A Bird’s Eye View of Customer Lifetime Value

Venue Capacity: 70 people

The capacity of the venue is about 70 people but because of no-shows, we have a policy of first-come, first-served on the night in the event of the room filling up. Managing no-shows is a problem all Meetups have to deal with and this is the fairest method we can think of. While we have never been in a position to turn anyone away for capacity reasons, it is always a possibility, so please arrive early to avoid disappointment on the night.

A business’s value is inextricably linked to its customer equity, i.e. the total lifetime value of all its customers. Determining this value is almost always troublesome, and predicting it even moreso. Determining how much a new customer is worth is crucial in determining what is the acceptable cost to pay for acquiring them. Knowing the value of a customer’s residual lifetime value is needed to determine how much effort should be put in preventing them from churning.

The well-known Pareto principle -- that 20% of customers generate 80% of revenue -- causes problems when trying to predict their exact value, as tail heavy distributions don’t lend themselves to standard regression techniques. Predicting LTV gets even more complicated as in many contexts it is difficult to pinpoint the exact moment when a customer churns.

This talk will present an overview of the common approaches and challenges for making LTV predictions as well as certain options for tackling them.

About me

Eric Mehes is a Senior Data Scientist at Ding. An avid Pythonista with a focus on customer analytics, he creates machine learning models that will help Ding to become the web’s leading top up platform. In the past Eric worked on recommender systems for fashion e-commerce and before that he dabbled a bit in physics.