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

---

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.

---

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

---

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)

---

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

---

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

---

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

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.

---

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.