# B2B Case Study Customer Win/Loss Intelligence (`quanmatrix/b2b-case-study-win-loss-intelligence`) Actor

Use this Actor to analyze b2b case study customer win/loss and return decision-ready structured signals. Compare recurring vendor case-study and customer-story datasets to detect newly won customers, vanished references, use-case shifts, and proof momentum.

- **URL**: https://apify.com/quanmatrix/b2b-case-study-win-loss-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** Business, Lead generation, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $12.60 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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`.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/b2b-case-study-win-loss-intelligence/changelog.md

# Actor input Schema

## `currentItems` (type: `array`):

Current source or normalized rows to analyze.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset ID used when currentItems is not supplied.

## `previousItems` (type: `array`):

Optional previous snapshot rows for period-over-period comparison.

## `previousDatasetId` (type: `string`):

Optional previous Apify Dataset ID used instead of previousItems.

## `maxItems` (type: `integer`):

Maximum records loaded from a Dataset input.

## `previousAnalysis` (type: `object`):

Optional prior Gen2 output used to calculate decision-metric deltas and regression.

## `valuePerImpactUnitUsd` (type: `number`):

Optional user-supplied economic value per impact unit. Leave empty to avoid monetary estimation.

## `monthlyRuns` (type: `integer`):

Optional expected monthly run count used only with valuePerImpactUnitUsd for economic impact estimation.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor\_title}}, {{result\_summary}}, or {{result\_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO\_SAFE\_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "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,
  "monthlyRuns": 1,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

## `results` (type: `string`):

Decision-ready intelligence report.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "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"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/b2b-case-study-win-loss-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "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",
        },
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/b2b-case-study-win-loss-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "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"
    }
  ]
}' |
apify call quanmatrix/b2b-case-study-win-loss-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/b2b-case-study-win-loss-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/52GXqW0pg8cDZvEsV/builds/fPaPQHwXj1k5FvUFu/openapi.json