B2B Case Study Customer Win/Loss Intelligence

Use this Actor to analyze b2b case study customer win/loss and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Compare recurring vendor case-study and customer-story datasets to detect newly won customers, vanished references, use-case shifts, and proof momentum.

Why use this Actor

Case-study miners extract customer stories one page at a time, but competitive teams need longitudinal evidence of which vendors are adding or losing customer proof and where their use cases are shifting. 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

• New and removed customer-reference detection across snapshots.

• Vendor-level win/loss and proof momentum from public case-study datasets.

• Use-case and industry mix shifts beyond single-page extraction.

• Decision-ready competitive signals for account and market strategy.

• 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

• competitive intelligence teams.
• account-based sales strategy.
• VC PE and market research.

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.