Press Release Narrative Shift Intelligence
Pricing
from $10.50 / 1,000 results
Press Release Narrative Shift Intelligence
Use this Actor to analyze press release narrative shift and return decision-ready structured signals. Compare recurring corporate press-release datasets to detect topic, product, geography, claim, and strategic narrative shifts over time.
Pricing
from $10.50 / 1,000 results
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0.0
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Developer
Rafael Barreto Haddad
Maintained by CommunityActor stats
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1
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4 days ago
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Use this Actor to analyze press release narrative shift and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.
Compare recurring corporate press-release datasets to detect topic, product, geography, claim, and strategic narrative shifts over time.
Why use this Actor
Press-release monitors surface new announcements, but strategy teams need to know how a company’s repeated messaging, product emphasis and strategic narrative are changing across time. 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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Narrative and topic mix changes across recurring release corpora.
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Company-level strategic messaging momentum instead of one-time release retrieval.
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Product and geography emphasis shifts with deterministic review actions.
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Data-first layer that can sit downstream of any press-release scraper.
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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
- competitive intelligence and strategy.
- investor relations research.
- PR and corporate communications monitoring.
Pricing
One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.015 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.
Input
Provide current normalized records in currentItems or an Apify Dataset ID in currentDatasetId. Add previousItems or previousDatasetId for period-over-period comparison. Optional Gen2 fields support prior-analysis comparison and transparent economic-impact assumptions.
Output
The Actor writes one decision-ready report to the default Dataset, including normalized counts, ranked signals, change metrics, confidence, executive decision, regression status, recommended action, and an auditable agentAction.
