Analyze whether end users will discover clear value in product ideas. Use when: discussing product concepts, evaluating features, planning marketing strategies, analyzing user adoption problems, or when the user expresses uncertainty about product direction (e.g., 'is this idea good?', 'what do you think of this?', 'will users want this?', 'why aren't users staying?', 'how should we position this?').
Status: Production Ready ✅
Version: 1.1.4
Last Updated: 2026-02-21
Type: Analytical Framework
Overview
This skill provides a philosophical framework and analytical methods for evaluating whether end users will "know" what value they can achieve through a product. Guides analysis through the lens of value discovery, not by providing checklists.
What this skill provides:
Framework to evaluate product ideas when certainty is lacking
Analysis methods for assessing end user value discovery
Patterns from real product successes and failures
Analysis methods for product design and positioning
Core question: Can end users clearly understand what value they'll realize through the product - even if that value takes time to achieve?
Key terminology:
User: The person using this skill (product creator, PM, designer, entrepreneur, etc.)
Related Skills
End user: The person who will use the product being discussed
Value: What end users achieve through the product (outcomes such as identity, financial gain, capability enhancement, time savings, etc.)
Features: What the product does (technical capabilities)
Core distinction:
Features are not value
Features are what the product can do, value is what end users achieve
Analysis examines whether features translate into end user outcomes
The Central Insight
End users adopt products when they know what value they'll get. This "knowing" is critical:
If end users know they'll achieve something valuable (even long-term), they'll use it
If end users don't know what they'll achieve, they won't use it - no matter how good the product is
What "knowing" means:
End users can explain to themselves or others why they're using the product
End users can describe what they'll achieve (not just what features exist)
End users understand the outcome, even if it takes time to materialize
Observed patterns:
When end users can articulate clear value → higher adoption rates
When end users cannot articulate value → adoption challenges, even with innovative features
Some end users adopt without full clarity, then discover value through use (progressive discovery)
Value types end users seek (but aren't limited to):
Identity and belonging
Financial gain
Short-term benefits
Long-term benefits
Status and recognition
Capability enhancement
Time savings
Problem resolution
The Challenge
Most product creators face a hidden problem: end users often don't know what they actually want, and their stated methods may be wrong.
The job isn't just to build what end users ask for - it's to help end users discover what value they're actually seeking.
How to Engage with This Skill
This skill operates through conversational analysis. When the user presents a product idea:
Identify the end users - Determine who will use the product
Examine value discovery - Analyze whether end users will understand what they'll achieve
Evaluate through four dimensions - Value clarity, timeline, perception, discovery
Consider context - Each product, market, and end user group differs
This framework guides thinking. It does not prescribe solutions.
Analysis approach:
Must complete analysis of all four dimensions, each dimension as independent section
For each dimension:
Provide status assessment using status indicators (🔴🟡🟢) with specific description of current state (not generic)
Explain the analytical reasoning for this dimension (why this dimension matters for this product)
Systematically apply the dimension's analytical methods to the product idea (cannot skip analysis and jump to questions)
When citing real product cases, base on verifiable information and explain relevance to current product
Pose sharp questions that directly challenge product necessity or require comparison with existing solutions
After completing all four dimensions, provide summary
Avoid logical gaps, show complete reasoning chain
Guide users to make decisions based on analysis
Analysis Framework
When the user discusses a product idea, analyze these four dimensions to evaluate whether end users will discover value:
1. Value Clarity
Examine:
Can end users articulate what they'll achieve?
Is the value proposition clear or vague to end users?
Do end users understand the outcome, not just the features?
Why this matters:
End users won't adopt a product if they can't explain to themselves (or others) why they're using it.
Real example - Dropbox (see references/real-cases.md for detailed data):
Clear value to end users: "I can access my files from any device"
End users immediately understood what they'd achieve
Not about "cloud storage" (technical) but about "access anywhere" (value)
Insight: Translate technical features into user-facing value
Real example - Google Wave (see references/real-cases.md for detailed analysis):
Vague value to end users: "Unified communication"
End users couldn't explain what they'd achieve
Failed despite innovative features
Lesson: Features without clear value = no adoption
Analysis method:
Ask: What would an end user say when asked "Why are you using this?" If the answer is unclear or feature-focused ("because it has X"), dig deeper into the actual value proposition.
2. Value Timeline
Examine:
Is the value immediate or delayed for end users?
If delayed, do end users know it's coming?
What keeps end users engaged during the journey?
Why this matters:
Both short-term and long-term value are valid approaches. The choice depends on the product's nature, specific scenarios, and end user context. Neither is inherently superior.
Short-term value products (end users see results in minutes/hours):
Dropbox: Upload → see file on other device (< 5 minutes)
Zoom: Click link → join meeting (< 30 seconds)
Stripe: Run test payment → see it work (< 1 minute)
Consideration: Immediate value is the complete offering
Long-term value products (end users see results in weeks/months):
Duolingo: Language fluency (6-12 months)
Fitness apps: Body transformation (3-6 months)
Investment apps: Wealth building (years)
Consideration: End users commit to the journey
Design approaches available:
Pure short-term: Deliver immediate value, that's the complete product
Pure long-term: End users are committed to the journey, no short-term touchpoints needed
Hybrid: Long-term goal with optional short-term touchpoints (XP, streaks, milestones)
All three approaches are valid - choose based on product nature and end user context
Analysis method:
Identify the primary value timeline. Assess whether the approach matches the product's nature and target end users' expectations. Don't force short-term mechanisms if end users are already committed to long-term goals.
3. Value Perception
Examine:
Can end users see/feel what they achieved?
Is progress tangible or abstract to end users?
Can end users show others what they've accomplished?
Why this matters:
Invisible value feels like no value to end users. Progress must be perceivable.
Note: "Perceivable" takes different forms across product types:
Developer tools: Build outputs, test results, performance metrics
The key is that end users can point to something concrete that shows value was delivered
Visible outcomes for end users:
Dropbox: File appears on other device (tangible)
Instagram: Beautiful photo with likes (tangible)
GitHub: Contribution graph (tangible)
Duolingo: Streak counter (tangible)
Observation: Achievements that are visible and shareable
Invisible outcomes (problematic for end users):
"Your data is synced" (abstract, can't see it)
"Security improved" (no visible change)
"Algorithm optimized" (nothing looks different)
Observation: Technical improvements need visible manifestations
Analysis method:
Identify what end users can point to and say "I achieved this". If the value is invisible, explore ways to make it tangible through UI, notifications, or progress indicators.
4. Value Discovery
Examine:
Do end users already know they want this?
Or will end users discover the value after using it?
How to help end users discover value they don't yet recognize?
Why this matters:
Sometimes end users don't know what they want until they experience it. The product must help them discover it quickly.
Discovery pattern - Instagram (see references/real-cases.md for growth data):
End users thought they wanted: "Share photos"
End users discovered they valued: "Become a photographer" (identity)
Instagram helped discovery through filters, likes, and social validation
Observation: Instagram's success came from enabling identity transformation, not just photo sharing utility
Discovery pattern - Notion:
End users thought they wanted: "Take notes"
End users discovered they valued: "Become organized" (identity)
Notion helped discovery through flexible databases and templates
Observation: Enable end users to discover their own organizational style
Analysis method:
Determine whether end users already know what they want, or need to discover it. If discovery is needed, identify the fastest path to the "aha" moment through onboarding, tutorials, or progressive feature revelation.
Patterns from Real Products
These aren't rules to follow - they're patterns to consider when analyzing specific situations.
For detailed case studies with real data, see references/real-cases.md (English) or references/real-cases-zh.md (中文).
Pattern: Value Communication
Products using concrete outcome descriptions:
Dropbox: "Access files from any device"
Instagram: "Become a photographer" (identity transformation)
Observation: These products use concrete, achievable outcome descriptions
Products using technical or feature descriptions:
Google Wave: "Unified communication" (technical concept)
Some products: "Cloud storage with 2GB free" (feature list)
Some products: "Distributed file synchronization" (technical jargon)
Observation: These descriptions make it harder for end users to understand what they'll achieve
Real Examples
For complete case studies with metrics and data sources, see references/real-cases.md.
Success: Dropbox
Value to end users: Access files from any device
Timeline: Immediate (< 5 minutes)
End user perception: File visibly appears on other device
Why it worked: Clear, immediate, visible value for end users
Data: 3900% growth in 15 months (see references)
Success: Duolingo
Value to end users: Learn a language (long-term)
Timeline: 6-12 months for fluency
End user perception: XP, streaks, levels (immediate feedback)
Why it works: Long-term goal with optional short-term touchpoints
Data: 500M+ users, 3x retention improvement (see references)
Success: WeChat
Value to end users: Efficient problem-solving (not time-wasting)
Philosophy: "Use and go" - respect end user time
Why it worked: Trust through non-addictive design
Data: 1.3B monthly active users (see references)
Failure: Google Wave
Problem: End users couldn't explain what they'd achieve
Lesson: Features ≠ Value. End users need to know what they'll accomplish.
Data: Shut down 14 months after launch (see references)
Failure: Quibi
Problem: "10-minute videos on mobile" wasn't a value end users recognized
Lesson: End users already had YouTube/TikTok. No clear additional value.
Data: $1.75B funding, shut down in 6 months (see references)
When This Framework Applies
Most applicable for:
Consumer products (B2C)
Competitive markets (end users have alternatives)
Products requiring adoption and retention
New product categories (end users don't know what to expect)
Less applicable for:
Enterprise software (decision makers ≠ end users, switching costs high)
Monopoly products (end users have no choice)
Products where value IS delayed by nature (investing, insurance)
Common Pitfalls
Pitfall 1: Assuming End Users Know What They Want
The trap: Building exactly what end users ask for
The reality: End users often don't know what they actually need
The approach: Help end users discover the real value through conversation and exploration
Pitfall 2: Focusing on Features Instead of Value
The trap: "Our product has X, Y, Z features"
The reality: End users don't care about features, they care about what they'll achieve
The approach: Always translate features into value: "Feature X helps end users achieve Y"
Pitfall 3: Copying Patterns Without Context
The trap: "Duolingo uses streaks, so we should too"
The reality: Streaks work for daily habits, not for episodic use
The approach: Understand why a pattern works for end users, then adapt to specific context
Pitfall 4: Invisible Value
The trap: "Our algorithm is 10x better"
The reality: If end users can't see/feel the improvement, it doesn't matter
The approach: Make value tangible and visible to end users
Research Methodology
Verify Information Accuracy
When citing real product cases, base on verifiable information and explain relevance to current product.
Tool Availability:
WebFetch and WebSearch available for verifying information
When research fails, proceed with analysis based on framework and clearly indicate which information needs verification
Evaluating Case Study Applicability
The cases in references/real-cases.md (Dropbox, Instagram, Duolingo, WeChat, Google Wave, Quibi) illustrate patterns, not universal rules.
Assess applicability:
Product type match: B2C consumer apps vs B2B developer tools vs enterprise software
Market context match: Competitive markets vs niche markets vs monopoly situations
User behavior match: Daily use vs episodic use vs one-time transactions
Value delivery match: Immediate utility vs long-term transformation vs hybrid approaches
When cases don't apply:
If the user's product differs significantly from reference cases (e.g., B2B infrastructure tool vs C2C social app), search for comparable products in the same domain. Analyze those domain-specific examples instead of forcing consumer app patterns onto different contexts.
Example:
User discusses: Developer infrastructure tool (like Temporal, Kubernetes)
Action: Search for similar developer tools, analyze their value propositions, adoption patterns
Avoid: Applying Instagram's identity transformation pattern to infrastructure software
Balancing Exploration and Evidence
Exploratory thinking (appropriate when):
Identifying potential value types end users might seek
Brainstorming ways to make value visible or tangible
Considering multiple positioning approaches
Exploring "what if" scenarios for product direction
Evidence-based analysis (required when):
Claiming specific adoption patterns or metrics
Comparing to real products or market examples
Stating what "works" or "doesn't work" in practice
Conducting analysis based on industry precedents
Process:
Explore possibilities through discussion and brainstorming
When specific claims or comparisons arise, verify with research
Conduct analysis based on verified patterns, not assumptions
Acknowledge when evidence is limited or context differs from known cases
Research Sources
Primary sources (preferred):
Official product websites and documentation
Company blog posts or announcements
Published metrics, user counts, or growth data
Academic research or industry reports
Secondary sources (use with caution):
Tech news articles or analysis pieces
User reviews or community discussions
Third-party market research or estimates
Avoid:
Relying solely on memory or general knowledge
Assuming patterns from one domain apply universally
Making claims without verifiable sources
Treating reference cases as prescriptive templates
Guiding Principles
Core Distinctions
User vs End user:
User: The person using this skill (product creator, PM, designer, entrepreneur, etc.)
End user: The person who will use the product being discussed
These are distinct roles with different perspectives
Features vs Value:
Features: What the product does (technical capabilities)
Value: What end users achieve through the product (outcomes, benefits)
End users adopt products based on value, not features
Value perception timing:
Immediate perception: End users perceive they achieved something during or right after use
Delayed perception: End users perceive they achieved something after sustained use over time
These are not mutually exclusive; products can provide both
Neither is inherently superior; each addresses different end user needs
Research Approach
When encountering unfamiliar concepts:
Research mentioned products, technologies, or domain-specific terms
Use WebFetch or WebSearch to gather current information
Seek official documentation, published metrics, and verified sources
Balancing exploration and evidence:
Exploratory thinking: Appropriate when identifying potential value types or brainstorming approaches
Evidence-based analysis: Required when claiming specific patterns, comparing to real products, or stating what works in practice
Evaluating case applicability:
Reference cases illustrate patterns, not universal rules
Assess whether product type, market context, user behavior, and value delivery match
When cases do not apply, research comparable products in the relevant domain
How to Use This Skill
This skill works best in conversation. When the user discusses a product idea:
Explore value clarity: Can end users articulate what they'll achieve?
Examine the timeline: Is value immediate or delayed for end users? What's appropriate for this product?
Assess perception: Can end users see/feel their progress?
Discover hidden value: What value might end users not yet recognize?
This isn't a checklist - it's a way of thinking. Each product is different. Each market is different. The goal is to think clearly about whether end users will "know" what value they'll get.
Research during analysis: When the user mentions specific products, technologies, or concepts, this skill may research them via WebFetch or WebSearch to provide context-appropriate analysis based on current information rather than assumptions.
Key Principles
End users must "know" what value they'll achieve - even if it takes time
Value types are diverse - identity, money, benefits, status, capability, and more
End users often don't know what they want - help them discover it
Perception matters to end users - invisible value feels like no value
Context is everything - patterns from one product may not apply to others
Test with real end users, don't assume - validate in specific scenarios
Both short-term and long-term are valid - neither is superior, choose based on product nature
Additional Resources
Reference Files
For detailed case studies with real data and metrics:
references/real-cases.md - Complete analysis of Dropbox, Instagram, Duolingo, WeChat, Google Wave, and Quibi with real numbers and data sources
references/real-cases-zh.md - 中文版真实案例分析
Remember
This skill helps think about value, not prescribe solutions. Every product is unique. Every market is different. The goal is to discover whether end users will clearly understand what they'll achieve - because that understanding is what drives adoption.