Google Maps Review & Reputation Monitor

Analyze recurring Google Maps review datasets to detect rating drift, negative-review acceleration, reputation risk and competitor review momentum over time.

Business use and first result

For: Local business reputation analysts.

Why run it: Identify rating drift, negative-review acceleration and review momentum across supplied Google Maps snapshots.

What to inspect: Time-based comparison signals based on provided review/rating records.

First run: use the documented sample input below, inspect the Dataset rows and source links, then reuse the saved output for a later comparison only where snapshots are supported. Confirm the input scope and current Pay Per Event pricing before scheduling ongoing use.

Scope and limitations: Analysis requires legitimate review data and enough comparison coverage; it does not bypass access restrictions.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "id": "g1",
      "business": "Cafe A",
      "reviewer": "Jo",
      "rating": 2,
      "text": "Slow service",
      "publishedAt": "2026-09-09",
      "url": "https://maps.example/g1"
    },
    {
      "id": "g2",
      "business": "Cafe A",
      "reviewer": "Lu",
      "rating": 1,
      "text": "Bad experience",
      "publishedAt": "2026-09-09",
      "url": "https://maps.example/g2"
    },
    {
      "id": "g3",
      "business": "Cafe A",
      "reviewer": "Mi",
      "rating": 5,
      "text": "Great coffee",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g3"
    },
    {
      "id": "g4",
      "business": "Cafe B",
      "reviewer": "Ana",
      "rating": 4,
      "text": "Good",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g4"
    }
  ],
  "previousItems": [
    {
      "id": "g3",
      "business": "Cafe A",
      "reviewer": "Mi",
      "rating": 5,
      "text": "Great coffee",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g3"
    }
  ]
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current rows: Current normalized or raw source rows.
• currentDatasetId — Current Apify Dataset ID: Optional Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional prior Dataset ID.
• maxItems — Maximum rows: Maximum number of source rows to load from an Apify Dataset.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include dataset, report.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.010000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Analyze Google Maps review datasets to detect rating drift, negative-review acceleration, reputation risk and competitor review momentum.

Why use this Actor

Local brands and agencies can collect reviews but still need recurring evidence of reputation deterioration, review velocity and competitor movement. This Actor is deliberately separated from source extraction. It accepts supplied public-data rows or an Apify Dataset and turns them into recurring decision intelligence. That architecture reduces dependence on source-site markup, login flows and proxy behavior while letting upstream collection tools change without rebuilding the intelligence layer.

Key features

• Measures rating and negative-review drift across snapshots
• Computes deterministic reputationRiskScore and review velocity
• Separates new negative reviews from historical baseline
• Produces agent-ready escalation actions instead of raw reviews
• Reads inline rows or an Apify Dataset with read-only permissions.
• Compares an optional previous snapshot and emits deterministic agentAction output.
• Runs at 256 MB with no mandatory paid LLM or browser dependency.

Example

{
  "currentItems": [
    {
      "id": "g1",
      "business": "Cafe A",
      "reviewer": "Jo",
      "rating": 2,
      "text": "Slow service",
      "publishedAt": "2026-09-09",
      "url": "https://maps.example/g1"
    },
    {
      "id": "g2",
      "business": "Cafe A",
      "reviewer": "Lu",
      "rating": 1,
      "text": "Bad experience",
      "publishedAt": "2026-09-09",
      "url": "https://maps.example/g2"
    },
    {
      "id": "g3",
      "business": "Cafe A",
      "reviewer": "Mi",
      "rating": 5,
      "text": "Great coffee",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g3"
    },
    {
      "id": "g4",
      "business": "Cafe B",
      "reviewer": "Ana",
      "rating": 4,
      "text": "Good",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g4"
    }
  ],
  "previousItems": [
    {
      "id": "g3",
      "business": "Cafe A",
      "reviewer": "Mi",
      "rating": 5,
      "text": "Great coffee",
      "publishedAt": "2026-09-08",
      "url": "https://maps.example/g3"
    }
  ]
}

The run writes one structured report to the default Dataset and stores the same object in INTELLIGENCE_REPORT. Downstream automations can route the report by agentAction, while the current source Dataset can be preserved as the baseline for the next scheduled run.

Use cases

• Local SEO agencies
• Multi-location brands
• Reputation teams
• Franchises
• Competitive intelligence
• AI agents

A common workflow is: collect authorized public data, store it in an Apify Dataset, run this Actor with current and previous Dataset IDs, route the resulting action to a workflow or agent, and preserve the current Dataset for the next comparison.

Pricing

The product uses one primary pay-per-event unit: One decision-ready intelligence report written to the default dataset. The configured price is $0.010 per report before any Marketplace tiering or future approved changes. There is no separate start-fee design in the QuanMatrix product model.

Limitations

• Analyzes supplied public-data datasets; it does not bypass authentication or private-data controls.

• Signals are deterministic heuristics based on observed snapshot fields.

• Input field aliases are normalized conservatively.

• The Actor does not bypass authentication, private-account controls, robots restrictions, or source-platform access rules.

• Decision scores are prioritization aids based on observed fields and snapshot differences, not guarantees about future outcomes.

Output and automation

Every result contains ok, mode and agentAction, plus source counts and product-specific score fields. The output schema exposes both the default Dataset and the stored intelligence report so the Actor can be used from Tasks, schedules, API calls, agents and other Apify workflows.

Gen2 decision intelligence

This Actor preserves its original analysis and adds a decision layer with baseline awareness, regression detection, confidence, GO/WARN/BLOCK executive output, and an optional economic-impact estimate. Economic estimates are produced only when the user supplies valuePerImpactUnitUsd; the result states the calculation basis instead of inventing monetary value.

Agent execution and AI discovery (October 2026)

Use this Actor when: Identify rating drift, negative-review acceleration and review momentum across supplied Google Maps snapshots. Output: Time-based comparison signals based on provided review/rating records.

For AI agents and automations:

• Find this Actor in Apify Store by its use case and verify its actual input and pricing through Apify MCP (search-actors and fetch-actor-details). Availability and ranking in Apify AI are controlled by Apify; discovery is not guaranteed.
• Read the Input tab or input schema. Recognized fields include currentItems, currentDatasetId, previousItems, previousDatasetId, maxItems, valuePerImpactUnitUsd, monthlyRuns, previousAnalysis. Required fields: See the Input tab for valid example values. Do not invent records or credentials.
• Supply a small representative input first. Use Apify API, Tasks, or MCP to run and retrieve the structured dataset; inspect the output schema rather than assuming all inputs produce the same rows.
• For programmatic calls, set timeouts and maxTotalChargeUsd in run options to limit exposure. Check the Apify Pricing tab for billable events and discounts.
• Payment compatibility: this Actor is published with limited permissions and pay-per-event pricing. Apify AGI prepaid x402 tokens can execute limited-permission Actors. Direct per-request payment is a separate, conditional feature that may require additional eligibility checks and creator verification. Do not interpret this paragraph as proof of approved KYC or guaranteed payment acceptance.
• This Actor does not independently guarantee real-time access to a third-party source. Verify input provenance and important conclusions, and do not supply confidential personal data unnecessarily.