Which of your customers drive the greatest value over the long term? And more importantly, what steps can you take to transform more of your customers into high value customers? Click through to our first article where we share the challenges with optimising to the average customer, and talk through a simple model that you can build to start segmenting your customers today!

What actions should I take with my highest value customers, and those customers that are at risk of churning? In the second post in our series we discuss the different ways we can use Recency-Frequency-Monetary Value (RFM) scores to create different segments and drive targeted action to keep the highest value customers, develop the mid-range customers, and protect the customers that are likely to leave.

How much will our customers spend in the future? How likely are they to churn? In this article, we go a step further, and demonstrate how we can use models from the field of Biology to predict the likelihood of a customer churning. We also show how lifetime value models can be used to predict future customer spend, by giving us an estimate of expected purchases and expected order value.