You get your hands on a new data set. The goal - to get a deeper understanding of a specific phenomenon, say churn. You do the due diligence - eyeball the data to see what data points it contains and prepare some basic summaries (averages, medians etc.). Then you start asking questions of the data to get a better understanding of churn.
However, after building out a few reports, nothing stands out. The behavior of users who end up churning seems very similar to users who renew. Their interactions with your product/service are not very different. Customer support metrics are equally positive.
This leaves you confused and a bit apprehensive. If there are no visible differences or indicators, how do you optimize renewal?
1) Use The Right Visualization
The importance of accurate data presentation cannot be stressed enough. The wrong visualization can obscure insights. Here's a basic guide to using the right chart types -
Additionally, a table of data can also be the right presentation format. When the result is less than 4X4 cells, it is easy for the eye to interpret it.
2) 5 Whys Approach
The trick to getting the data to talk is to ask a ton of questions. You won’t be able to generate a ton of questions right from the beginning, though, and that’s normal. The idea is to let the answers to your questions beget more questions.
Find the smallest indicator you can observe from your initial set of reports, and ask why that indicator might exist. The moment you ask why, your brain generates guesses and opinions. Do this 5 times, and you’ll be drowning in ideas for reports.
Let’s continue with the example of analyzing customer churn -
The above example is a bit too optimistic, but you can see how digging deeper into a seemingly small observation can lead to actionable insights.
3) Invite Opinions
We did this exercise with one of our clients recently. We built out one iteration of reports, presented its findings to the broader team, and invited their questions and thoughts. It was incredible - being experts in their industry, they were able to provide context to raw numbers. We were easily able to distinguish findings that mattered from the ones that were the result of a one-off experiment. This exercise gave us clear next steps for the second round of reports.
Do you have other ideas and tips for getting answers from data? We’d love to hear them in the comments below.