While there are many metrics to measure CX, NPS remains a top contender for many businesses. Therefore, it’s natural and necessary to link your investments in CX to the outcome in NPS. Did your latest investment into improving some of your customer journeys change NPS afterward? How long did it take? What was the cost per percentage point increase in NPS?
To answer these questions you need a causal model that is capable of linking historical CX investments to future NPS outcomes. While it might be tempting to use a Marketing Mix Model (MMM) to measure this. It comes with a lot of issues. First off MMMs are based on multiple linear regression which can be good to measure targets that can assume any value at all. This is problematic in this case since NPS is a measure that can only vary between -100 and +100. As such it is important to include the proper inductive bias in your model to do this. To learn more about how Holistic Business Measurement and Optimization is changing the game here have a look at our previous post
Once you are armed with a model that can continuously link your CX investments to your NPS score, you are in a position to measure and prove the effect those investments have.