Conference Exhibitor Churn Intelligence

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

Compare recurring trade-show exhibitor datasets to identify new exhibitors, lost exhibitors, category churn, and competitor event-presence shifts.

Why use this Actor

Exhibitor scrapers return a current list, while event and sales teams need to know who arrived, who disappeared, and which categories or competitors are changing presence over 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

• Year-over-year exhibitor entry and exit intelligence.

• Category-level churn and event-presence shifts instead of one-time lead extraction.

• Snapshot-based competitor attendance monitoring.

• Works downstream of exhibitor-list scrapers without requiring another crawl.

• 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

• event strategy and sponsorship teams.
• B2B competitive intelligence.
• sales territory and partner planning.

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.