Toolkit

Opportunity Framing Toolkit

A practical, fillable Opportunity Canvas, Hypothesis Template, Assumption & Risk Map, plus a 60-minute facilitation agenda and guidance — now interactive so teams can capture, save, and iterate on discovery-ready problem statements and first experiments.

Interactive Tool

Opportunity Framing Toolkit

Welcome — turn curiosity into a testable discovery plan

This toolkit helps teams convert fuzzy interests or complaints into a one-sentence problem, measurable success criteria, prioritized assumptions, and a first low-risk experiment. Use the fillable canvas during a rapid 60-minute framing workshop and save responses so your team can iterate, compare, and run experiments with less risk.

What you get

  • A fillable Opportunity Canvas for capturing evidence, users, success metrics, risks, and assumptions.
  • A hypothesis template that keeps outcomes measurable and testable.
  • An Assumption & Risk Map and a short facilitation agenda for a 60-minute rapid framing session.

How to use

  1. Gather 3–6 participants with diverse perspectives.
  2. Follow the timed facilitation agenda below while filling the canvas fields in this form.
  3. Prioritize 1–3 assumptions to test and design a small experiment.
  4. Save the canvas here so you can iterate, compare outcomes, and link experiments to your team's tracker.

Facilitation agenda (60 minutes)

  1. 0–5 min — Introductions & clarify scope
  2. 5–20 min — Share observations and evidence (fill Observation & Evidence)
  3. 20–30 min — Draft one-sentence Problem Statement and Target User
  4. 30–40 min — Identify Success Metrics and list Key Assumptions
  5. 40–50 min — Map top Risks and choose highest-priority assumptions to test
  6. 50–60 min — Craft a Hypothesis and a first experiment plan with success criteria and next steps

Example (compact)

Observation: Many customers abandon checkout on mobile.
Problem Statement: Mobile checkout flow causes friction for users trying to complete purchases on phones.
Success Metric: Increase mobile checkout completion rate from 42% to 55% in 8 weeks.
Key Assumption: Reducing required form fields will reduce abandonment.
Hypothesis: If we reduce checkout fields on mobile, then completion rate will increase, measured by checkout completion rate and time-to-complete.
First Experiment: A/B test a simplified mobile checkout for 3,000 sessions over 2 weeks.
Enter the date of the framing session (optional).
Who will guide the session?
List participants and their roles or perspectives (e.g., product manager, engineer, customer support).
What specific behaviors, complaints, metrics, or anecdotes led us here? Be concrete (links, dates, numbers help).
Write a single sentence describing the core problem to address. Focus on the user and the pain, not a solution. Example: ‘Customers on mobile abandon checkout because the form is too long and unclear.’
Who experiences the problem? Be specific (persona, segment, context).
List the quantitative and qualitative evidence that this is worth investigating (e.g., drop-off rates, support tickets, quotes). Include metrics and ranges where possible.
How will you know an intervention worked? Provide specific metrics, baseline values, and target values or thresholds.
List the assumptions that must be true for your hypothesis or solution to deliver the success metrics. Prefer concise, testable assumptions (e.g., ‘Reducing fields will cut time-to-complete by 20%’).
Rate overall confidence in these assumptions (1 = very low, 5 = very high).
1.0 10.0
List the most important risks if the assumption(s) are false and short mitigation ideas. Examples: regulatory risk, data loss, customer backlash.
Use this template: If [change], then [measurable outcome] for [target user], measured by [metric]. Example: ‘If we shorten mobile checkout by removing non-essential fields, then mobile completion rate will increase by X percentage points for first-time mobile shoppers, measured by checkout completion rate.’
Describe a small, low-cost experiment to test the highest-priority assumption. Include sample size, duration, and what will be measured. Examples: prototype test, A/B test, concierge experiment, smoke test.
Estimate how long to run the experiment to collect meaningful results.
Define the pass/fail thresholds for the experiment (be explicit).
Team-assigned priority for follow-up work.
List the next actions, owners, and any resources needed to run the first experiment.
Add links to relevant dashboards, tickets, recordings, or files (paste URLs).
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
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