Executive Departure & Leadership Change Monitor

Detect clusters of executive departures, appointments, promotions, and leadership turnover from recurring executive-change datasets, with structured change evidence and decision-ready signals.

Why use this Actor

Executive-change feeds report one move at a time, but investors and GTM teams need to detect when multiple senior departures or appointments cluster around the same company and signal leadership instability or strategic change.

Key features

• Buyer-native inputs aligned to executive job changes.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "eventId": "x1",
      "company": "Acme Corp",
      "person": "CEO A",
      "role": "CEO",
      "seniority": "C-Suite",
      "moveType": "departure",
      "seniorityWeight": 100,
      "date": "2026-09-01"
    },
    {
      "eventId": "x2",
      "company": "Acme Corp",
      "person": "CFO B",
      "role": "CFO",
      "seniority": "C-Suite",
      "moveType": "departure",
      "seniorityWeight": 95,
      "date": "2026-09-04"
    },
    {
      "eventId": "x3",
      "company": "Beta Inc",
      "person": "COO C",
      "role": "COO",
      "seniority": "C-Suite",
      "moveType": "appointment",
      "seniorityWeight": 90,
      "date": "2026-09-05"
    }
  ],
  "previousItems": [
    {
      "eventId": "x0",
      "company": "Acme Corp",
      "person": "VP D",
      "role": "VP Sales",
      "seniority": "VP",
      "moveType": "departure",
      "seniorityWeight": 55,
      "date": "2026-06-01"
    },
    {
      "eventId": "x3",
      "company": "Beta Inc",
      "person": "COO C",
      "role": "VP Ops",
      "seniority": "VP",
      "moveType": "promotion",
      "seniorityWeight": 70,
      "date": "2026-06-10"
    }
  ],
  "maxItems": 20000
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current records: Current source or normalized rows to analyze.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset ID used when currentItems is not supplied.
• previousDatasetId — Previous Apify Dataset ID: Optional previous Apify Dataset ID used instead of previousItems.
• maxItems — Maximum records: Maximum records loaded from a Dataset input.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.018000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

Detect clusters of executive departures, appointments, promotions, and leadership turnover from recurring executive-change datasets.

Why use this Actor

Executive-change feeds report one move at a time, but investors and GTM teams need to detect when multiple senior departures or appointments cluster around the same company and signal leadership instability or strategic change. 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

• Company-level executive turnover clusters across time windows.

• Seniority-weighted concentration instead of isolated job-change rows.

• Departure-versus-appointment balance and leadership instability signals.

• Reusable downstream layer for executive-change feeds, SEC 8-K data, and workforce datasets.

• 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

• B2B sales trigger intelligence.
• investor and risk monitoring.
• competitive workforce strategy.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.018 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.