problem-statement

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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.

nandanosql By nandanosql schedule Updated 2/28/2026

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 statement
  • skills/discovery/jobs-to-be-done/SKILL.md — Understand what the user is trying to accomplish
  • skills/discovery/discovery-interview-prep/SKILL.md — Validate your problem with real users
  • skills/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

Install via CLI
npx skills add https://github.com/nandanosql/all_in_one_pm_skills --skill problem-statement
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