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.