search Engine Land, September 2026.