B2B Case Study Customer Win/Loss Analyzer

Compare recurring vendor case-study and customer-story datasets to detect newly won customers, vanished references, use-case shifts, and proof momentum. Returns structured, machine-readable results for repeatable research, monitoring, automation, Apify Tasks, API workflows, and AI.

Quick start

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

{
  "currentItems": [
    {
      "id": "c1",
      "vendor": "Acme SaaS",
      "customer": "Northwind",
      "industry": "Retail",
      "useCase": "AI support",
      "proofScore": 92,
      "publishedAt": "2026-09-01"
    },
    {
      "id": "c2",
      "vendor": "Acme SaaS",
      "customer": "Contoso",
      "industry": "Finance",
      "useCase": "Fraud analytics",
      "proofScore": 85,
      "publishedAt": "2026-09-05"
    },
    {
      "id": "c3",
      "vendor": "Beta Cloud",
      "customer": "Fabrikam",
      "industry": "Manufacturing",
      "useCase": "Data platform",
      "proofScore": 76,
      "publishedAt": "2026-09-07"
    }
  ],
  "previousItems": [
    {
      "id": "c1",
      "vendor": "Acme SaaS",
      "customer": "Northwind",
      "industry": "Retail",
      "useCase": "AI support",
      "proofScore": 80,
      "publishedAt": "2026-06-01"
    },
    {
      "id": "c4",
      "vendor": "Acme SaaS",
      "customer": "OldCo",
      "industry": "Media",
      "useCase": "Automation",
      "proofScore": 65,
      "publishedAt": "2026-04-01"
    }
  ],
  "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

Detailed documentation

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.