The recent election put complex data analysis in the mainstream view in a new way, but the role and importance of data in business decision making is nothing new. Customer Experience professionals can take some important lessons from the failures of accurate data analysis made by many pollsters and pundits.
Customer Experience teams are often required to draw conclusions from incomplete or imprecise business data. Mature CX programs can often provide close to a 360° view of customer life-cycle data, but this is not the norm. CX teams need to be able to know not only if they are paying attention to the right data, but also if they are seeing the whole customer story beyond the available data. As many political pundits learned quite publically a few weeks ago, it is easy to make startlingly bad inferences with data.
Mathematician Abraham Wald worked for the Allied air forces during WWII to help improve survival rates for bombers over Europe. They tracked damage patterns on returning bombers to determine where they should add armor. The counter-initiative conclusion he came to via his analysis was they needed to add armor to the areas on surviving bombers that were not seeing significant bullet damage. This was because the bombers damaged in these areas often never made it back to base.
Nate Silver summed the issue up well in his recent book, The Signal and the Noise, Numbers don’t speak for themselves.” CX teams need to make sure they are working to fill-in their own gaps in data in order to successfully apply complex statistical analysis and predictive modeling in their customer experience efforts. Do you find out why you aren’t able to acquire specific types of customers? Do you follow-up with lapsed customers to find out why they left? Are you working to understand the ones that got away or are you only looking at the ones that make it back?