Deduplicating Google Maps Store Data: Place ID Is Not a Permanent Business Identity

Deduplicating Google Maps store data requires more than checking whether store names match or assuming a single entity always keeps one Place ID. Google clarifies that the same location can have different IDs, and IDs can change over time. For long-term store lists, a safer approach is to maintain your own internal business entity identifiers and manage their mappings to external location identifiers.

This issue appeared in a Stack Overflow https://stackoverflow.com/questions/29751398/geocoding-google-maps-api-return-different-place-id: Sergey Kuznetsov queried with one location ID and received a different response ID. While older answers help identify the issue, they do not establish that old IDs remain permanently valid. This article is based on official documentation as of 2026.

This discussion focuses on entity recognition and data quality, not bulk store scraping or a claim about EveryInfra automated merge features. The recording methods described here are primarily intended for proprietary store records and data you are authorized to process.

Place IDs Change: Why They Are Still Worth Retaining

The Google Place ID documentation states that IDs identify locations, but the same location may have multiple IDs and IDs can change. The documentation recommends refreshing IDs saved for more than 12 months and explains that outdated IDs may return NOT_FOUND.

This does not mean Place IDs lack value. Instead, they serve as better external object identifiers than generic names like "coffee shop" or "central branch." The key is preventing external identifiers from carrying all business meanings directly.

For example, if a chain store moves from a street corner to the adjacent mall, the business may still consider it the same operating branch, but its location has changed. Conversely, two branches with identical brand names should not be merged if their businesses and locations differ. Define whether your table records a "brand," an "operating branch," or a "physical location" before setting deduplication rules.

Combining these three layers into a single store_id saves time initially, but creates errors in store counts, review attribution, and historical trends over time. An external ID change might be misrecorded as a new store opening, and name normalization could collapse two distinct branches into one row.

Establish a Master Business Record First, Then Maintain External Identifier Mappings

For proprietary store data, assign internal identifiers to business objects that do not depend on third-party platforms. Store external platforms, location IDs, confirmation timestamps, and validity ranges in mapping records. Maintain description fields like names and addresses in compliance with the permitted usage and storage scopes of their sources.

This does not require building a massive master data system. Small teams can start with two lists: one tracking the stores you manage, and another tracking the current external identifiers for each store. When an ID changes, update the mapping relationship rather than rebuilding all business history.

The key is retaining the reason for the change. Manual confirmation that a new ID belongs to the same location differs from official relocation links. Both may require linking old and new records, but systems should not treat them as simple "duplicate data deleted" events.

Mappings should also be reversible. If two stores are merged incorrectly, you should be able to trace the reasoning, restore ownership, and recalculate affected rollups rather than guessing from an overwritten table. This preserves permitted identifiers and operation logs without requiring copies of all platform text.

Store Names and Proximity Serve Only as Candidates, Not Final Merge Criteria

Consider this hypothetical example, which does not correspond to any real stores: A brand operates two locations on the first and fourth floors of the same shopping center—sharing the same brand name, primary address, and close coordinates. Automatically merging them based on matching names and close proximity results in an undercounted operating location and misattributed reviews.

Another example involves English and local-language names for the same store. Different names do not automatically mean two separate stores; judgments should incorporate source location identifiers, proprietary store data, and other verified sources. Cross-language name matching helps identify candidates but should not dictate final conclusions.

We recommend dividing decisions into three tiers: confirm mappings when evidence is robust, explicitly reject merges when evidence conflicts, and route items to review queues when names or locations are merely similar. An unknown state is not system failure; it is a necessary step to prevent questionable matches from polluting data permanently.

Candidate rules should adapt to contexts. Airport counters, shopping malls, and streetfront stores have different spatial relationships, and no single distance threshold applies to all cities and formats. If thresholds rely on team experience, document them as internal rules rather than attributing them to Google recommendations.

ID Expiration, Permanent Closures, and Relocations Are Distinct Events

NOT_FOUND alone does not prove a store has closed. Google's ID documentation lists scenarios such as outdated IDs and database updates, so check whether identifiers need refreshing before verifying business status. Query failures should not be directly counted as monthly store closures.

Place Details (New) provides businessStatus and explains that applicable relocation results point to new locations via movedPlace and movedPlaceId. Verify request fields, permissions, and billing scopes before implementation; these are Google fields and do not guarantee availability across other data APIs.

When relocations occur, business reports must resolve at least two things: whether operating branches retain original IDs, and how data from old and new addresses should be segmented. When comparing reviews before and after a relocation, specify which location object provided the reviews, without assuming all content from old locations migrated automatically.

Even when a business remains the same, its district, service team, or operating format may change. Linking pre- and post-relocation data into an unmarked trendline masks these shifts. Better presentations preserve event annotations so readers understand what changed before determining which metrics remain comparable.

Retaining Place IDs Does Not Mean All Details Can Be Stored Long-Term

Places API policy documentation outlines requirements for content caching, storage, and attribution, while providing specific exceptions for Place ID caching. Permitted identifier storage does not imply that returned details like reviews, photos, and attributes can be copied indefinitely.

Designs should differentiate by field and purpose. Proprietary master store records originate from internal operations; external location mappings permit ID storage; displaying external content requires following respective terms for updates, storage, and attribution. Conditions vary by region and use case, and this text does not replace project-specific compliance reviews.

If your task focuses on review analysis, the Google Maps Reviews API Guide distinguishes between review endpoints. Selecting ingestion points and entity deduplication are sequential but distinct steps: the former determines available data, while the latter determines where data is attributed.

Test for False Merges Using Edge Cases Before Deployment

Store deduplication rules should not be tested solely on whether obviously identical records merge correctly. Testing negative cases—situations that should not merge—and verifying recovery workflows provides greater value.

Prepare four groups of proprietary or synthetic samples: different branches under the same brand, identical locations with multi-language names, expired IDs where closure is unconfirmed, and relocated stores with new location IDs. Confirm that systems avoid merging separate branches incorrectly, splitting entities solely by language, counting query failures as closures, or erasing relocation events.

Spot-check rollups by naming external result counts, unique location counts, and business store counts separately. Because these metrics naturally differ, do not force discrepancies to align just to satisfy reports. The Multi-Platform Reputation Monitoring Blueprint discusses how misattributed data impacts business decisions.

The primary challenge in store data management is knowing which object you are comparing at any given time. Retain external IDs alongside their validity periods and mapping evidence, and keep unverified matches pending. As collection scales, this approach increases available records rather than generating harder-to-reverse incorrect linkages.