curiositech/product-appeal-analyzer

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

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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"

| Missing Element | User Reaction | |-----------------|---------------| | Identity Fit | "Seems useful, but not for me" | | Problem Urgency | "Cool, maybe someday" | | Trust Signals | "Looks sketchy / too good to be true" |

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

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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:

| Result | Score | Action | |--------|-------|--------| | All 4 clear in <3 sec | 9-10 | Ship it | | All 4 clear in 3-5 sec | 7-8 | Minor polish | | 3 of 4 clear | 5-6 | Fix the gap | | 2 or fewer clear | 2-4 | Significant rework | | Confusing/unclear | 0-1 | Start over |

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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

| Objection | Type | How Addressed? | |-----------|------|----------------| | "Is this legit?" | Trust | [Answer] | | "I've tried things before" | Skepticism | [Answer] | | "Too expensive" | Value | [Answer] | | "Too complicated" | Effort | [Answer] | | "Not for people like me" | Identity | [Answer] | | "What if it doesn't work?" | Risk | [Answer] | | "I'll do it later" | Urgency | [Answer] |

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)

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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: | Bad | Good | |-----|------| | "AI-Powered Recovery Planning Tool with Analytics" | "Know exactly what to do next in your recovery" | | "Comprehensive Legal Document Platform" | "Find out in 2 minutes if your record can be expunged" |

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: | Signal Type | How It Works | |-------------|--------------| | Visual identity | Dark mode = "power user"; Soft pastels = "wellness" | | Language/tone | "Crush your goals" vs "Find your balance" | | Social proof | Company logos vs individual testimonials | | Complexity | Minimal = simplicity-seeker; Feature-rich = power user |

Detection: Homepage tries to appeal to 3+ different personas

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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)

| File | When to Use | |------|-------------| | references/scoring-templates.md | Full scoring matrices and templates | | references/trust-ladder.md | Deep dive on trust building stages | | references/identity-signals.md | Visual/verbal identity signal catalog | | references/objection-catalog.md | Common objections by product type |

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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

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Integration with ux-friction-analyzer

Appeal + Friction = Complete picture

| This Skill Answers | ux-friction-analyzer Answers | |--------------------|------------------------------| | "Do they want it?" | "Can they use it?" | | Will they choose this over alternatives? | Can they complete the task? | | Does it feel made for them? | Does the flow make sense? | | Is the promise compelling? | Is the experience smooth? |

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

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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.