Reddit Brand Mention Monitor

An Apify-ready Python Actor that scans one subreddit for a target brand plus competitors, then returns a ranked attention queue of complaints, comparisons, purchase intent, and switching signals.

Store-ready positioning

Who this is for

• SaaS founders validating positioning, churn risk, and competitor pull
• product and support teams that want complaint and switching signals before they spread
• agencies and growth operators building lightweight brand-monitoring workflows without a full social-listening stack

Core promise

Give me one subreddit, one brand, and a competitor set — I return the posts most likely to matter for retention, positioning, and conversion.

Buyer personas

• Founder / PM: wants fast signal on complaints, feature frustration, and competitive pull
• Support lead: wants a shortlist of recurring pain points before tickets or churn rise further
• Growth / research operator: wants comparison and switching language that can feed copy, landing pages, or sales research

ROI angle

• find complaints before they snowball into churn or reputation drag
• catch competitor pull when users openly compare alternatives
• harvest real buyer language instead of guessing positioning from internal meetings

Why this is more sellable than a generic AI wrapper

• one narrow input domain: a single subreddit
• one clear use case: brand and competitor monitoring
• one output shape buyers understand: ranked attention items
• pay-per-event pricing can map to one attention item = one monetizable event

What it does

• fetches recent posts from arctic-shift.photon-reddit.com
• matches a target brand, aliases, and competitor names inside posts
• samples comments for lightweight confirmation and sentiment context
• classifies each matched post into complaint, purchase_intent, comparison, switching_intent, or general_mention
• scores each item by urgency and commercial relevance
• pushes one dataset item per ranked attention item
• exposes a custom charge event name: attention-item-found

Inputs

Field	Purpose
subreddit	subreddit name without /r/
brand	primary brand or product to monitor
aliases	alternative names or short forms for the brand
competitors	alternatives you want compared against
postLimit	number of recent posts to scan
commentsPerPost	comments sampled per matched post
minAttentionScore	drop low-value items below this threshold
maxItems	cap dataset output size
minPostScore	ignore low-score posts
painKeywords	complaint/friction signals
intentKeywords	buying/evaluation signals
comparisonKeywords	side-by-side evaluation signals
switchingKeywords	migration/replacement signals

Output shape

Each dataset item includes fields like:

• brand
• entityName
• entityType
• mentionType
• attentionScore
• urgencyLabel
• confidence
• matchedCompetitors
• score
• commentCount
• title
• url
• commentSnippets
• summary

The run-level summary includes:

• attentionItemCount
• matchedPostCount
• typeCounts
• topEntities
• executiveSummary

Practical Store listing angle

Better positioning:

• Reddit brand mention monitor for SaaS and AI tools
• competitor comparison radar for product teams
• complaint and switching-intent scout for churn prevention
• founder attention queue for fast-moving startups

Known limitations

• depends on Arctic Shift availability rather than official Reddit API access
• classification is keyword-based and intentionally lightweight
• best for fast monitoring and triage, not final market research by itself
• noisy subreddits may need threshold and keyword tuning

Pricing

This Actor is designed around Pay Per Event pricing so buyers pay for returned attention items rather than vague AI output.

Example use cases

Example 1: AI assistant monitoring

• subreddit: ChatGPT
• brand: ChatGPT
• competitors: Claude, Gemini
• good for: tracking reliability complaints, switching intent, and side-by-side model comparisons

Example 2: developer-tool monitoring

• subreddit: Cursor
• brand: Cursor
• competitors: Windsurf, Claude Code
• good for: spotting frustration, migration intent, and competitor pull in coding-tool workflows

What makes it useful

• surfaces the most actionable posts instead of a raw feed dump
• highlights buying, comparison, and switching language buyers actually care about
• works well for manual review, support triage, founder research, and downstream automation