Customer Analytics seems to be one of the hottest topics in business today, with the newly coined ‘Data Scientist’ role in high demand.  And with all hot topics there is hype and reality.   The reality is that analytics can provide a big impact on bottom line profits.  The hype is that it can provide a significant ROI in all environments.  In most environments, you need three critical components for customer analytics to provide a significant impact:

  • Business Measure in Need: A troubling trend in a key business measure compared to competitors or a business measure you are confident can be improved if given focus. Examples include decreasing customer value, increasing churn, reduced acquisition or static online advertising revenue.
  • Behavioral Data: Predictive customer analytics are data driven. Valuable data, that drives insight into why a business measure is changing or staying static is critical as it is that insight which enables strong predictive analytics. When evaluating data ask yourself if at least some of the key reasons why a business metric changes (eg. increased churn) may be  gleaned from the data.


  • Creating an Actionable Impact: An understanding of how the predictive analytics may be used, in the form of programs and customer treatments, to positively affect the business measure(s) of interest is a must. You do not need a detailed plan but you do need to be confident on how the analytics may be used to positively affect performance.

I have found that most focus on the first two components (business measures need help and data richness) when qualifying the potential impact of analytics but put less thought into how the analytics will be used once available … and that is the biggest reason why analytic projects fail.  Too many companies spend money building predictive analytics and the solutions sit on the shelf because not enough upfront thought was put into whether they truly can be used to affect business performance … and if so then how.

Analytics can be powerful but be realistic and put together a plan against the three dimensions above before embarking on a big project.  The plan will not only qualify whether analytics has a strong potential impact on a business measure but also gives you a means to prioritize your analytic development.