# Reddit Lead Qualifier (`delightful_unicorn/reddit-lead-qualifier`) Actor Extract purchase-intent and product-experience posts from Reddit with integrated sentiment analysis and lead scoring. Turn Reddit conversations into qualified sales leads. - **URL**: https://apify.com/delightful\_unicorn/reddit-lead-qualifier.md - **Developed by:** [Alessandro Amato](https://apify.com/delightful_unicorn) (community) - **Categories:** Lead generation, Social media, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.50 / 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Reddit Lead Qualifier **Turn Reddit conversations into qualified sales leads.** Unlike generic Reddit scrapers, this actor extracts ONLY posts with purchase intent or product experience signals, runs integrated sentiment analysis, and scores every lead from 0 to 100. Perfect for sales teams, product researchers, competitive intelligence analysts, and growth hackers who want to find high-intent prospects discussing problems, reviews, comparisons, and buying decisions in any niche. ### Features - **Multi-subreddit scraping** — process one or many subreddits in a single run - **Purchase-intent detection** — lexicon-based detection of buying signals, pain points, competitor mentions, and product experiences - **Integrated sentiment analysis** — powered by AFINN-165 lexicon (no external API key needed) - **Lead scoring (0–100)** — proprietary algorithm weighing intent, sentiment, engagement, and recency - **Global Reddit search** — search across all of Reddit by keyword - **Bulk qualification** — resilient batch processing with automatic retries and per-subreddit error isolation - **No Reddit API key required** — uses public JSON endpoints ### Actions #### `scrapeSubreddit` Scrape hot/new/top/rising posts from one or more subreddits. **Input:** ```json { "action": "scrapeSubreddit", "subreddit": "startups, SaaS, marketing", "sort": "hot", "limit": 25 } ```` #### `searchReddit` Search Reddit globally by keyword. **Input:** ```json { "action": "searchReddit", "keywords": "project management tool", "limit": 50 } ``` #### `qualifyLeads` ⭐ (Recommended) Scrape subreddits and return ONLY posts that match purchase-intent or product-experience signals, scored and ranked. **Input:** ```json { "action": "qualifyLeads", "subreddit": "startups, SaaS", "sort": "hot", "limit": 50, "keywords": "CRM, outreach", "minScore": 60 } ``` **Output example:** ```json { "leadScore": 87, "category": "buying_intent", "sentiment": { "score": 4, "comparative": 0.12, "positive": ["best", "love"], "negative": [] }, "intentMatches": { "buying": ["recommend", "best"], "experience": ["used"], "pain": [], "competitor": [] }, "post": { "title": "What's the best CRM for a 5-person SaaS team?", "selftext": "We've tried HubSpot but it's overkill...", "author": "techfounder92", "subreddit": "SaaS", "url": "...", "permalink": "https://www.reddit.com/r/SaaS/comments/...", "upvotes": 142, "comments": 38, "createdUtc": "2024-05-01T14:23:00.000Z" } } ``` #### `analyzeSentiment` Analyze sentiment of raw text or a Reddit post URL. **Input:** ```json { "action": "analyzeSentiment", "text": "I absolutely love this product, best purchase I've made all year!" } ``` #### `bulkQualify` Process multiple subreddits with full error isolation — if one subreddit fails, the others continue. **Input:** ```json { "action": "bulkQualify", "subreddit": "startups, SaaS, marketing, sales, productivity", "sort": "new", "limit": 25, "minScore": 50 } ``` ### Lead Scoring Algorithm | Factor | Points | |--------|--------| | Base score | 50 | | Buying-intent keywords detected | +25 | | Product-experience keywords detected | +15 | | Pain-point + negative sentiment | +20 | | Competitor mention detected | +10 | | Strong sentiment (|score| > 5) | +10 | | Engagement boost (upvotes + comments×2) | +0 to +15 | | Posted < 24 hours ago | +5 | **Score range:** 0–100. Leads ≥ 70 are high-probability opportunities. ### Input Parameters | Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | `action` | string | yes | — | One of: `scrapeSubreddit`, `searchReddit`, `qualifyLeads`, `analyzeSentiment`, `bulkQualify` | | `subreddit` | string | no | `startups` | Comma-separated subreddit names (without `r/`). | | `sort` | string | no | `hot` | `hot`, `new`, `top`, `rising` | | `limit` | integer | no | `25` | Max posts per subreddit (1–100). | | `keywords` | string | no | — | Comma-separated extra filter keywords. | | `text` | string | no | — | Text or Reddit permalink for `analyzeSentiment`. | | `minScore` | integer | no | `0` | Minimum lead score to return (0–100). | ### Rate Limits & Resilience - 750ms delay between sequential Reddit requests when processing multiple subreddits - Randomized User-Agent rotation - Graceful error handling: partial data is returned even if Reddit blocks some requests (403/429) - No Reddit API key or OAuth required ### Pricing **Pay Per Event** - Start event: **$0.0005** - Result event: **$0.0005** Approx. **$1 per 1,000 qualified leads** extracted. ### Use Cases - **Sales Development:** Find prospects actively asking for product recommendations in your niche - **Competitive Intelligence:** Track competitor mentions and switching intent - **Product Research:** Aggregate honest reviews, pain points, and feature requests - **Content Marketing:** Discover trending questions and high-engagement discussions to address ### Disclaimer This actor scrapes publicly available Reddit data. Always comply with Reddit's [Terms of Service](https://www.reddit.com/policies/user-agreement) and applicable data regulations. The lead scores and sentiment labels are algorithmic estimates and should be validated by a human before outreach. # Actor input Schema ## `action` (type: `string`): What do you want to do? ## `subreddit` (type: `string`): Comma-separated subreddit names (without r/). Example: startups, SaaS, marketing ## `sort` (type: `string`): How to sort Reddit posts. ## `limit` (type: `integer`): Max posts to fetch per subreddit (max 100). ## `keywords` (type: `string`): Comma-separated keywords to filter posts. Leave empty to use default intent keywords. ## `text` (type: `string`): Text or Reddit post permalink for analyzeSentiment action. ## `minScore` (type: `integer`): Only return leads with score >= this value (0-100). ## Actor input object example ```json { "action": "qualifyLeads", "subreddit": "startups", "sort": "hot", "limit": 25, "minScore": 0 } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("delightful_unicorn/reddit-lead-qualifier").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("delightful_unicorn/reddit-lead-qualifier").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call delightful_unicorn/reddit-lead-qualifier --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delightful_unicorn/reddit-lead-qualifier", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Reddit Lead Qualifier", "description": "Extract purchase-intent and product-experience posts from Reddit with integrated sentiment analysis and lead scoring. Turn Reddit conversations into qualified sales leads.", "version": "1.0", "x-build-id": "HFFQD6valsNOPzN7p" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delightful_unicorn~reddit-lead-qualifier/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delightful_unicorn-reddit-lead-qualifier", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delightful_unicorn~reddit-lead-qualifier/runs": { "post": { "operationId": "runs-sync-delightful_unicorn-reddit-lead-qualifier", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delightful_unicorn~reddit-lead-qualifier/run-sync": { "post": { "operationId": "run-sync-delightful_unicorn-reddit-lead-qualifier", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "action" ], "properties": { "action": { "title": "Action", "enum": [ "scrapeSubreddit", "searchReddit", "qualifyLeads", "analyzeSentiment", "bulkQualify" ], "type": "string", "description": "What do you want to do?", "default": "qualifyLeads" }, "subreddit": { "title": "Subreddit(s)", "type": "string", "description": "Comma-separated subreddit names (without r/). Example: startups, SaaS, marketing", "default": "startups" }, "sort": { "title": "Sort By", "enum": [ "hot", "new", "top", "rising" ], "type": "string", "description": "How to sort Reddit posts.", "default": "hot" }, "limit": { "title": "Limit", "minimum": 1, "maximum": 100, "type": "integer", "description": "Max posts to fetch per subreddit (max 100).", "default": 25 }, "keywords": { "title": "Keywords", "type": "string", "description": "Comma-separated keywords to filter posts. Leave empty to use default intent keywords." }, "text": { "title": "Text / Post URL", "type": "string", "description": "Text or Reddit post permalink for analyzeSentiment action." }, "minScore": { "title": "Minimum Lead Score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only return leads with score >= this value (0-100).", "default": 0 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```