# B2B Stakeholder Mapper — Buying Committee & Contacts (`noniner/b2b-stakeholder-mapper`) Actor

For each target company, map the buying committee for what YOU sell: which roles to target, their priorities and outreach order — plus any people named on the company site and the likely corporate email pattern. No LinkedIn scraping.

- **URL**: https://apify.com/noniner/b2b-stakeholder-mapper.md
- **Developed by:** [Fredrik Hansson](https://apify.com/noniner) (community)
- **Categories:** Lead generation, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $150.00 / 1,000 account mappeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

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 Stakeholder Mapper — Buying Committee & Contacts

**Stop selling to a single inbox.** A B2B deal is won by a *committee*, not a contact. Hand this Actor a list of target company domains and one line about what **you** sell, and for each account it maps the **buying committee**: which job roles to target, who champions you, who signs the cheque, who blocks you — the order to reach them in, an opening angle for each, plus any people named on the company's own site and the **likely corporate email pattern**.

You bring the account list. It hands back a multi-threading playbook per company.

***

### The flow

```
1. Domains + what you sell       2. Read the site + enrich          3. Get the committee map
   stripe.com, notion.so     →      homepage · about · team    →      roles · personas · order ·
   "We sell SOC2 automation"        + firmographics (size)            angles · contacts · pattern
```

1. **Give target domains and your `offering`.** The committee is always mapped *relative to what you sell* — a security platform and a payroll tool pull a completely different set of roles out of the same company.
2. **The Actor reads each company's own website** (homepage + `about` / `team` / `leadership` / `people` / `contact`) and optionally enriches firmographics (industry + size) so seniority is calibrated — a 5,000-person company buys through VPs and Directors; a 20-person startup buys through its founders.
3. **AI builds the buying committee** for that specific purchase and returns it ready to paste into your CRM or sequencer.

***

### What you get per company

For every company, the Actor:

1. **Crawls the homepage + up to 7 relevant subpages** (`about`, `about-us`, `team`, `leadership`, `company`, `people`, `contact`) with a fast HTTP client.
2. **Extracts role-based contacts** published on the site (`info@`, `sales@`, `hello@`, …) plus links to LinkedIn / X / Facebook / Instagram / YouTube.
3. **Finds on-site people** — any names and titles printed on team/leadership pages (`onSitePeople`), confirmed by the AI from the page text. Empty when the site names nobody.
4. **Infers the corporate email pattern** from a single real personal address on the site: `jane.doe@company` → `{first}.{last}@company`; `jdoe@company` → `{f}{last}@company`. The evidence email is recorded, and the pattern is `null` when only generic inboxes exist.

Then the **AI layer** (OpenAI `gpt-4o-mini`, a fast and low-cost model) returns:

- **`buyingCommittee`** — an array of `{ role, seniority, personaType, whyRelevant, whatTheyCareAbout, likelyObjection }`. `seniority` is one of `C-level · VP · Director · Manager · IC`; `personaType` is one of `champion · economic buyer · technical buyer · end user · blocker`.
- **`recommendedOrder`** — the roles in the sequence you should actually reach out in (usually champion / technical buyer first, economic buyer later).
- **`openingAngleByRole`** — a concrete opening angle tailored to each role.

***

### Three ways people use it

- **Account mapping** — turn a flat list of target domains into a structured map of *who to contact at each company*, with the firmographics (industry, size) that justify the seniority.
- **Buying committee / multi-threading** — stop single-threading a deal: get every persona in the committee (champion, economic buyer, technical buyer, end user, blocker), what each one cares about, and their likely objection, so you can multi-thread with intent.
- **Outbound targeting & email pattern discovery** — know the exact roles to prospect, the order to hit them, an opening line per role, the role-based inboxes already on the site, and the inferred email pattern so you can construct addresses for the people you target.

***

### Input

| Field | Type | Notes |
|---|---|---|
| `domains` | array | **Required.** Company domains, website URLs or emails (the domain is extracted). One per line. |
| `offering` | string | **Required.** What you sell. The committee is mapped relative to this. |
| `maxResults` | integer | Cap how many companies to map. Default `50`. One charge per company mapped. |
| `maxConcurrency` | integer | Companies processed in parallel. Default `5`. |
| `model` | string | Override the OpenAI model. Default `gpt-4o-mini`. |
| `useEnrichment` | boolean | Default `true`. Call AI Company Enrichment first for industry + size, which calibrate seniority. Turn off for cheaper, less precise mapping. |

#### Minimal input example

```json
{
  "domains": ["stripe.com", "notion.so"],
  "offering": "We sell a SOC2 compliance automation platform for SaaS companies."
}
```

***

### Output example

```json
{
  "domain": "stripe.com",
  "companyName": "Stripe",
  "industry": "Financial technology / payments infrastructure",
  "sizeSignal": "5000+ employees",
  "buyingCommittee": [
    {
      "role": "Chief Information Security Officer (CISO)",
      "seniority": "C-level",
      "personaType": "economic buyer",
      "whyRelevant": "Owns the security and compliance posture that a SOC2 automation platform directly improves.",
      "whatTheyCareAbout": "Reducing audit toil and continuous compliance across a large engineering org.",
      "likelyObjection": "We already have mature internal tooling and a dedicated GRC team."
    },
    {
      "role": "Head of Security / GRC",
      "seniority": "Director",
      "personaType": "champion",
      "whyRelevant": "Runs the day-to-day audit evidence collection your platform automates.",
      "whatTheyCareAbout": "Cutting manual evidence gathering before the next SOC2 cycle.",
      "likelyObjection": "Switching costs and integration effort with existing controls."
    }
  ],
  "recommendedOrder": [
    "Head of Security / GRC",
    "Chief Information Security Officer (CISO)",
    "VP of Engineering"
  ],
  "openingAngleByRole": [
    { "role": "Head of Security / GRC", "angle": "Lead with hours saved on evidence collection before the next audit window." }
  ],
  "onSitePeople": [],
  "roleBasedEmails": ["press@stripe.com"],
  "emailPattern": "{first}@stripe.com",
  "emailPatternEvidence": "patrick@stripe.com",
  "socials": {
    "linkedin": "https://linkedin.com/company/stripe",
    "twitter": "https://twitter.com/stripe"
  }
}
```

`onSitePeople` is `[]` when the site names no individuals; `emailPattern` is `null` when only generic inboxes are found.

***

### Pay-per-event pricing

This Actor uses **pay-per-event** billing with a single event:

| Event | Price | Charged when |
|---|---|---|
| `account-mapped` | $0.15 | Once per company mapped into a buying committee with target roles, outreach order, on-site contacts and the inferred email pattern. |

The Actor logs its OpenAI token usage and the estimated AI cost at the end of every run, so the event price always stays safely above the model cost.

***

### Setup

The AI layer calls the OpenAI API. Set the environment variable / secret **`OPENAI_API_KEY`** on the Actor. Without it the run fails fast — the committee mapping is the whole product.

***

### No LinkedIn / a note on privacy

This Actor **does not scrape LinkedIn or any people database** — those sources are blocked and legally risky. It reads only the **company's own public website** and reasons about *roles* with AI. It will surface a named individual only when that person is already published on the company's own team/leadership page, and it never invents specific people. The email *pattern* is inferred from one real address on the site; it is a format, not a scraped list of personal addresses.

***

### Use it from an AI agent (Apify MCP server)

This Actor is available through the [Apify MCP server](https://mcp.apify.com), so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it `domains` and an `offering`, and read back the buying-committee map — with no custom integration code.

***

### Run it locally

```bash
npm install
## put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...
apify run -p
```

***

### Related actors

- [B2B Account Brief](https://apify.com/noniner/b2b-account-brief) — Turn a domain into a sales-ready account brief: what they do, signals and angles
- [AI Cold Email Personalizer](https://apify.com/noniner/ai-cold-email-personalizer) — Turn a list of domains into personalized cold-email openers from each company's site
- [B2B Intent Radar](https://apify.com/noniner/b2b-intent-radar) — Spot companies showing buying signals so you reach out at the right moment
- [Email Finder](https://apify.com/noniner/email-finder-scraper) — Find email addresses for any domain — crawls the homepage plus contact/about/team pages

***

**Part of the [No Niner](https://noniner.io) toolkit** — no-code web scraping, enrichment & AI data tools, all pay-per-use. Browse the full set at **[noniner.io](https://noniner.io)**.

# Actor input Schema

## `domains` (type: `array`):

Company domains, website URLs or emails (the domain is extracted). One per line.

## `offering` (type: `string`):

Describe what YOU sell. The buying committee is mapped relative to this — who inside each company would evaluate, buy and block it.

## `datasetId` (type: `string`):

Optional. Read the domains from a previous Actor run's dataset — for no-code chaining via the Integrations tab.

## `datasetField` (type: `string`):

Which dataset field holds the domain/URL. Leave blank to auto-detect (domain, website, companyDomain, url, companyUrl).

## `maxResults` (type: `integer`):

Max companies to map. One pay-per-event charge per company mapped.

## `maxConcurrency` (type: `integer`):

How many companies to process in parallel.

## `model` (type: `string`):

Override the model (default gpt-4o-mini).

## `useEnrichment` (type: `boolean`):

Call the AI Company Enrichment actor for industry + size signals, which calibrate the seniority of the committee. Turn off to skip (cheaper, less precise).

## Actor input object example

```json
{
  "domains": [
    "stripe.com",
    "notion.so"
  ],
  "offering": "We sell a SOC2 compliance automation platform for SaaS companies.",
  "maxResults": 50,
  "maxConcurrency": 5,
  "useEnrichment": true
}
```

# Actor output Schema

## `accounts` (type: `string`):

All mapped accounts in the default dataset.

# 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 = {
    "domains": [
        "stripe.com",
        "notion.so"
    ],
    "offering": "We sell a SOC2 compliance automation platform for SaaS companies."
};

// Run the Actor and wait for it to finish
const run = await client.actor("noniner/b2b-stakeholder-mapper").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 = {
    "domains": [
        "stripe.com",
        "notion.so",
    ],
    "offering": "We sell a SOC2 compliance automation platform for SaaS companies.",
}

# Run the Actor and wait for it to finish
run = client.actor("noniner/b2b-stakeholder-mapper").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 '{
  "domains": [
    "stripe.com",
    "notion.so"
  ],
  "offering": "We sell a SOC2 compliance automation platform for SaaS companies."
}' |
apify call noniner/b2b-stakeholder-mapper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,noniner/b2b-stakeholder-mapper"
        }
    }
}
```

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/YaqiDejGZiEwbh3iA/builds/Ns06fBzHSsbCAuZbM/openapi.json