Feature Request Deduplicator

Pro v1.0.0 1 view

Deduplicate and cluster feature requests from multiple sources. Identifies duplicates using semantic similarity, merges related requests, clusters into themes, and reveals true demand signals.

What You Get

Save 2-4 hours per week on manual duplicate detection. Consolidate scattered feedback into clear prioritized demand signals with confidence-scored merge recommendations.

The Problem

Product managers receive feature requests from multiple channels (email, Slack, support tickets, sales calls) where the same feature gets requested in different words. Without deduplication, popular features appear as separate low-priority items, leading to poor roadmap decisions and missed demand signals.

The Solution

Uses semantic analysis to identify duplicate feature requests regardless of phrasing. Clusters related requests, assigns confidence scores for merge recommendations, groups into themes, and generates prioritized lists weighted by request frequency or customer revenue. Flags ambiguous matches for user review rather than auto-merging.

How It Works

  1. 1 Load and validate CSV with feature requests, confirming required columns exist
  2. 2 Perform semantic duplicate detection comparing each request pair for meaning similarity
  3. 3 Generate confidence-scored merge recommendations with reasoning for each cluster
  4. 4 Create theme clusters grouping unique requests into 15-20 high-level categories
  5. 5 Produce deduplicated list, merge log, theme analysis, and enhanced CSV with tracking columns

What You'll Need

  • CSV file with columns: request_id, title, description, source, customer
  • 100-500 feature requests for optimal analysis quality
  • English language requests

Get This Skill

Requires Pro subscription ($9/month)

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Examples

Ambiguous Case Handling

Tests edge case where all 4 requests relate to 'notifications' but target different channels (mobile, Slack, email, real-time). Demonstrates proper ambiguous case flagging at 72% confidence rather than auto-merging, recommending manual review.

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Basic Duplicate Detection

Simple 3-request example demonstrating core duplicate detection. Identifies that 'Slack integration' and 'Integrate with Slack' are semantically identical while keeping 'Dark mode' separate. Shows confidence scoring and demand ranking.

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Revenue-Weighted Enterprise Prioritization

Demonstrates revenue-weighted analysis with 5 requests totaling $455K. Shows how SSO/SAML requests from enterprise customers ($350K combined) should be prioritized over webhooks ($30K) despite equal request counts. Includes strategic recommendations.

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