Layoff-to-Hiring Reversal Intelligence

Use this Actor to analyze layoff-to-hiring reversal and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Combine layoff and hiring datasets to detect company workforce reversals, recovery and renewed contraction.

Why use this Actor

Layoff notices and job postings live in separate feeds, obscuring the moment a company moves from contraction back to expansion or relapses. 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

• Cross-signal layoff-to-hiring reversal detection.

• Company recovery versus renewed contraction scoring.

• Combines WARN-style and job-market snapshots.

• Deterministic workforce-cycle actions.

• 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

• credit and equity research.
• recruiting strategy.
• economic intelligence.

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 currentItems as normalized JSON rows or select a cloud Dataset with currentDatasetId. For change analysis, add previousItems or previousDatasetId. The Actor reads Datasets with limited READ permission. Keep identifiers stable across periods and normalize source fields where possible. The prefilled example is intentionally small so the first run can be validated before scaling.

Output

The default Dataset receives one decision-ready intelligence report with record counts, snapshot additions and removals, ranked entity signals, secondary-dimension breadth, a bounded signal score, and agentAction. The same report is stored as INTELLIGENCE_REPORT for downstream automation. Results are designed for Tasks, schedules, webhooks, dashboards, and agent workflows rather than as a replacement for source evidence.

Evolution round 2

Expanded source-field normalization after fresh market research so the Actor can consume a broader range of upstream datasets without pretending adjacent products are direct competitors.