App Store Competitor Release & Review Shift Intelligence
Pricing
from $1.17 / 1,000 results
App Store Competitor Release & Review Shift Intelligence
Use this Actor to analyze app store competitor release and review shift and return decision-ready structured signals. Turn recurring reviews datasets into app store competitor release and review shift intelligence with snapshot deltas, confidence-scored GO/WARN/BLOCK decisions, and agent-ready recom
Pricing
from $1.17 / 1,000 results
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Developer
Rafael Barreto Haddad
Maintained by CommunityActor stats
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21 hours ago
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Use this Actor to analyze app store competitor release and review shift and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.
Turn recurring reviews datasets into app store competitor release & review shift intelligence with snapshot deltas, confidence-scored GO/WARN/BLOCK decisions, and agent-ready recommended actions.
Why use this Actor
Teams following reviews 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
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Current-versus-previous snapshot comparison with deterministic deltas.
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Gen2 confidence-scored GO/WARN/BLOCK decision contract.
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Agent-ready recommended actions instead of raw rows.
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Dataset-ID inputs for recurring Apify workflows.
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Reads inline JSON rows or Apify Dataset IDs with limited READ permission.
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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
- reviews 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 0.002 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 writes one decision-ready intelligence report to the default Apify Dataset. The output includes snapshot counts, ranked change signals, confidence, GO/WARN/BLOCK decision fields, regression indicators, economic-impact fields when the user provides a value basis, and an agent-ready recommended action.
