Salary Band Drift by Role Intelligence
Pricing
from $8.40 / 1,000 results
Salary Band Drift by Role Intelligence
Use this Actor to monitor salary band drift by role changes and return decision-ready change signals. Track role-level salary band movement across companies and locations from recurring job datasets.
Pricing
from $8.40 / 1,000 results
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Developer
Rafael Barreto Haddad
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2 days ago
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Use this Actor to monitor salary band drift by role changes and return decision-ready change signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.
Track role-level salary band movement across companies and locations from recurring job datasets.
Why use this Actor
Job scrapers expose salary fields but rarely answer whether pay bands for a role are rising, falling or diverging across employers. 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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Role-level compensation drift across recurring snapshots.
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Company and geography breadth context.
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Pay-pressure and cooling signals.
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Works with normalized salary fields from job datasets.
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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
- compensation benchmarking.
- recruiting.
- workforce planning.
Pricing
One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 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.
