Product market fit with biased data
One of the toughest things to understand when creating a product is the Product-Market fit. In the problem-solution cycle we have many tools to understand if the problem we identify is common to other people and if they see a value in our proposed solution. However, when building a product, we have to make choices that depart from the ideal scenario. Product-market fit is therefore when we achieve a status in which what we have developed is what the market wants, even if not ideal.
The lean methodology suggests that we should learn from experiments and iterate quickly in order to improve our product. However, this experiments can be done on biased samples, for example testing a product with managers and not with the employees who will actually use it.
This can be even worse if we have gender biased data (see: the only way of improving stoves is to actually consider their main users and some products are designed taking into account only men). Therefore we can think that we have a product that satisfies the demand, but based an skewed insights and prejudices.
Therefore it is important to develop design procedures and business analysis tools that take into account not only gender as a random sample of the population, but actively considering that roles and physiology of women are different (see: it is not clear the amount of farming done by women and Designing tools for work is traditionally done considering the average man and not woman).
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