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product-appeal-analyzer

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Product Appeal Analyzer

产品吸引力分析器

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.
判断用户是否想要一款产品——而非仅仅使用它。本工具是摩擦分析的补充。
核心洞察：用户不会选择“最好”的产品，他们会选择那种感觉专为自己打造的产品。

When to Use

适用场景

✅ 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
✅ 适用场景：
  • 评估着陆页、产品详情页、应用商店列表
  • 竞品对比下的产品定位
  • 信息传递、语气风格、视觉品牌方向的打磨
  • 评估与目标用户画像的情感共鸣
  • 上线前的「这款产品能转化吗？」分析
❌ 不适用场景：
  • UX摩擦审计（→ 请使用ux-friction-analyzer）
  • 视觉设计落地（→ 请使用web-design-expert）
  • A/B测试实施（→ 请使用frontend-developer）
  • 市场规模估算或财务预测
  • 功能对比矩阵

The Desirability Triangle

吸引力三角模型

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 ElementUser 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.
三个要素必须同时具备，缺失任意一个都会扼杀转化：
                    IDENTITY FIT
                    "这是为像我这样的人设计的"
                         /\
                        /  \
                       /    \
                      /  ★   \
                     / 吸引力 \
                    /          \
                   /______________\
        问题               信任
        紧迫性               信号
   "我现在就需要这个"     "这个产品真的能管用"
缺失要素用户反应
身份契合度"看起来有用，但不是给我用的"
问题紧迫性"挺酷的，也许以后会用"
信任信号"看起来不靠谱/好得不像真的"
决策树：分析时，为每个顶点打1-10分。若任意一项得分＜5，即为优先优化项。

Quick Analysis: The 5-Second Test

快速分析：5秒测试

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:
ResultScoreAction
All 4 clear in <3 sec9-10Ship it
All 4 clear in 3-5 sec7-8Minor polish
3 of 4 clear5-6Fix the gap
2 or fewer clear2-4Significant rework
Confusing/unclear0-1Start over
用户着陆后5秒内，应该能明确：
  1. 这是什么？（品类识别）
  2. 是给谁用的？（身份信号）
  3. 核心承诺是什么？（价值主张）
  4. 我接下来该做什么？（清晰的CTA）
测试方法：
  • 向不熟悉产品的人展示着陆页，时长恰好5秒
  • 隐藏页面后询问：「这是什么？给谁用的？你会在上面做什么？」
  • 如实记录回答——不要引导或解释
评分标准：
测试结果得分行动建议
3秒内明确所有4项9-10直接上线
3-5秒内明确所有4项7-8小幅优化
明确其中3项5-6修复缺失项
仅明确2项及以下2-4大幅重构
完全困惑/不清晰0-1重新设计

Analysis Process

分析流程

Step 1: Identify Target Personas

步骤1：明确目标用户画像

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
针对每个用户画像，记录：
  • 用户是谁：一句话描述
  • 核心问题：存在什么痛点+带来的感受
  • 当前解决方案：他们现在的做法（以及为什么不好用）
  • 身份认同：他们如何看待自己，想要成为什么样的人

Step 2: Score the Desirability Triangle

步骤2：为吸引力三角模型评分

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]
针对每个用户画像：
用户画像: [名称]

身份契合度                    [/10]
  视觉品牌匹配度         [/10]  "这个工具看起来符合我的风格吗？"
  语言共鸣度            [/10]  "他们说的是我能听懂的话吗？"
  隐含用户匹配度        [/10]  "展示的用户是否和我类似？"

问题紧迫性                 [/10]
  痛点识别度       [/10]  "他们理解我的问题"
  情感共鸣度           [/10]  "他们懂这种挫败感"
  解决方案清晰度              [/10]  "我明白这个产品如何解决问题"

信任信号                   [/10]
  专业度执行        [/10]  "看起来很正规"
  社交证明                  [/10]  "和我类似的人也在使用"
  风险降低                [/10]  "如果没用怎么办？"

整体吸引力得分:           [/90]

Step 3: Map Objections

步骤3：梳理用户异议

ObjectionTypeHow 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]
异议内容类型如何解决？
"这靠谱吗？"信任[解决方案]
"我之前试过类似的产品"怀疑[解决方案]
"太贵了"价值[解决方案]
"太复杂了"成本[解决方案]
"不是给我用的"身份[解决方案]
"如果没用怎么办？"风险[解决方案]
"我以后再弄"紧迫性[解决方案]

Step 4: Generate Recommendations

步骤4：生成优化建议

Use priority formula: 
Impact = (Users Affected × Severity) / Fix Difficulty
Categorize into:
  • Immediate (ship this week)
  • Medium-term (this sprint)
  • Long-term (roadmap)
使用优先级公式：
影响度 = （受影响用户数 × 问题严重程度） / 修复难度
将建议分为三类：
  • 立即优化（本周上线）
  • 中期优化（当前迭代）
  • 长期优化（纳入 roadmap）

Common Anti-Patterns

常见反模式

Feature Soup Headline

功能堆砌式标题

Novice thinking: "List all capabilities to show value"
Reality: Visitors scan for 2-3 seconds. Feature lists feel generic.
What to use instead:
BadGood
"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"
新手思路：「列出所有功能以体现价值」
实际情况：用户只会浏览2-3秒，功能列表会显得通用且无重点。
替代方案：
反面案例正面案例
"AI驱动的恢复规划工具，内置分析功能""在恢复过程中明确知道下一步该做什么"
"全面的法律文件平台""2分钟内查清你的记录是否可以消除"
识别方法：标题包含3个以上名词，或带有「AI驱动」「全面」「平台」等 buzzword

Screenshot Hero

截图式首屏

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
新手思路：「展示产品界面，让用户知道他们会得到什么」
实际情况：陌生人看不懂你的UI，他们只关心结果。
替代方案：
  • 用户获得收益后的场景
  • 他们能得到的结果/改变
  • 抽象化的转变可视化
识别方法：首屏图片是无上下文的产品截图

Trust Ladder Violation

信任阶梯违规

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
新手思路：「先获取邮箱，再转化用户」
实际情况：信任是逐步建立的。过早索要信息会扼杀转化。
信任阶梯（每一级都需要更多信任）：
  1. 着陆页面 → 专业设计，无破损元素
  2. 点击/浏览 → 导航清晰，加载快速
  3. 停留＞2分钟 → 展示价值，进度清晰
  4. 填写信息 → 说明索要原因，无暗模式
  5. 创建账号 → 隐私政策可见，字段最少，收益明确
  6. 支付费用 → 有保障，有 testimonial，支付渠道可信
识别方法：在展示价值前就要求用户创建账号

Identity Mismatch

身份不匹配

Novice thinking: "Broad appeal = more users"
Reality: When everyone is the target, no one feels targeted.
What to use instead:
Signal TypeHow It Works
Visual identityDark mode = "power user"; Soft pastels = "wellness"
Language/tone"Crush your goals" vs "Find your balance"
Social proofCompany logos vs individual testimonials
ComplexityMinimal = simplicity-seeker; Feature-rich = power user
Detection: Homepage tries to appeal to 3+ different personas
新手思路：「受众越广，用户越多」
实际情况：当所有人都是目标用户时，没有人会觉得自己是目标用户。
替代方案：
信号类型实现方式
视觉品牌深色模式 =「专业用户」；柔和马卡龙色 =「健康养生」
语言/语气「达成目标」vs「找到平衡」
社交证明企业logo vs 个人 testimonial
复杂度极简 =「追求简单的用户」；功能丰富 =「专业用户」
识别方法：首页试图吸引3种及以上不同的用户画像

Self-Contained Tools

内置工具

Analysis Workflow

分析工作流

  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
  1. 阅读着陆页的内容与结构
  2. WebFetch 目标URL以分析实时内容
  3. 撰写分析结果并保存为 markdown 文件
  4. 编辑建议，转化为可落地的文案修改方案

Appeal Scorer Script

吸引力评分脚本

Run: 
python scripts/appeal_scorer.py <url>
Produces structured JSON output with scores and recommendations.
运行命令：
python scripts/appeal_scorer.py <url>
输出结构化JSON结果，包含得分与优化建议。

Reference Files (See for deep dives)

参考文件（深入学习）

FileWhen 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
文件使用场景
references/scoring-templates.md
完整评分矩阵与模板
references/trust-ladder.md
信任建立阶段的深度解析
references/identity-signals.md
视觉/语言身份信号目录
references/objection-catalog.md
按产品类型分类的常见异议

Output Format

输出格式

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
运行本工具时，需生成以下内容：
  1. 执行摘要 - 3条核心发现
  2. 吸引力三角模型得分 - 按用户画像分类
  3. 5秒测试评估 - 明确的信息与缺失的信息
  4. Top 3 用户异议 - 及解决方法
  5. 优先级优化建议 - 立即/中期/长期

Integration with ux-friction-analyzer

与ux-friction-analyzer的集成

Appeal + Friction = Complete picture
This Skill Answersux-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.
吸引力 + 摩擦 = 完整的产品评估
本工具解答ux-friction-analyzer解答
「用户想要这款产品吗？」「用户能使用这款产品吗？」
用户会选择这款而非竞品吗？用户能完成任务吗？
产品是否感觉专为用户打造？流程是否合理？
承诺是否有吸引力？使用体验是否流畅？
建议同时运行两款工具：高吸引力+高摩擦=沮丧的用户；低摩擦+低吸引力=被放弃的产品。
核心理念：低摩擦但低吸引力的产品会被用户放弃；高吸引力但高摩擦的产品会让用户沮丧。两者缺一不可。