Chrome Web Store Extension Market Analyzer

Analyze Chrome Web Store datasets to detect growth, pricing drift, rating changes, adoption momentum, new entrants and competitive opportunity.

Why use this Actor

Extension teams need longitudinal Chrome Web Store intelligence that reveals competitor growth and opportunity rather than isolated listing exports.

Key features

• Buyer-native inputs aligned to chrome web store extension intelligence.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

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

{
  "currentItems": [
    {
      "id": "a",
      "name": "Alpha App",
      "publisher": "Vendor A",
      "category": "Productivity",
      "price": 9,
      "rating": 4.6,
      "reviews": 120,
      "users": 5000,
      "rank": 4
    },
    {
      "id": "b",
      "name": "Beta Tool",
      "publisher": "Vendor B",
      "category": "Analytics",
      "price": 15,
      "rating": 4.3,
      "reviews": 75,
      "users": 2300,
      "rank": 12
    }
  ],
  "previousItems": [
    {
      "id": "a",
      "name": "Alpha App",
      "publisher": "Vendor A",
      "category": "Productivity",
      "price": 8,
      "rating": 4.5,
      "reviews": 100,
      "users": 4200,
      "rank": 7
    }
  ],
  "maxItems": 20000
}

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 marketplace rows: Current normalized or raw marketplace listing rows.
• currentDatasetId — Current Apify Dataset ID: Optional Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Dataset ID.
• maxItems — Maximum rows: Maximum rows loaded 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 $0.008000 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.

Decision-ready workflow (verified scope)

For: Extension developers.

Outcome: See category and publisher concentration and inspect high-potential extensions.

Input: Chrome Web Store listing rows supplied as JSON or an Apify Dataset.

First-run honesty: Without an earlier snapshot, this Actor reports the current records. It does not claim that every listing or record is newly added, nor that a trend was measured. A meaningful change needs an actual previous snapshot.

Evidence and costs: The Actor analyzes data provided as JSON or an accessible Apify Dataset, except the OpenAPI monitor which can also read public spec URLs. It does not guarantee direct marketplace crawling or guaranteed revenue. Review the source records, run cost, and Apify Store pricing before recurring deployment.

Detailed documentation

Detailed documentation

Analyze Chrome Web Store datasets to detect growth, pricing drift, rating changes, adoption momentum, new entrants and competitive opportunity.

Why use this Actor

Marketplace pages answer what exists right now. Product, strategy and competitive-intelligence teams usually need a different question answered: what changed, what is accelerating, which competitors are gaining adoption, where pricing is moving, and which categories deserve attention. This Actor turns recurring Chrome Web Store exports into a stable decision layer. It can use inline rows or Apify Datasets, so teams can pair it with an existing scraper, their own data collection, or scheduled snapshots without rebuilding the analysis workflow.

Key features

• Compare current and previous marketplace snapshots.
• Detect new and removed listings, pricing changes and rating drift.
• Measure adoption and review growth when those fields are present upstream.
• Rank leading listings using normalized adoption, review and rating signals.
• Produce a deterministic marketMomentumScore and a machine-friendly agentAction.
• Compare publisher concentration and category cohorts, and count vendor-lead records when public contact fields are supplied.
• Work with supplied datasets instead of requiring credentials or private marketplace access.

The Actor deliberately separates collection from intelligence. That reduces proxy cost, avoids unnecessary anti-bot coupling, and lets the same analysis run on different upstream collectors as the ecosystem changes.

Input

Provide currentItems directly or select a currentDatasetId. For longitudinal analysis also provide previousItems or previousDatasetId. Common fields such as title/name, publisher/vendor, price, rating, reviews, users/installs/downloads, rank and URL are normalized automatically. Missing optional metrics do not cause the run to fail.

Output

Each run writes one decision-ready report containing listing counts, entrant/removal counts, price-change count, rating changes, adoption growth, review growth, a market momentum score, top listings, new listings and the recommended agentAction. The report is also stored in the MARKET_INTELLIGENCE key-value record.

Example

Use a weekly snapshot from Chrome Web Store as currentDatasetId and the prior week's dataset as previousDatasetId. The Actor can then identify new entrants, price moves, rating deterioration, adoption acceleration and category leaders without manually comparing exports.

Use cases

Developers can monitor competitors before shipping a new product. Product managers can watch pricing and category crowding. Agencies can benchmark client ecosystems. Investors and analysts can detect adoption shifts across software marketplaces. QuantMatrix-style product factories can use the score as one input when deciding which niches deserve further research.

Pricing

This Actor uses pay-per-event pricing. The base price is USD 0.0080 per decision-ready intelligence report. Billing is attached to the primary result rather than every raw source row, keeping the commercial unit aligned with user value. Apify subscription tiers may receive lower effective event prices.

Limitations

The Actor analyzes public-data exports supplied by the user or another Apify Actor. It does not unlock private seller analytics, bypass authentication, or guarantee that every upstream marketplace exposes installs, sales, ratings or price. When an upstream scraper changes its field names, the generic normalizer covers common aliases but specialized mapping can still be useful. Decisions should use the score as structured evidence, not as a substitute for domain judgment.

Automation pattern

Schedule the upstream collector, save each run as a dated Dataset, then run this Actor with the newest and previous Dataset IDs. Store the resulting score and action in a dashboard, webhook, workflow or downstream agent. That creates recurring marketplace intelligence while keeping collection, analysis and action independently replaceable.

Agent execution and AI discovery (October 2026)

Use this Actor when: Analyze Chrome Web Store datasets to detect growth, pricing drift, rating changes, adoption momentum, new entrants and competitive opportunity.

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, mcpConnectors, mcpToolName, mcpToolArguments. 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.