Abstract: That old mantra of "the customer is always right" isn't always accurate. On the contrary, assuming Product Owners/Managers are customer proxies who wholly know the customer does not work either. This session explores what can happen when we give the customer OR Product Owner too much power over what we're building. You guessed it - we build the WRONG thing.
In this session we put on our statistics and science caps to look at customer feedback data and form experiments to understand our customers. You will hear stories about how asking the customer what they wanted pointed us to an overly specific solution that was not sellable or representative of the customer whole. Think about any time you are asked to give feedback as a customer - you usually only do when you really like or really hate something; we're missing the whole bulk of customer's feedback - the middle! On the other side of the coin, you will see how our own biases can forced us down a bad path even when we're looking at data that looks promising! It's like clicking on that first Google search result that matches your query while ignoring the others that don't. These simple examples are extrapolated when we're spending lots of money developing a feedback specific solution and we're looking at non-representative, biased feedback!
Join Natalie to dive into how to better utilize the scientific method to write hypotheses to narrow down options using the concept of set-based design (that you don't have to be a designer to understand). Find out how to run tests and use real data measurement generated from your hypotheses to understand what design set direction your product should try next. After all, interpreting the real data is much better than assuming you OR the customer know best so we can move to building the right thing as we learn the right thing.
Learning Outcomes: - How to explain, understand, and utilize set-based design to maximize options
- Design experiments and hypotheses to test and narrow down design options using real data
- Identify research bias and customer bias in real data trends to protect your product from an expensive wrong turn
