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Assumption Mapping
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The Assumption Mapping Cheatsheet helps to prioritize assumptions about an idea or product.
It aims to identify the critical assumptions that need to be validated to justify the value of an idea or product and further investment in resources for it.
how does the method work?
Assumption Mapping originally comes from the Lean UX area and is now widely used in the Google Design Sprint. As a method, Assumption Mapping offers a prioritization of assumptions from all three areas Desirability, Feasibility & Viability of the "Sweet Spot for Innovation" based on their risk.
Assumptions are first differentiated into Analogs and Antilogs based on their risk or strength of evidence:
- Analogs: These are assumptions for which we already see a high strength of evidence through validation in the market by similar products or services (known-to-the-market). For example, when the Apple iPod was introduced, it was relatively certain that customers would want to listen to music on the go through headphones, as this was already widely accepted by the market with other products such as the Sony Walkman.
- Antilogs: These are assumptions with still relatively low strength of evidence. They are therefore assumptions where we, as well as the market, do not yet have sufficient certainty to accept them and more evidence is needed (unknown-to-everyone). When the Amazon Echo was introduced, for example, it was not certain in advance whether customers would voluntarily want to put a microphone in their homes.
In addition to the strength of evidence, the risk of an assumption is also determined by the degree of importance of the assumption for the success of an idea or product. The goal of risk ranking is to identify critical assumptions. These are assumptions with low evidence strength, i.e. antilogs, which also have a high degree of importance.
Step by Step Guide
The Assumption Canvas Cheatsheet consists of a 2x2 matrix with the two dimensions of degree of importance and strength of evidence as respective axes. Discuss these two dimensions for your pre-identified assumptions and rank them together along the two dimensions. Using this matrix, all assumptions can be classified into four different categories that are subject to different levels of criticality and recommended action:
Quadrant (Known/Important): Important analogs you should plan for. The assumptions in the upper left quadrant are often facts rather than assumptions. Discussion of those is still useful, as the understanding of these assumptions as facts often varies across a team. These assumptions should be aligned and integrated with the existing plan such as in user story mapping or Agile release train planning.
Quadrant (Known/Unimportant): Unimportant Analogs that you can deprioritize. Assumptions in the lower left quadrant can be considered a distraction for your team. The Lean approach is about eliminating such distractions. Often teams spend their time in this very quadrant because it feels safe.
Quadrant (Unknown/Unimportant): Unimportant antilogs that you have yet to generate knowledge about. The assumptions in the lower right quadrant are complicated in that you are aware that you cannot yet classify them with certainty because you do not yet know enough about them. This is where generative experimentation techniques such as problem intervies, proto-personas, or contextual interviews are necessary to initially assist in defining a possible problem.
Quadrant (Unknown/Important): Important antilogs you need to validate. Assumptions in the upper right quadrant are assumptions you have made that need to be validated. This requires more evaluative experimentation techniques such as Google Ads, landing pages, solution interviews, or A/B pricing tests that look for evidence of a specific value proposition, technology, or revenue model.
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