Google Trends Breakout Demand Analyzer

Turn Google Trends exports into breakout, acceleration, seasonality and regional-demand intelligence with repeatable scores. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI agents. Returns structured.

Quick start

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

{
  "currentItems": [
    {
      "id": "t1",
      "title": "ai agents",
      "interest": 94,
      "growthPct": 88,
      "breakoutScore": 97,
      "regionalSpread": 82,
      "seasonalityFit": 76
    },
    {
      "id": "t2",
      "title": "old keyword",
      "interest": 48,
      "growthPct": 3,
      "breakoutScore": 21,
      "regionalSpread": 44,
      "seasonalityFit": 52
    }
  ],
  "previousItems": [
    {
      "id": "t1",
      "title": "ai agents",
      "interest": 71,
      "growthPct": 52,
      "breakoutScore": 76,
      "regionalSpread": 63,
      "seasonalityFit": 70
    }
  ],
  "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 source records: Optional current source rows. If supplied, these are analyzed directly and no source fetch is required.
• currentDatasetId — Current Apify Dataset ID: Current Apify Dataset from a compatible upstream extraction workflow.
• previousDatasetId — Previous Apify Dataset ID: Previous Apify Dataset for recurring snapshot comparison.
• maxItems — Maximum records to analyze: Maximum number of records analyzed and written to the output Dataset.
• payload — Apify integration payload: Automatically supplied by Apify Actor-to-Actor integrations. If currentDatasetId is empty, payload.resource.defaultDatasetId is used.

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 results, report.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.003000 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

Detailed documentation

Turn Google Trends exports into breakout, acceleration, seasonality and regional-demand intelligence with repeatable scores. This Actor is designed as a decision layer over current and historical datasets, so teams can reuse extraction they already trust instead of paying twice for the same collection work.

Why use this Actor

Raw marketplace exports are useful, but they usually stop at rows and fields. Google Trends Breakout Demand Intelligence adds deterministic scoring, explicit change detection, and action-ready output. It accepts inline records or an Apify Dataset, compares a previous snapshot when supplied, and returns a consistent decision surface for dashboards, workflows, agents, and recurring monitoring.

The design deliberately separates extraction from intelligence. That makes the Actor easier to maintain when a source website changes and lets users combine it with any compatible upstream scraper. Scores are transparent and based on documented fields rather than an opaque mandatory LLM call.

Key features

• Dataset-first workflow that works with upstream Apify Actors or your own normalized exports.
• Deterministic signalScore, changeScore, agentAction, and agentReason on every result.
• Current-versus-previous snapshot comparison for recurring intelligence.
• Low-memory 256 MB runtime and predictable pay-per-result economics.
• Structured Dataset output plus an INTELLIGENCE_REPORT key-value summary.
• Useful defaults for one-click testing before wiring a production Dataset.

Input

Provide currentItems for inline analysis or currentDatasetId for an upstream Apify Dataset. For change intelligence, also provide previousItems or previousDatasetId. maxItems limits how many records are analyzed in one run. The Actor does not require credentials for the source platform because the stable workflow starts from supplied or upstream-collected data.

Output

Each row contains the original source record plus signalScore, changeScore, agentAction, agentReason, normalized metrics, detected field changes, and an observation timestamp. This makes the Dataset suitable for filtering, automation, alerts, portfolio review, and AI-agent tool chains.

Example

A recurring workflow can run an upstream scraper daily, pass its Dataset ID as currentDatasetId, pass yesterday's Dataset as previousDatasetId, and use only records whose agentAction indicates a material opportunity or risk. This avoids rebuilding the same comparison logic in every automation.

Use cases

Use it for competitive monitoring, market research, prioritization, change alerts, analyst triage, recurring portfolio reviews, and agent workflows where raw records need a consistent action layer. It is especially useful when the source has many rows but human attention should be spent only on the highest-signal changes.

Pricing

The primary event price is $0.0030 USD per decision-ready result row. There is no separate analytical start fee in the product design. Users can control spend with maxItems and by filtering upstream data before analysis.

Limitations

This Actor analyzes the records supplied to it; source completeness depends on the upstream scraper or dataset. Scores are decision support, not predictions of financial outcomes. Field conventions can differ between upstream Actors, so the sample schema should be normalized when necessary. Only public or appropriately licensed data should be processed. Snapshot intelligence is meaningful only when entity identifiers are stable across periods.

Workflow design

For production use, keep extraction and intelligence as separate steps. This makes source replacement easier, allows historical snapshots to be retained independently, and lets the same intelligence Actor consume data from different collectors. The output schema stays stable even when the upstream extractor changes.