Spotted in the wild:
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Mythbusting business assumptions through experimentation |
In 2002, the U.S. Secretary of Defense, Donald Rumsfeld, introduced the concept of Known Knowns, Known Unknowns, and Unknown Unknowns. When designing hypothesis for ideas to test with clients, it's interesting to see how pretotyping brings to light these "Unknown Knowns".
In the world of data versus opinion, Unknown Knowns are the assumptions that are often treated as facts within companies, but in reality, they’re far from accurate — often by a considerable margin. This could relate to basic information such as market share percentages, customer numbers, customer activity rates, and more. Essentially, all the
data that lies beneath the visible metrics of revenue, profit, and other big shiny numbers. These Unknown Knowns are where the true opportunity and challenge of experimentation lies.
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The purpose of running experiments is to decide whether to invest in a new idea, feature, or product. This requires a baseline, which helps us understand the 'X' in the 'At least X% of Y will do Z' statement. Without this baseline for success or failure, conducting the experiment would be pointless, because we wouldn't be able to determine whether it passed or failed.
The advantage of applying pretotyping in organisations is that it allows us to uncover and understand the actual numbers, helping us make better decisions over time.
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What is the takeaway? |
Approach all non-headline figures with skepticism and treat them as Unknown Knowns! The quality of your experiments and decisions will improve as you improve your understanding of accurate internal customer metrics to discover the true customer behaviour. But be prepared for resistance — nobody wants to be measured 😉
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