Google Maps Competitor Location Intelligence Agent

Use this Actor to analyze google maps competitor location and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Turn recurring Google Maps business snapshots into expansion, closure, brand-share, rating/review and geographic opportunity intelligence.

Why use this Actor

Raw Google Maps business data answers what exists now. Competitive strategy usually needs a different question: what changed, which brand is expanding, where locations disappeared, how concentrated is a city, and where is relative whitespace? This Actor is the downstream intelligence layer for those recurring decisions.

It accepts business rows from your preferred Google Maps collection workflow and compares the current portfolio with an optional previous snapshot. That separation is deliberate: customers can keep the scraper they already trust while using this Actor to create repeatable competitor intelligence.

Key features

• Detect openings and closures using placeId, URL or name fallback identity.
• Calculate brand location counts, share of observed locations and period-over-period deltas.
• Aggregate average rating, total reviews, city coverage and category coverage by brand.
• Detect rating, review-count, category and city changes for persistent locations.
• Rank cities by competitor density and report the observed market leader.
• Generate geographic density cells from latitude/longitude coordinates.
• Produce a transparent relative whitespace score for less crowded observed cities.
• Emit an agent-ready market summary with expansion/contraction action.

Input

Provide currentBusinesses as an array of Google Maps business rows. placeId is the preferred stable key. Include brand or competitor, city, category, rating, reviewsCount, latitude and longitude when available. Add previousBusinesses to activate opening, closure and change intelligence.

Output

The default Dataset contains multiple normalized record types: market_summary, brand_intelligence, city_intelligence, opening, closure, location_change and density_cell. The key-value store also receives COMPETITOR_SUMMARY with compact brand, city, category and density intelligence for downstream automation.

Use cases

Use this Actor for retail and franchise expansion, local SEO competitive monitoring, commercial-real-estate prospecting, territory planning, competitor footprint tracking, market-entry research, agency reporting and location-strategy agents. A recurring workflow can scrape or import locations, run this Actor with the previous snapshot, and alert only when meaningful expansion or contraction appears.

Example

Provide two current Austin coffee locations and a previous snapshot containing one Austin location plus a now-missing Dallas competitor. The output will identify the Dallas closure, rating/review changes on persistent locations, current brand share, city density and the net location-change action.

Pricing

Pricing is pay per intelligence row, not per scraped Google Maps request. This reflects the product's actual customer outcome: decision-ready brand, city and change records. Tiered PPE is applied only when measured margin remains positive.

Limitations

This Actor consumes supplied business rows and does not itself bypass Google Maps access controls. The whitespace score is a relative density heuristic over the observed dataset, not a population, demand or revenue forecast. Missing place IDs reduce identity precision because URL or name fallbacks can change. Coordinates are required for density-cell output.

Reliability and interpretation

All rankings and deltas are deterministic from the supplied snapshots. The Actor does not invent demand estimates. A city with low observed density is a research lead, not proof that a new store should be opened there. Customers should combine these signals with demographic, mobility, property and unit-economics data before capital decisions.