# Hero Signal Detector - Customer Success and Proof Signals (`shashghosh/hero-signal-detector`) Actor

Detect public customer-success, proof, review, news, and transformation signals for a company or account.

- **URL**: https://apify.com/shashghosh/hero-signal-detector.md
- **Developed by:** [Shashwat Ghosh](https://apify.com/shashghosh) (community)
- **Categories:** Social media, News, Lead generation
- **Stats:** 18 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$2.00 / 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.

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

## Hero Signal Detector — Monitor Customer Success & Proof Signals

Hero Signal Detector monitors public evidence for customer-success and transformation signals.

### What it does

It looks for public proof moments such as customer stories, review signals, public wins, case-study mentions, launches, and success indicators. It returns structured signal rows, confidence, source type, and recommended action.

### Best for

- Customer marketing and advocacy teams
- GTM teams looking for proof moments
- RevOps teams enriching account intelligence
- Agencies building customer-success signal reports

### Example input

```json
{
  "companyName": "Freshworks",
  "sources": "g2,capterra,trustradius,news,case-studies",
  "monitoringTargets": "reviews,news,publications",
  "signalTypes": "transformation,success",
  "alertThreshold": "low",
  "maxReviewsPerSource": 3,
  "useDirectReviewCrawl": false
}
```

### Important note

This Actor prioritizes reliable public evidence. Direct review-site crawling is intentionally conservative because many review platforms block automated crawlers. The Actor returns structured `no_signal` rows instead of failing when evidence is unavailable.

### Output

Rows include `recordType`, `companyName`, `sourceType`, `url`, `title`, `snippet`, `heroScore`, `confidence`, `persona`, `heartDimensions`, `status`, and `recommendation`.

### Limitations

Use this as a monitoring/proof-signal tool, not as a guarantee of complete market coverage. Recall depends on the accessibility of public evidence.

# Actor input Schema

## `companyName` (type: `string`):

Name of the company to monitor for hero signals

## `monitoringTargets` (type: `string`):

Comma-separated sources to monitor for hero signals. Any order is accepted, e.g. reviews,publications,news or reviews,news,publications.

## `signalTypes` (type: `string`):

Types of hero signals to detect (comma-separated)

## `alertThreshold` (type: `string`):

Minimum quality threshold for generating alerts

## `enableSocialMedia` (type: `boolean`):

Enable LinkedIn/Twitter monitoring (requires cookies for enhanced access)

## `linkedinCookies` (type: `string`):

LinkedIn session cookies in JSON format - use Cookie-Editor browser extension to export

## `twitterCookies` (type: `string`):

Twitter session cookies in JSON format - use Cookie-Editor browser extension to export

## `delayBetweenRequests` (type: `integer`):

Delay in milliseconds between requests to avoid rate limiting and account blocks

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

Maximum number of concurrent requests (keep low to avoid bans)

## `sources` (type: `string`):

Optional comma-separated override for underlying review sources. Leave blank to derive from Monitoring Targets.

## `maxReviewsPerSource` (type: `integer`):

Maximum records to inspect per source.

## `heroThreshold` (type: `integer`):

Numeric threshold. Overrides Alert Threshold if supplied.

## `useApifyProxy` (type: `boolean`):

Use Apify proxy where available.

## `includeEmptyResults` (type: `boolean`):

Return a structured no_signal row instead of an empty dataset when no signals are found.

## `testMode` (type: `boolean`):

Return deterministic sample output without external network calls.

## `searchFallback` (type: `boolean`):

Use public search-result snippets as the default resilient source strategy instead of direct review-site crawling.

## `crawlReviewSites` (type: `boolean`):

Advanced option. Off by default because review sites often block automated crawlers and increase runtime/cost.

## `maxSearchResults` (type: `integer`):

Maximum public search-result signals to keep.

## Actor input object example

```json
{
  "companyName": "Slack Technologies",
  "monitoringTargets": "reviews,publications,news",
  "signalTypes": "transformation,success,milestone",
  "alertThreshold": "medium",
  "enableSocialMedia": false,
  "delayBetweenRequests": 3000,
  "maxConcurrency": 1,
  "sources": "",
  "maxReviewsPerSource": 50,
  "heroThreshold": 60,
  "useApifyProxy": true,
  "includeEmptyResults": true,
  "testMode": false,
  "searchFallback": true,
  "crawlReviewSites": false,
  "maxSearchResults": 10
}
```

# Actor output Schema

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

Structured canonical rows produced by the actor.

## `summary` (type: `string`):

Human/agent-readable run summary stored in the default key-value store.

# 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 = {
    "companyName": "Slack Technologies",
    "monitoringTargets": "reviews,publications,news",
    "signalTypes": "transformation,success,milestone",
    "alertThreshold": "medium",
    "enableSocialMedia": false,
    "delayBetweenRequests": 3000,
    "maxConcurrency": 1
};

// Run the Actor and wait for it to finish
const run = await client.actor("shashghosh/hero-signal-detector").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 = {
    "companyName": "Slack Technologies",
    "monitoringTargets": "reviews,publications,news",
    "signalTypes": "transformation,success,milestone",
    "alertThreshold": "medium",
    "enableSocialMedia": False,
    "delayBetweenRequests": 3000,
    "maxConcurrency": 1,
}

# Run the Actor and wait for it to finish
run = client.actor("shashghosh/hero-signal-detector").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 '{
  "companyName": "Slack Technologies",
  "monitoringTargets": "reviews,publications,news",
  "signalTypes": "transformation,success,milestone",
  "alertThreshold": "medium",
  "enableSocialMedia": false,
  "delayBetweenRequests": 3000,
  "maxConcurrency": 1
}' |
apify call shashghosh/hero-signal-detector --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,shashghosh/hero-signal-detector"
        }
    }
}
```

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/6BnyUjG2FMRAUHjtp/builds/h8Lkr04vqv3SwwuCz/openapi.json