Google 지도 신호 누출로 인해 비즈니스 프로필 필드를 넘어 로컬 SEO 초점이 이동되었습니다.

Newly recovered data examined by Search Engine Land offers an unusually detailed view of the systems that may sit behind Google Maps and local search. Its central lesson for local businesses is clear: a Google Business Profile matters, but it may be only one visible input into a much broader representation of a place, its identity, its attributes, and the evidence connected to it across Google's systems.

The analysis is not an official Google disclosure, and it does not reveal the weights that determine Maps rankings. Still, Search Engine Land's examination of the recovered Maps data provides a plausible framework for rethinking local SEO. Rather than treating profile fields as a self-contained checklist, businesses should make it easier for Google to understand and corroborate the underlying local entity.

What the recovered Maps data reveals

Researchers obtained a binary exposing a non-public Geostore scope, then cross-referenced it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and material from Google's 2024 leak. The resulting analysis identifies 72 Geostore ranking signals, 793 data source providers, and 446 local search intent types.

Those numbers should not be read as a list of 72 tactics or a confirmed Maps ranking formula. The signals belong to a system called Oyster Rank, described as an internal ranking vocabulary for Geostore. The research places that vocabulary within a larger pipeline that also involves query understanding, semantic matching, candidate generation, geography and quality assessment, and reranking. Twenty-five of the 72 signals are marked deprecated, further underlining why a simple ranking-factor checklist would be misleading.

The recovered material also points to a substantial technical layer behind the map interface: 50,998 Mapcore styles, 12,936 label styles, and 10,936 searchable Geostore declarations. There is also a separate on-device scorer with eight signals across 13 tiers. These findings illustrate the scale and complexity of the architecture, not a recipe for manipulating results.

At the heart of the analysis is the idea that Google represents a local listing as a geographic Feature, rather than merely as a collection of profile fields. That Feature can include identity, geometry, source information, websites, brand relationships, Knowledge Graph references, concepts, and ranking information. Google can connect the entity to external references through the Knowledge Graph and a webref layer that links documents to entities.

Local SEO view What the recovered analysis suggests Business Profile as the primary local asset The listing is a visible surface for a deeper geographic Feature. Fixed ranking-factor checklist Oyster Rank appears to be a signal vocabulary within a wider ranking pipeline, with no public weights. Single-source optimization Geostore can incorporate source information, websites, Knowledge Graph references, and brand relationships. One universal local query model The analysis identifies [local search intent types](https://scalevise.com/resources/2026-seo-survey-search-intent-backlinks/), suggesting query intent is a significant part of the system.

Why entity consistency becomes more important

For a business, the practical implication is not to abandon Google Business Profile optimization. Accurate core information remains essential because it helps establish a clear representation of the place. The more important shift is to view that profile as one part of a wider evidence base.

A useful local SEO program should therefore prioritize consistency and completeness wherever the business is represented. The goal is to reduce ambiguity around the entity Google is trying to identify and match to a local query. Based on the framework in the recovered analysis, this means paying close attention to:

• Consistent identity details, including the business name, address, phone number, website, and relevant attributes.
• Clear website evidence that explains what the business is, where it operates, and which services or products are actually associated with that location.
• Accurate relationships and references, such as brand associations and external entity references that support the same understanding of the business.
• Semantic completeness, so important details are not implied only through marketing language but are stated clearly in pages Google can connect to the entity.

This is not proof that any single change will improve a Maps position. The recovered data does not provide coefficients, weights, or a direct causal ranking model. It does, however, support a more durable approach than chasing isolated profile edits without checking whether a company's website, local information, and broader entity signals tell the same story.

What this could mean for local search strategy

The research suggests that local search is likely less about filling in fields and more about validity and breadth of evidence. A business with a polished profile but inconsistent or thin supporting information may be harder for a complex entity system to interpret than one whose identity and local relevance are consistently documented across connected sources.

That matters as Maps develops toward AI-assisted conversational experiences. In that environment, systems need to reason about entities and their relationships, not simply retrieve a matching category. Businesses should make sure their public information can answer basic questions unambiguously: what they offer, where they operate, which location provides which service, and how their website and brand references relate to the place shown in Maps.

Local teams can use this finding as an audit prompt, not as a promise of ranking gains. Review the Business Profile alongside the location pages, service descriptions, contact details, and references that support the business's identity. Correct contradictions first. Then improve missing information that would make the location easier to understand for a search engine and a prospective customer.

For businesses trying to stay discoverable as Google and other AI-driven interfaces change how people find local services, Scalevise can help turn scattered online information into a clearer visibility strategy. An AI Visibility and GEO assessment can identify where your brand information is inconsistent, unclear, or absent across the signals that shape AI-assisted discovery. Start an AI Visibility scan to find the highest-priority gaps in your local presence.

Limits of the current evidence

The recovered data is valuable, but its boundaries matter. Search Engine Land characterizes it as reverse-engineered analysis, not an official Google release. Google has not publicly confirmed the specific signal count, provider count, or the detailed architecture described in the report.

Most importantly, the data does not show how much weight any signal receives, how signals interact, or which factors are decisive for a particular query, market, device, or user. It also does not establish that every listed signal remains active, especially because 25 are marked deprecated. The best reading is that this is a map of internal concepts and possible evaluation stages, not a guaranteed guide to Maps rankings.

Frequently Asked Questions

What are the 72 Google Maps ranking signals?

The recovered analysis identifies 72 Geostore signals associated with a system called Oyster Rank. It does not show their weights or prove they are a complete Google Maps ranking algorithm.

Does this mean Google Business Profile optimization no longer matters?

No. The analysis suggests a Business Profile is one visible part of a deeper geographic entity. Accurate profile information remains important, alongside consistent evidence on websites and connected references.

What is Geostore in the recovered analysis?

Geostore is described as a non-public scope that represents local entities as geographic Features. Those Features can include identity, geometry, sources, websites, brand relationships, Knowledge Graph references, concepts, and ranking information.

Can businesses use these findings to guarantee better Maps rankings?

No. The recovered material does not provide signal weights or a fixed ranking formula. It is better used to guide entity consistency, accurate information, and complete local evidence.

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Conclusion

The recovered Maps data reinforces a practical local SEO principle: optimize the business Google needs to understand, not just the profile customers see. While the specific ranking mechanics remain unknown and unconfirmed by Google, consistent identity, clear location information, and well-supported entity evidence are more resilient priorities than a narrow checklist of profile edits.

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