I did a panel at Etsy's engineering leadership offsite today, which was amusing. One of the topics of the panel was:
Challenges of balancing data-light product bets vs purely data driven incremental improvements.
All three of the panelists agreed, although each of us phrased it a different way. The first panelist (Dan McKinley) spoke about the need, even for products that are not purely A/B test driven, to drill down on the goals and try to find something to measure about how a project will actually achieve a goal. If the project is part of a larger goal to increase revenue by 25% this year, what way is it contributing to that? How do we measure its success? Even when you are driving decisions by "vision" there is some quantitative goal you are trying to achieve, so state what it is.
The second panelist (Albert Wenger) spoke of the importance of balancing the quantitative with the qualitative. Some things cannot be purely quantitatively measured, and there are qualitative measures that are incredibly valuable to the process of discovering product market fit. User testing, user research, watching how people actually engage with a product are all essential to creating something great, beyond simply finding numbers to measure and trying to increase them.
My perspective on the issue is that qualitative methods are important, but qualitative is still analytical. You may not be able to use data-driven reasoning because you're starting something new, and there are no numbers. It is hard to do quantitative analysis without data, and new things only have secondary data about potential and markets, they do not have primary data about the actual user engagement with the unbuilt product that you can measure. Furthermore, even when the thing is released, you probably have nothing but "small" data for a while. If you only have a thousand people engaging with something, it is hard to do interesting and statistically significant A/B tests unless you change things drastically and cause massive behavioral changes.
So, instinct and guesses are necessary. But we needn't lose our analytical approach just because we don't have data. When you build something, you have a hypothesis about the person you are building it for. You have a guess as to what they will like, and most of the time you have a reason for that guess. When you're trying to build a business, you need a chain of events that you expect could happen that would indicate a product is successful. You have a sense of what to start measuring once the thing is released that will show whether it is working. Answering the questions of who is my customer, why would she use this, and what will signs of interest and engagement look like is essential to going from vague instinct to thoughtful first product.
Data can't make all our decisions for us because data isn't there to get us from 0->1. We have to use our powers of observation, of empathy with our customers, and of deduction, to create smart hypothesis in a qualitative way. Qualitative analysis will always have a role in product development, and customer empathy is likely to drive us more quickly to great products than simply relying on numbers alone.
Thanks to Etsy for hosting and my apologies to my fellow panelists if I misrepresented your views!
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