We learn in a logarithmic scale (that is time, effort, number of resources on the subject or number of interviews). The logarithmic curve means that initially, we get better very fast when we learn, and then our improvement slows down gradually like observed in the graph below.
Only customer datas is worth something. Everything else is positional and biased. Basically we all have tendency to imagine a great product based on our single and latest customer interview ... a single point of view, a single customer, tech aspect or business perspective. A one dimensional feature doesn't make a product.
Only proof is worth something : observation, non biased customer feedbacks & interviews, tech feasibility, proof of value, proof of urgency, proof of usage, payment etc ... One way to gather data is thus to experiment : conduct user research, sales interview, testing session, prototyping implementation etc ... Besides, the goal of experimentation is to break a big objectives into small experimentations allowing you to advance, gain customer knowledge while balancing the level of risk and the level of investment.
One essential dimension of product management is to learn fast, in order to identify new opportunities, enable critical thinking and improve decision-making.
Our goal is to make the company a machine at learning, producing, building upon and refining ideas, rethinking what we are doing until each product fits its market and each cohort its model.
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The process mixes analytical, intuitive and experential Intelligence.
