version: "1.0.1" name: product-appeal-analyzer description: Evaluate product desirability, market positioning, and emotional resonance—the complement to friction analysis. Assess whether users will WANT a product (not just use it), identity fit, trust signals, and value proposition clarity. Activate on "will they like it", "market positioning", "appeal analysis", "product desirability", "value proposition", "why would someone choose this", "landing page review", "conversion optimization", "messaging strategy". NOT for UX friction analysis (use ux-friction-analyzer), visual design implementation (use web-design-expert), or A/B test setup (use frontend-developer). allowed-tools: Read,Write,Edit,WebFetch category: Research & Analysis tags:

• product-strategy
• marketing
• positioning
• value-proposition
• conversion
• user-research

pairs-with:

• skill: ux-friction-analyzer

reason: Appeal asks "do they want it?" Friction asks "can they use it?" Use both.

• skill: competitive-cartographer

reason: Position against alternatives with strategic mapping

• skill: web-design-expert

reason: Implement visual identity recommendations

Product Appeal Analyzer

Evaluate whether users will want a product—not just use it. The complement to friction analysis.

Core insight: Users don't choose the best product—they choose the product that feels most like it was made for them.

When to Use

✅ Use for:

• Evaluating landing pages, product pages, app store listings
• Positioning a product against alternatives
• Crafting messaging, tone, visual identity direction
• Assessing emotional resonance with target personas
• Pre-launch "will this convert?" analysis

❌ NOT for:

• UX friction audits (→ use ux-friction-analyzer)
• Visual design execution (→ use web-design-expert)
• A/B test implementation (→ use frontend-developer)
• Market size estimation or financial forecasting
• Feature comparison matrices

The Desirability Triangle

All three must be present. Missing any one kills conversion:

                    IDENTITY FIT
                    "This is for people like me"
                         /\
                        /  \
                       /    \
                      /  ★   \
                     / DESIRE \
                    /          \
                   /______________\
        PROBLEM               TRUST
        URGENCY               SIGNALS
   "I need this now"     "This will actually work"

Decision tree: When analyzing, score each vertex 1-10. If any is <5, that's your priority fix.

Quick Analysis: The 5-Second Test

Within 5 seconds of landing, a visitor should know:

1. What is this? (Category recognition)
2. Who is it for? (Identity signal)
3. What's the core promise? (Value proposition)
4. What do I do next? (Clear CTA)

How to run it:

• Show landing page to someone unfamiliar for exactly 5 seconds
• Hide it, then ask: "What was that? Who's it for? What would you do there?"
• Record verbatim—don't coach or clarify

Scoring:

Analysis Process

Step 1: Identify Target Personas

For each persona, document:

• Who: One-sentence description
• Problem: What's broken + how it feels
• Current workaround: What they do today (and why it sucks)
• Identity: How they see themselves, who they want to become

Step 2: Score the Desirability Triangle

For each persona:

PERSONA: [Name]
 
IDENTITY FIT                    [/10]
  Visual identity match         [/10]  "Does this look like my kind of tool?"
  Language resonance            [/10]  "Do they speak my language?"
  Implied user match            [/10]  "Are people like me shown?"
 
PROBLEM URGENCY                 [/10]
  Pain point acknowledged       [/10]  "They understand my problem"
  Emotional resonance           [/10]  "They get how frustrating it is"
  Solution clarity              [/10]  "I see how this fixes it"
 
TRUST SIGNALS                   [/10]
  Professional execution        [/10]  "This looks legitimate"
  Social proof                  [/10]  "Others like me use it"
  Risk reduction                [/10]  "What if it doesn't work?"
 
OVERALL APPEAL SCORE:           [/90]

Step 3: Map Objections

Step 4: Generate Recommendations

Use priority formula: Impact = (Users Affected × Severity) / Fix Difficulty

Categorize into:

• Immediate (ship this week)
• Medium-term (this sprint)
• Long-term (roadmap)

Common Anti-Patterns

Feature Soup Headline

Novice thinking: "List all capabilities to show value"

Reality: Visitors scan for 2-3 seconds. Feature lists feel generic.

What to use instead:

Detection: Headline contains 3+ nouns or buzzwords like "AI-powered", "comprehensive", "platform"

Screenshot Hero

Novice thinking: "Show the product interface so people know what they're getting"

Reality: Strangers don't understand your UI. They care about outcomes.

What to use instead:

• Person experiencing the benefit
• The outcome/result they'll get
• Abstract visualization of the transformation

Detection: Hero image is a product screenshot with no context

Trust Ladder Violation

Novice thinking: "Get their email immediately, then convert them"

Reality: Trust builds in stages. Asking for too much too early kills conversion.

The Trust Ladder (each rung requires more trust):

1. Land on page → Professional design, no broken elements
2. Click/explore → Clear navigation, fast load
3. Spend >2 min → Demonstrated value, clear progress
4. Enter info → Why you need it explained, no dark patterns
5. Create account → Privacy visible, minimal fields, clear benefit
6. Pay money → Guarantee, testimonials, recognizable processor

Detection: Asking for account creation before demonstrating value

Identity Mismatch

Novice thinking: "Broad appeal = more users"

Reality: When everyone is the target, no one feels targeted.

What to use instead:

Detection: Homepage tries to appeal to 3+ different personas

Self-Contained Tools

Analysis Workflow

1. Read the landing page content and structure
2. WebFetch the target URL to analyze live content
3. Write analysis results to a markdown file
4. Edit recommendations into actionable copy changes

Appeal Scorer Script

Run: python scripts/appeal_scorer.py <url>

Produces structured JSON output with scores and recommendations.

Reference Files (See for deep dives)

Output Format

When running this skill, produce:

1. Executive Summary - 3 bullet key findings
2. Desirability Triangle Scores - Per persona
3. 5-Second Test Assessment - What's clear, what's not
4. Top 3 Objections - And how to address them
5. Priority Recommendations - Immediate / Medium / Long-term

Integration with ux-friction-analyzer

Appeal + Friction = Complete picture

Run both: High appeal + high friction = frustrated users. Low friction + low appeal = abandoned product.

Philosophy: A product with low friction but low appeal gets abandoned. A product with high appeal but high friction gets frustrated users. You need both.