name: problem-statement description: Frame a customer problem with evidence before jumping to solutions. Forces clarity on who's affected, what's broken, and why it matters — using the 5W1H framework. type: component domain: discovery difficulty: beginner estimated_time: "15-25 min" prerequisites: [] outputs: ["problem-statement-document"] triggers: - "I need to define the problem we're solving" - "Help me frame the customer problem" - "Write a problem statement" - "We're jumping to solutions too fast"
Purpose
Frame a customer problem with evidence before jumping to solutions. This skill forces clarity on who is affected, what is broken, why it matters, and how big the impact is — so your team builds the right thing, not just the first thing someone suggested.
This is not a solution brief. It's the foundation that every PRD, epic, and user story should trace back to.
Key Concepts
The 5W1H Problem Frame
A complete problem statement answers six questions:
| Question | What It Captures |
|---|---|
| Who | Specific user segment affected |
| What | Observable behavior or gap |
| Where | Context where the problem occurs |
| When | Frequency and timing |
| Why | Root cause (not symptoms) |
| How big | Quantified impact (users, revenue, time) |
Why This Works
- Prevents solution bias: Forces problem exploration before solution design
- Creates shared understanding: Everyone agrees on what's broken before debating fixes
- Enables prioritization: Quantified impact lets you compare problems objectively
- Validates assumptions: Evidence requirements surface what you actually know vs. assume
Anti-Patterns (What This Is NOT)
- Not a solution disguised as a problem: "The problem is we don't have Feature X" ← This is a solution, not a problem
- Not a complaint: "Users are unhappy" ← Too vague, no actionable insight
- Not a metric: "Churn is 5%" ← This is a symptom, not the problem
When to Use This
- Starting a new initiative or feature
- Justifying work in a roadmap or PRD
- Aligning stakeholders on what to solve
- Before any discovery research to clarify focus
When NOT to Use This
- When the problem is already well-documented (update, don't restart)
- For bugs or technical issues (use bug reports)
- When you need a solution spec (write a PRD instead)
Application
Step 1: Identify the User Segment
Be specific. "Users" is too broad.
**Who is affected:**
[Specific persona or segment — e.g., "Trial users in their first 7 days"]
**How many:** [Number or % of total user base]
**Evidence:** [Source: analytics, interviews, support tickets, etc.]
Quality check:
- Can you name a real person who has this problem?
- Is this a segment (not "all users")?
Step 2: Describe the Observable Problem
State what's happening (or not happening) in concrete, observable terms.
**What is happening:**
[Observable behavior — e.g., "72% of trial users abandon onboarding at step 3"]
**What should be happening:**
[Expected behavior — e.g., "Trial users complete onboarding and reach first value within 10 minutes"]
**Gap:**
[Delta between current and expected — e.g., "62% gap between current and target completion"]
Quality check:
- Is this stated as observable behavior (not an opinion)?
- Can someone else verify this independently?
Step 3: Establish Context (Where & When)
**Where it occurs:**
[Environment, workflow step, or touchpoint — e.g., "During web onboarding, specifically the 'connect your data source' step"]
**When it occurs:**
[Frequency and timing — e.g., "Every new signup; worse on mobile (85% drop vs 65% desktop)"]
**How long has this existed:**
[Timeline — e.g., "Since we added the data source integration in Q3 2025"]
Step 4: Analyze Root Cause (Why)
Use the 5 Whys or Ishikawa to dig past symptoms.
**Symptom:** [What you first noticed]
**Why 1:** [First-level cause]
**Why 2:** [Deeper cause]
**Why 3:** [Deeper still]
**Root cause hypothesis:**
[Your best understanding of the fundamental cause]
**Confidence level:** [High / Medium / Low]
**What would change your mind:** [Evidence that would disprove this]
Step 5: Quantify Impact (How Big)
**Users affected:** [Number and % of relevant segment]
**Revenue impact:** [$ lost, at-risk, or opportunity cost per month/quarter]
**Operational cost:** [Support tickets, manual workarounds, team time]
**Strategic impact:** [Alignment with company goals, competitive risk]
**If we do nothing:** [What happens in 3/6/12 months]
Step 6: Assemble the Problem Statement
# Problem Statement: [Descriptive Title]
## Summary
[User segment] is experiencing [observable problem] when [context],
resulting in [quantified impact]. This has been occurring since [timeline]
and is caused by [root cause hypothesis].
## Evidence
- [Data point 1 + source]
- [Data point 2 + source]
- [Data point 3 + source]
## Impact
- **Users:** [X affected]
- **Revenue:** [$Y at risk]
- **Strategic:** [alignment with goal Z]
## What Success Looks Like
[Measurable outcome — e.g., "Onboarding completion increases from 28% to 70%"]
## What We Don't Know Yet
- [Open question 1]
- [Open question 2]
Examples
✅ Good Example
# Problem Statement: Trial User Onboarding Abandonment
## Summary
Trial users in their first 7 days are abandoning onboarding at the "Connect Data Source"
step (Step 3 of 5), resulting in a 72% drop-off rate. This affects approximately 3,200
new signups per month and represents ~$480K in lost ARR annually. The root cause is that
the data source connection requires technical credentials most trial users don't have
access to immediately.
## Evidence
- Mixpanel funnel: 72% drop at Step 3 (last 90 days, n=9,600)
- Support tickets: 340/month mention "can't connect" or "where do I find credentials"
- Exit survey (n=180): 64% cite "too complicated" as reason for not completing
## Impact
- Users: 3,200/month affected (72% of trial signups)
- Revenue: ~$480K ARR (assuming 5% trial→paid at $250/mo ARPU)
- Strategic: Core growth metric (trial activation) blocked
## What Success Looks Like
Trial onboarding completion increases from 28% to 65%+ within 60 days of fix.
## What We Don't Know Yet
- Would a sandbox/demo data option satisfy the "connect data" requirement?
- Are there segments (technical users) who don't hit this barrier?
Why this works: Specific segment, quantified impact, root cause with evidence, clear success metric, honest about unknowns.
❌ Bad Example
# Problem: Users don't like our onboarding
Users are complaining about onboarding. We should make it simpler.
Maybe add a tutorial or something.
Why this fails: No specific segment, no data, solution embedded in the problem, no impact quantification, no root cause analysis.
Quality Rubric
Score each dimension 0-2. Target: 8+ out of 10.
| Dimension | 0 (Missing) | 1 (Partial) | 2 (Excellent) |
|---|---|---|---|
| Specificity | "Users" (generic) | Named segment but broad | Named persona + count |
| Evidence | Opinion only | Some data, one source | Multiple data sources, cited |
| Impact | Not quantified | Partial (users OR revenue) | Users + revenue + strategic |
| Root Cause | Not explored | Surface-level why | 5-Whys with confidence level |
| Objectivity | Solution embedded | Mostly neutral | Pure problem, no solutions |
If score < 8: Revise the weakest dimension before sharing with stakeholders.
Common Pitfalls
Pitfall 1: Solution Masquerading as Problem
Symptom: "The problem is we don't have a mobile app"
Consequence: Skips problem validation entirely. You might build a mobile app for a problem that has a simpler solution.
Fix: Reframe: "Mobile users can't complete [task] because [reason]." Now you can explore multiple solutions.
Pitfall 2: Boiling the Ocean
Symptom: Problem statement covers 5 different issues for 3 different segments.
Consequence: Too broad to act on. Team can't align because everyone reads a different problem.
Fix: One problem per statement. If you have multiple problems, write multiple statements and prioritize.
Pitfall 3: Metrics Without Meaning
Symptom: "Our NPS dropped 10 points"
Consequence: NPS is a lagging indicator. It tells you something is wrong but not what, why, or for whom.
Fix: Dig into the verbatims and segment data. Find the specific behavior driving the metric.
Pitfall 4: Skipping "What We Don't Know"
Symptom: Problem statement reads as 100% certain.
Consequence: False confidence leads to building the wrong thing. You skip validation because "we already know."
Fix: Always include unknowns and confidence levels. It's intellectually honest and guides next steps.
References
Related Skills
skills/discovery/proto-persona/SKILL.md— Define the "who" in your problem statementskills/discovery/jobs-to-be-done/SKILL.md— Understand what the user is trying to accomplishskills/discovery/discovery-interview-prep/SKILL.md— Validate your problem with real usersskills/discovery/discovery-process/SKILL.md— Full discovery workflow that uses this skill
External Frameworks
- 5W1H Problem Frame — Foundation of structured problem definition
- 5 Whys (Taiichi Ohno, Toyota) — Root cause analysis
- MITRE Problem Framing — Look Inward / Look Outward / Reframe
Skill type: Component Domain: Discovery & Research Prerequisites: None