# Google Maps Review Drift & Reputation Intelligence (`quanmatrix/google-maps-review-drift-reputation-intelligence`) Actor

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

- **URL**: https://apify.com/quanmatrix/google-maps-review-drift-reputation-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Social media, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $7.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Google Maps Review Drift & Reputation Intelligence

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

```json
{
  "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.

# Actor input Schema

## `currentItems` (type: `array`):

Current normalized or raw source rows.

## `currentDatasetId` (type: `string`):

Optional Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for change intelligence.

## `previousDatasetId` (type: `string`):

Optional prior Dataset ID.

## `maxItems` (type: `integer`):

Maximum number of source rows to load from an Apify Dataset.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied USD value per detected impact unit. Used only for transparent economic impact estimates.

## `monthlyRuns` (type: `integer`):

Optional number of comparable monthly runs used with value per impact unit for the economic estimate.

## `previousAnalysis` (type: `object`):

Optional previous Gen2 output used to compare decision metrics and detect regression between analyses.

## Actor input object example

```json
{
  "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"
    }
  ],
  "maxItems": 20000,
  "monthlyRuns": 1,
  "previousAnalysis": {}
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

## `report` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/google-maps-review-drift-reputation-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/google-maps-review-drift-reputation-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call quanmatrix/google-maps-review-drift-reputation-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/google-maps-review-drift-reputation-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/SdhKcbKIkpqafWW3K/builds/f3Jl92ckqwtJYejoy/openapi.json