7 Segmenting customers with advanced metrics

 

This chapter covers

  • Metrics made from ratios of other metrics
  • Metrics that measure behavior as a percentage of a total
  • Metrics that show how behavior changes over time
  • Metric conversions from long periods to short periods, and vice versa
  • Metrics for multiuser accounts
  • Choice of ratios to use

You’ve learned a lot about understanding churn with metrics derived from events and subscriptions. You’ve seen that simple behavioral measurements can be powerful for segmenting customers who may be at risk for churn and who have different levels of engagement. But you’ve also seen some of the limitations of simple behavioral metrics.

Many simple metrics are correlated, and correlations arise because customers who have a lot of product-related events tend to have a lot of other events as well. Correlations make it harder to tell which types of behaviors are most important. The problem is deeper than a lack of refinement. In this chapter, you’ll learn that correlation between metrics can make you misread the influence of a behavior. A behavior that’s negative (in the sense that it takes utility and enjoyment away from customers) can appear to enhance engagement when it’s correlated with other behaviors that provide utility and enjoyment.

7.1 Ratio metrics

7.1.1 When to use ratio metrics and why

7.1.2 How to calculate ratio metrics

7.1.3 Ratio metric case study examples

7.1.4 Additional ratio metrics for the simulated social network

7.2 Percentage of total metrics

7.2.1 Calculating percentage of total metrics

7.2.2 Percentage of total metric case study with two metrics

7.2.3 Percentage of total metrics case study with multiple metrics

7.3 Metrics that measure change

7.3.1 Measuring change in the level of activity

7.3.2 Scores for metrics with extreme outliers (fat tails)

7.3.3 Measuring the time since the last activity