ai-voc-review-insights

# AI VoC Review Intelligence Deep AI-powered Voice of Customer analysis — go beyond basic sentiment to extract purchase motivations, hidden pain points, unmet needs, and product-market fit signals from customer reviews across any platform. ## Commands ``` voc analyze <reviews> # full VoC analysis of review set voc pain-points <reviews> # extract and rank customer pain points voc motivations <reviews> # identify purchase motivations voc unmet-needs <reviews> # find unserved customer needs voc personas <reviews> # build customer persona from reviews voc jobs-to-be-done <reviews> # JTBD analysis from review language voc compare <reviews1> <reviews2> # compare VoC between two products voc opportunity <reviews> # identify product development opportunities voc marketing <reviews> # extract marketing messages from reviews voc report <product> # full VoC intelligence report ``` ## What Data to Provide - **Reviews** — paste 20-200 customer reviews (more = better analysis) - **Star distribution** — 1-5 star count breakdown - **Product category** — context for benchmarking - **Competitor reviews** — for comparative VoC analysis - **Your marketing copy** — to align with customer language ## VoC Analysis Framework ### Level 1: Surface Analysis (Standard Review Analysis) **What customers say explicitly:** ``` "The product is great quality" "Arrived quickly" "Easy to assemble" "A bit expensive but worth it" ``` Basic sentiment: positive/negative/neutral classification ### Level 2: Semantic Analysis (What They Really Mean) **Reading between the lines:** ``` Review: "Exactly what I needed" → Unmet need was real, product solves it Review: "Better than I expected" → Category has history of disappointing products Review: "I was skeptical but..." → High purchase anxiety in this category Review: "Bought this as a gift" → Gifting is a significant use case Review: "Replaced my old [brand]" → Competitor switching signal Review: "My husband/wife loves it" → Multi-person household use Review: "Works in my [specific context]" → Niche use case validation ``` ### Level 3: Jobs-to-be-Done (JTBD) Analysis **Functional jobs** (what they hire the product to do): - "I need to [task]" - Extract the core functional use from review language **Emotional jobs** (how they want to feel): - "I feel confident/safe/proud/excited when..." - Extract emotional outcomes from positive reviews **Social jobs** (how they want to be perceived): - "My [guests/family/colleagues] noticed..." - Extract social signaling from reviews ``` JTBD template from reviews: When I [situation], I want to [motivation], so I can [outcome]. Example from reviews of a standing desk converter: When I work from home all day, I want to avoid back pain, so I can stay productive without discomfort. → Marketing message: "Work pain-free all day. Designed for the modern home office." ``` ### Pain Point Extraction Matrix Extract all pain points and classify: **Dimension 1: Frequency** - Mentioned in >20% of reviews: Critical issue - Mentioned in 10-20%: Significant issue - Mentioned in 5-10%: Notable issue - Mentioned in <5%: Edge case **Dimension 2: Intensity** - "Terrible", "awful", "destroyed", "complete waste": Severity 5 - "Disappointed", "frustrated", "annoyed": Severity 4 - "Could be better", "wished it had": Severity 3 - "Minor issue", "small complaint": Severity 2 - Implied, not stated directly: Severity 1 **Dimension 3: Resolution Potential** - Product redesign needed: Hard (3-6 months) - Listing/instruction update: Easy (<1 week) - Packaging/insert improvement: Medium (2-4 weeks) - Customer service response: Immediate ``` Pain Point Matrix: Pain Point Freq Intensity Resolution Priority Instructions unclear 18% 3 Easy HIGH Strap breaks easily 12% 5 Hard HIGH Bag smaller than shown 9% 4 Listing fix MEDIUM Color slightly off 6% 2 Listing fix LOW ``` ### Customer Persona Building From review language patterns, identify buyer segments: **Segment 1: Core buyers (most reviews)** ``` Demographics: [infer from review context] Trigger: [what prompted purchase] Use case: [primary use] Success metric: [what makes them happy] Quote: "[representative review excerpt]" ``` **Segment 2: Edge case buyers (cause most problems)** ``` Demographics: [who writes the negative reviews] Mismatch: [how product doesn't meet their expectations] Fix: [listing change to filter them out or meet their needs] ``` **Segment 3: Surprise buyers (unexpected use cases)** ``` Discovery: [how they found your product] Use case: [unexpected application] Opportunity: [new marketing angle or product variation] ``` ### Purchase Motivation Analysis Extract why people buy, beyond the obvious: **Rational motivators** (stated reasons): - Quality, price, functionality, specifications **Emotional motivators** (unstated reasons): - Status, identity, relationships, fear/risk reduction - Safety ("my child will be safe") - Belonging ("everyone in our community uses this") - Achievement ("I finally solved this problem") **Trigger events** (what caused the purchase NOW): - "After moving to a new home" - "Since working from home" - "After my old one broke" - "Doctor recommended" - "Saw on TikTok" ### Unmet Needs Identification Find gaps in the market from review language: **Explicit unmet needs:** - "I wish it came in [X]" - "Would be perfect if it also [function]" - "Need something like this but for [use case]" **Implicit unmet needs** (inferred from workarounds): - "I had to [work around]" → product doesn't do X natively - "It would help if..." → feature request pattern - Comparisons to competitors: what competitor does better ### Competitive Switching Signals From reviews mentioning competitors: ``` "Switched from [Brand X]" → X is your direct competitor "Better than [Brand X]" → X is in buyer's consideration set "[Brand X] stopped working, got this" → X has quality issues "Half the price of [Brand X]" → X is premium alternative ``` ### Marketing Message Extraction The best marketing copy comes directly from customer words: ``` Reviews say: → Marketing copy: "Finally found one that..." → "The [product] you've been searching for" "Works exactly as advertised" → "What you see is what you get" "Gift for my husband, he loves it" → "The gift he'll actually use" "Solved my [problem]" → "[Problem]? Problem solved." "Worth every penny" → "Invest in quality. Feel the difference." ``` ### Sentiment Evolution Analysis Compare early reviews vs. recent reviews: ``` Early reviews (product launch): Focus on unboxing, first impressions Recent reviews (mature product): Focus on durability, long-term value Declining sentiment pattern: Early avg: 4.5 stars → Recent avg: 3.9 stars Signal: Quality or supplier change, investigate manufacturing ``` ## Workspace Creates `~/voc-intelligence/` containing: - `analyses/` — full VoC reports per product - `personas/` — customer persona profiles - `pain-points/` — pain point matrices - `marketing/` — extracted marketing messages - `jtbd/` — jobs-to-be-done frameworks ## Output Format Every VoC analysis outputs: 1. **VoC Executive Summary** — 5 key findings in plain language 2. **Pain Point Matrix** — all pain points scored by frequency × intensity 3. **JTBD Framework** — functional, emotional, and social jobs identified 4. **Customer Personas** — 2-3 buyer segments with profiles 5. **Unmet Needs List** — product/feature gaps discovered 6. **Marketing Messages** — 5 ready-to-use copy lines from customer language 7. **Competitor Switching Map** — which competitors appear and in what context 8. **Product Roadmap Signals** — prioritized improvements by business impact