Customer Lifetime Value Prediction

The Problem

Predicting which customers matter most

Teams know customer value varies, but don't know which signals matter. Every customer gets the same support and retention effort, even though some are worth far more than others. This exploration tested whether past purchase behavior could predict future customer value well enough to guide prioritization decisions.

My Approach

Lead with discovery

  • Understand what business problem I'm solving
  • Find data where I can test the approach
  • Learn whether teams value explainability over model accuracy
  • Validate that predictions will change how teams work
What I Found

Four consistent signals

  • What customers bought before tells us their future value better than anything else.
  • Grouping customers by value matters more than predicting their exact amount.
  • How you organize and clean the data matters more than which model you choose.
  • The same prediction error can be acceptable or unacceptable depending on what you need it for.

The biggest insight wasn't the accuracy score. It was that teams care more about understanding why a customer is valuable than having a perfect prediction.

So What?

Using predictions to allocate resources

  • Invest heavily in high-value customers. They generate most of your revenue and deserve priority support and attention.
  • Identify growth customers early. Find lower-value customers with signals of future growth and nurture them strategically.
  • Automate low-value customer experience. Use self-service and automation for lower-value segments to reduce costs.
  • Segment everything. Use CLV predictions to create different product tiers, pricing models, and support levels that match customer value.
If We Deployed This

Measuring impact and performance

  • Ranking accuracy: Does the model correctly rank customers by value? Rankings matter more than absolute predictions.
  • Segment stability: Do predictions stay consistent over time, or do they fluctuate too much to be actionable?
  • Cost of misallocation: What happens if we invest heavily in a low-value customer or ignore a high-value one?
  • Segment balance: Does the model work equally well across all customer types, or does it favor certain groups?
  • Business outcome tracking: Are we actually increasing revenue from high-value customers and reducing costs on lower-value ones?