Assumption Mapping identifies the beliefs that must be true for your product or idea to succeed. Plotting assumptions on Importance vs. Evidence forces teams to focus testing effort on the beliefs that matter most and are least proven.
Before building anything significant. Most valuable at the start of a new initiative when enthusiasm is high but data is low.
- Brainstorm all assumptions: customer, problem, solution, and business model
- Draw a 2x2: X-axis = Evidence (low to high), Y-axis = Importance (low to high)
- Place each assumption on the grid as a team
- Focus on top-right quadrant: high importance, low evidence — these are Critical Assumptions
- Design the smallest possible experiment to test each critical assumption before committing to a build
Before building a social listening feature, critical assumptions include: (1) Users want friends to see what they're listening to — high importance, uncertain. (2) Social visibility will increase session length — high importance, low evidence. Both need testing first, because embarrassment about listening habits turned out to be a real blocker to the social feature's success.
Before building AI-powered alert triage, critical assumptions include: (1) Analysts will trust and act on AI recommendations — high importance, zero evidence. (2) The model can achieve acceptable precision on real market data — high importance, low evidence. Testing these early prevents building an expensive system that compliance teams simply override.
- Listing only technical assumptions while ignoring customer and market assumptions
- Testing easy assumptions rather than important ones to feel productive
- Not revisiting the map as you learn — it should be a living document throughout the project
- Testing Business Ideas — David Bland & Alex Osterwalder