n8n Reddit Monitoring: Score Buyer-Intent Posts Automatically

Most Reddit monitoring setups answer the wrong question. They tell you that a keyword appeared. F5Bot pings your inbox, a dashboard tallies mentions, and you're left reading hundreds of posts to find the three where someone's actually asking which tool to buy. That's monitoring as a firehose, not as a lead source.

n8n Reddit monitoring can be built to do the filtering for you. n8n is an open-source automation tool where you connect nodes on a canvas, and a Reddit-scanning workflow can fetch posts, score each one for buyer intent with AI, draft a reply, and hand you only the warm ones. The difference between "47 new mentions" and "3 people asking for a recommendation right now" is the scoring step.

This post builds that scoring pipeline. The part the popular guides skip.

What you can automate

• Scanning multiple subreddits on a schedule for posts matching your buyer-intent keywords
• Scoring each matched post 1 to 10 so you focus only on the warm and hot ones
• Drafting a helpful, non-promotional reply for every high-scoring post
• Filtering out everything below your minimum score so the noise never reaches you
• Logging opportunities to a Google Sheets review queue for team triage
• Pinging Telegram or email the moment a strong opportunity lands

The official n8n template "Reddit monitoring with AI sentiment analysis" (n8n.io) is well built, but it's pointed at product feedback and growth insights, not sales. Its AI does topic categorization and sentiment tagging, and it explicitly targets "product, marketing, sales, support, and leadership" intel rather than lead qualification. The BrowseAct 9-step guide (browseract.com) has the same blind spot: its AI tags relevance and category, with no buyer-intent scoring and no reply drafts. Passive monitoring, both of them.

The Reddit opportunity pipeline

Schedule Trigger → Fetch subreddit posts → Keyword filter
   → OpenAI (score 1–10 + reason) → Filter (score ≥ threshold)
   → OpenAI (draft reply) → Log to Sheets → Telegram alert

Two AI calls, not one. The first decides if a post is worth your time. The second writes the opening line so you don't start from a blank box.

1. Fetch on a schedule

A scheduleTrigger v1.2 node fires hourly. An httpRequest v4.2 node pulls recent posts from each target subreddit's public listing. Reddit's read-only endpoints don't need a paid tier for this volume, though heavier scanning should register a Reddit app to stay on the right side of the rate limits. Keep the subreddit list, keywords, and score threshold in a single Config (Set) node so you can retune without editing logic.

2. Filter by intent keywords

Before spending a token on AI, drop the obvious misses. A Filter node keeps posts whose text contains buyer-signal phrases: "looking for", "recommend", "best tool", "alternative to", "anyone using". This pre-filter is cheap and it cuts your OpenAI bill hard. Scoring every post in r/SaaS hourly adds up fast otherwise.

3. Score for buyer intent

This is the step both ranking guides leave out. Feed each surviving post to the @n8n/n8n-nodes-langchain.openAi v2.1 node and prompt it to score the post 1 to 10 on how strongly it signals a real purchase decision, and to return a one-line reason. The node returns its output at $json.output[0].content[0].text. Parse that with a Code node into a numeric score field. A vague "this is automation-related" gets a 3; "we're evaluating three tools this week, what do you use" gets a 9.

4. Filter to the warm ones

A second Filter node keeps only posts at or above your threshold (start at 7). Everything else is logged but never alerts you. This is the line between a workflow that helps and one that becomes another inbox you ignore.

5. Draft the reply, then alert

For each high-scoring post, a second OpenAI call drafts a genuinely helpful reply that answers the question first and mentions your product only if it fits. A Google Sheets node logs the opportunity with score, reason, and draft. A Telegram node sends the summary so a human can post the reply in seconds. You're reviewing, not writing.

Implementation patterns

Pattern 1: Keyword pre-filter before AI. Never send raw subreddit output straight to the model. A Filter node on buyer-signal phrases removes 80% of posts for free. The remaining slice is what you pay to score. Skipping this is the fastest way to a surprising OpenAI invoice.

HTTP Request (subreddit) → Filter (intent phrases) → OpenAI (score)

Pattern 2: Score-gated reply drafting. The score field gates the second AI call. Posts under threshold get logged for trend data but never trigger a draft or an alert. This is the active-engagement layer the official template doesn't include, since it stops at sentiment.

OpenAI (score) → Filter (score ≥ 7) → OpenAI (draft reply) → Telegram

Pattern 3: Review queue plus instant alert. Two destinations from one branch. Google Sheets is the durable record your team triages weekly; Telegram is the real-time nudge for the rare 9-out-of-10 post you'd want to answer within the hour. Fast-moving threads go cold quickly, so the alert matters.

n8n nodes you'll use most

Getting started

1. Run n8n (self-hosted or Cloud) and create an empty workflow.
2. Add a Config (Set) node holding your subreddits, intent keywords, and minimum score.
3. Wire a scheduleTrigger to an httpRequest node that fetches each subreddit's recent posts.
4. Add a Filter node on your intent phrases to cut volume before any AI runs.
5. Add the OpenAI scoring node and a Code node to extract the numeric score.
6. Add a second Filter on the threshold, then a second OpenAI node to draft replies.
7. Finish with a Google Sheets log and a Telegram alert, then test against one busy subreddit.

The scoring prompt and threshold logic are laid out in detail on the Community Opportunity Scanner page if you'd rather start from a working example.

The score-then-act pattern carries straight over to broader competitor monitoring automation and the demand-sensing side of market research automation. Same scan, same AI-scoring backbone, different signal. For founders mining forums for product gaps rather than leads, the Market Trend Analyzer points the same machinery at Hacker News and Reddit r/SaaS.