Competitor Pricing Page Change Intelligence

Use this Actor to monitor competitor pricing page change changes and return decision-ready change signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Turn recurring alt data datasets into competitor pricing page change intelligence with snapshot deltas, confidence-scored GO/WARN/BLOCK decisions, and agent-ready recommended actions.

Why use this Actor

Teams following alt data changes need repeatable evidence and prioritized actions instead of repeatedly inspecting raw rows by hand. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Current-versus-previous snapshot comparison with deterministic deltas.

• Gen2 confidence-scored GO/WARN/BLOCK decision contract.

• Agent-ready recommended actions instead of raw rows.

• Dataset-ID inputs for recurring Apify workflows.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• alt data monitoring and prioritization.
• zero direct intelligence recurring workflows.
• competitive research and executive briefs.
• AI-agent decision pipelines.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.017 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Output

The Actor returns one structured intelligence report containing normalized counts, snapshot deltas, the strongest pricing-page changes, confidence signals, the executive GO/WARN/BLOCK decision, and an agentAction suitable for downstream automation. Use the default Dataset view for audit-friendly review, or pass the resulting record into another Actor, webhook, dashboard, or AI-agent workflow. The output is intentionally aggregated so teams can act on material pricing changes without manually comparing every raw page snapshot.