Traditionally new products were developed according to the founder’s idea that was written down, which the engineers built. The last few years this pattern has changed. Across the internet there has been a shift in mindset to bring the customer into what we are building. There is a growing awareness that we are wrong about what the customer wants most of the time. Therefore it is necessary to experiment to find out what customers want.
We talked to Teresa Torres about the role of experimentation in product management. The greater part of her career has spent in pre product market fit internet start ups, so if someone should know how to experiment to find a product that is successful it’s Teresa. Today she helps companies make better product decisions as a consultant and coach.
According to Torres it is better to start thinking about product development in terms of experiments rather than requirements. In Marty Cagan’s dual-track scrum article, he recommends using a discovery track and a delivery track. First we should experiment in the discovery track to identify what the right thing to do is. In the discovery track there should be a lot of experimentation in order to to inform what to build. Today there is a tendency to build any and every idea.
But real experiments require quite a bit of rigor and experience in designing the experiment.
“This is my primary focus as a coach. Many teams start to experiment but don’t have the experience to do it well. Most of us don’t have strong science or statistics backgrounds. What happens in practice is instead of generating informed hypotheses and designing experiments to test those hypotheses, we are testing anything and every thing The problem with this approach is that we risk false positives. We are running tens and sometimes hundreds of experiments, many with way too many variations. This guarantees that we will see many false positives – changes that look good but really have no impact. As a result, we are making decisions on bad data. If we want to build better products, we need to understand how to run good experiments. The business side needs to be more scientific and the data science side needs to be more business oriented”
According to Torres the ready availability of experimentation tools like Optimizely and Visual Website Optimizer opens up the possibility for experimenting, but you need resources and expertise otherwise decisions will be made on faulty data. Part of the problem is the wide spread “Fear of Math”. Most people shy away from concepts like statistical significance and power. But it is necessary for product managers to begin understanding these concepts. Today there are many online resources that will teach you basic understanding of statistical concepts. Another problem is that we need to be better at hypothesis design. If you have not properly designed your hypothesis before you start you are not setting yourself up to get good data. We also need experimenters that can design experiments that can also test the hypotheses they are designed to test.
I asked Torres if there are any simple rules of thumb or best practices for product managers who want to get started.
“Don’t trust results that are not statistically significant. Surprisingly many teams are not even testing for significance. Define your hypotheses and decide upfront what decisions you will make if it passes, fails, or is flat . Otherwise you will just find yourself rationalizing after the fact why your change is still good regardless of what the data tells you. Run the same experiment multiple times. It helps reduce false positives. There is no absolute truth. The world is always changing, something that didn’t work in the past may work in the future. Always keep a mindset that everything evolves.”
For more tips, see her article on The 14 Most Common Hypothesis Testing Mistakes (And How to Avoid Them)
It is up to you if you take Teresa Torres’ suggestion to start experimenting. In the mean time visit her excellent blog Product Talk and sign up for her newsletter. It is always packed with interesting content about product management.