Reversible or Irreversible Decision?
Fast decision making is often the mark of a great entrepreneur. But as Steve Blank points out in a recent blog post: decisions have two states: “those that are reversible and those that are irreversible.” Entrepreneurs should take the time to make careful decisions when they are irreversible (such as accepting money from a VC). For reversible decisions, he recommends starting “a policy of making reversible decisions … before a meeting ends. In a startup it doesn’t matter if you’re 100% right 100% of the time. What matters is having forward momentum and a tight fact-based feedback loop (i.e. Customer Development) to help you quickly recognize and reverse any incorrect decisions.”
This is awesome guidance considering the countless hours I saw wasted at my first startup where people debated decisions that had little impact on results. On my marketing team I quickly ended these debates with “test it.” When debates extended across departments I abdicated the decision to others but measured the results to make sure they weren’t negatively affected.
Higher Velocity Testing Better than Perfect Certainty
At LogMeIn (my second startup) the goal was to start a testing and analytics culture on the marketing team right from the beginning. Rather than hiring someone with a traditional marketing background, my first marketing hire had an actuarial background (the people that assess insurance risk). Next we hired a super fast web designer/developer and an equally fast copywriter. This team was able to rapidly iterate everything to determine combinations that generated optimal conversions.
One warning before hiring a math wizard to lead your analytics is that they will often want sample sizes that almost completely eliminate doubt that you are making the right decision. With the volume of users at most startups, this would limit you to very few tests. When I suggest the following mental exercise to a mathematician, they usually come around to high velocity testing with lower certainty. I suggest that they try modeling the results of 25 tests with 80% certainty compared to 5 tests with 95% certainty. I also explain that we can always go back and test it again when we have higher volume.
Understanding User Motivations
But some decisions are a lot harder to test and require more up front traditional research. For example, when trying to understand the motivations behind users’ actions (or lack of actions) I generally interview and/or survey them. But as Robert Cialdini points out in his latest book: “We know that people’s ability to understand the factors that affect their behavior is surprisingly poor.” So in the past, this research often confused rather than enlightened me.
It wasn’t until I read Four Steps to the Epiphany that I realized you could take a more scientific approach to understanding user motivations. Steve Blank recommends starting with hypotheses around the key factors that will be important for building your business – such as the real problem you are solving and the people who are most motivated to solve this problem. By engaging prospective and actual users you can validate and/or refine these hypotheses. Unlike the previous approach to surveying, we now gain clarity with more user input.
Still I know there is a lot of room for improvement in my scientific approach to understanding user needs and motivations, so I recently brought on someone to help me take my research and analytics to the next level. He is Molecular Biologist as well as an entrepreneur who earlier in his career spent 10 years in biotech research. It should be interesting to see what happens when he applies his rigorous research approach to customer development. He’ll be joining me for my two projects that start next month.
At early stage web startups we have the distinct advantage over established companies of starting with a blank slate, making it possible to set up much more controlled experiments. In addition to making better use of tight startup time and money, we also hope to leverage the blank slate to challenge some long held marketing beliefs regarding what really works.