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

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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:
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Basic sentiment: positive/negative/neutral classification

Level 2: Semantic Analysis (What They Really Mean)

Reading between the lines:
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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

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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

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Customer Persona Building

From review language patterns, identify buyer segments:

Segment 1: Core buyers (most reviews)
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Segment 2: Edge case buyers (cause most problems)
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Segment 3: Surprise buyers (unexpected use cases)
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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:
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Marketing Message Extraction

The best marketing copy comes directly from customer words:

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Sentiment Evolution Analysis

Compare early reviews vs. recent reviews:
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Workspace

Creates ~/voc-intelligence/ containing:

• - analyses/ — full VoC reports per product
• INLINECODE2 — customer persona profiles
• INLINECODE3 — pain point matrices
• INLINECODE4 — extracted marketing messages
• INLINECODE5 — jobs-to-be-done frameworks

Output Format

Every VoC analysis outputs:

1. 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