Reddit Lead Finder: Find Buying Intent & Competitor Complaints

Turn Reddit into a lead list. Reddit Lead Finder scans public Reddit for people asking for a tool like yours, complaining about a competitor, or looking to switch, then hands you the few posts that matter, scored, ranked, and with a suggested reply on each. No API keys, no login, no manual thread-reading.

Built for founders and marketers who want customers, not a 1,000-row data dump.

Why this over a raw Reddit scraper?

A raw scraper returns 1,000 posts and leaves you to read them. This finds the 12 that are actually leads.

	Raw Reddit scraper	🎯 Reddit Lead Finder
Output	Every post that matched	Only scored buying-intent leads
Reading required	You read 1,000 rows	You read the ranked shortlist
Per lead	Intent? Unknown	Intent type + score + why
Next step	Figure it out	Suggested reply on every lead
You pay for	Every row	Only qualified leads

👥 Who uses this

🚀 Bootstrapped SaaS founders

You want customers from Reddit without spamming. Point it at your category and competitors, get back threads where someone is actively asking for what you sell.

{
    "productDescription": "A lightweight time-tracking app for freelancers.",
    "keywords": ["time tracking app", "track freelance hours", "toggl alternative"],
    "competitors": ["Toggl", "Harvest", "Clockify"],
    "intentTypes": ["seeking_recommendation", "competitor_complaint", "switching_intent"],
    "timeFilter": "month",
    "maxLeads": 50
}

📣 Growth marketers & agencies

Run social-selling and competitor-conquesting campaigns. Filter to competitor_complaint + switching_intent to catch people mid-churn from a rival.

{
    "productDescription": "A CRM built for small real-estate teams.",
    "keywords": ["real estate CRM", "CRM for realtors"],
    "competitors": ["Follow Up Boss", "kvCORE"],
    "intentTypes": ["competitor_complaint", "switching_intent"],
    "minIntentScore": 65
}

🔎 Market researchers

Surface unmet needs and feature requests in your space with feature_request + problem_unaware, scored so you can rank pain by frequency.

🧠 How it works

1. Scans Reddit for your keywords and competitor names (public data, residential proxies, no login).
2. Scores every post through a three-tier pipeline (details below).
3. Delivers only qualified leads, each with an intent type, a 0 to 100 score, the reasons it scored, and a suggested reply.

Three-tier scoring (precision without a key)

Tier	What it does	Cost
1. Lexicon	Fast keyword and intent-cue match. Kills obvious noise.	Free, on by default
2. Semantic gate	Local embeddings check that a post is actually about your product, not just sharing a word. Drops "time tracking" in r/Porsche (track days) even though the phrase matches.	Free, on by default, no key
3. LLM (optional)	Set scoringMode: llm with your own API key for the sharpest read on ambiguous posts. Falls back to lexicon on any error.	Your key, only runs on posts that pass tiers 1 and 2

Tiers 1 and 2 need no API key and run on every post, so you get semantic precision out of the box. The LLM tier is an optional top layer, and because it only sees posts that already passed the cheaper tiers, it stays cheap.

📤 Sample output

Each dataset item is one qualified lead:

{
    "leadId": "t3_1abc234",
    "type": "post",
    "intentType": "competitor_complaint",
    "intentScore": 82,
    "scoreReasons": [
        "matched intent cue: 'fed up with'",
        "mentions competitor(s): Toggl",
        "first-person (author is likely the buyer)"
    ],
    "matchedKeywords": ["time tracking", "Toggl"],
    "title": "Fed up with Toggl's new pricing, what are people using instead?",
    "snippet": "Been on Toggl 3 years but the per-seat jump is brutal. Freelancer, just need start/stop + CSV export...",
    "url": "https://www.reddit.com/r/freelance/comments/1abc234/x/",
    "subreddit": "freelance",
    "author": "designerjane",
    "score": 47,
    "numComments": 23,
    "createdAt": "2026-07-11T14:02:00+00:00",
    "ageHours": 189.0,
    "suggestedAction": "Reply empathizing with the pain point, then softly mention your product as an option. They mentioned Toggl; contrast on the point they complained about.",
    "sentiment": "negative",
    "isAnswered": false
}

🏷️ Intent types

Intent	Catches	Example
seeking_recommendation	People directly asking for a tool	"Any recommendations for a time tracker?"
competitor_complaint	People unhappy with a rival	"Toggl is too expensive now"
switching_intent	People actively leaving a tool	"Moving off Harvest, need a replacement"
feature_request	People describing a missing capability	"Wish there was a tool that could..."
problem_unaware	People with the problem, no solution named	"Spending hours reconciling invoices by hand"

💸 Pricing (plain and honest)

Two line items. That is the whole bill.

• $0.06 per search run: charged once, only when a scan actually returns posts.
• $0.02 per qualified lead: charged only for leads delivered to your dataset.

You are never charged when we deliver nothing. Blocked run or zero matches = $0.00. Posts that score below your threshold are free. Set ACTOR_MAX_TOTAL_CHARGE_USD to hard-cap any run. Full detail: docs/PRICING.md.

Example: 12 leads = $0.06 + 12 × $0.02 = $0.30.

📥 Bulk input

Driving from a spreadsheet or a prior run? Skip the keyword box:

• keywordsDatasetId: an Apify dataset of keyword strings or { "keyword": "..." } objects.
• keywordsFileUrl: a public .csv (column keyword) or newline-delimited .txt.

All sources merge with the inline list and de-duplicate automatically.

⚙️ Input reference

Field	Type	Default	Notes
productDescription	string	-	Required. What you sell. Powers intent matching.
keywords	array	-	Category terms + competitor names. Optional if using bulk input.
competitors	array	[]	Names that boost complaint/switching scores.
subreddits	array	[]	Empty = all of Reddit. One value restricts search.
intentTypes	array	3 core types	Which signals to surface.
minIntentScore	int	55	Below this: not returned, not charged.
timeFilter	enum	month	day / week / month / year / all.
maxLeads	int	50	Cap on delivered leads (1-500).
scoringMode	enum	lexicon	lexicon (free: tiers 1+2) or llm (adds tier 3, your key).
openaiApiKey	secret	-	Only for scoringMode: llm. Never stored.
keywordsDatasetId / keywordsFileUrl	string	-	Bulk keyword sources.
proxyConfiguration	object	Residential	Reddit blocks datacenter IPs.

🤖 Built for AI agents (MCP + x402 payments)

This actor is agent-native:

• MCP ready. Callable as a tool by Claude, ChatGPT, and Cursor through the Apify MCP server, no extra setup. Your agent asks for Reddit leads in plain language and gets back scored, structured results.
• x402 agentic payments. Because this actor uses pay-per-event pricing, an AI agent can run and pay for it autonomously in USDC on Base, with no Apify account and no API key, paying per use over HTTP via the open x402 protocol. Perfect for autonomous lead-gen agents that find and act on prospects without a human in the loop.

🔌 Integrations

Output is a standard Apify dataset: export to JSON, CSV, Excel, pipe into n8n, Zapier, Make, or wire into your own stack via the Apify API. Schedule daily runs to catch leads while they are still fresh.

❓ FAQ

Do I need a Reddit account or API key? No. Public data only.

Why residential proxies? Reddit hard-blocks datacenter IPs. Residential is required for reliable results and is included in the price above.

Is this against Reddit's terms? It reads only public pages, takes no in-Reddit actions (no posting, voting, or DMing), and paces requests politely. Use the leads to engage authentically, not to spam.

How accurate are the leads? Default scoring runs two tiers with no key: a lexicon filter plus a local semantic gate that checks a post is genuinely about your product, not just sharing a keyword. This catches most false positives (like "time tracking" in a car forum). For the sharpest read on ambiguous posts, set scoringMode: llm with your own key to add a third LLM tier, which only runs on posts that already passed the first two.

Can an AI agent run and pay for this by itself? Yes. It supports x402, so an agent can pay per run in USDC on Base with no Apify account or API key. It is also exposed as an MCP tool for Claude, ChatGPT, and Cursor.

What if a run finds nothing? You pay nothing. No leads, no charge.

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Made by mick_. Companion to Reddit Scraper, use that for raw data at scale, this for ready-to-act leads.