3 Measuring customers

 

This chapter covers

  • Measuring counts, averages, and totals of customer events
  • Running QA tests on metrics
  • Choosing time periods and timestamps for metrics
  • Measuring how long a customer has been using a service
  • Measuring subscription metrics

If you are operating a product or service with repeated interactions with users or customers, then you should be collecting data about those interactions in a data warehouse. Interactions in this context means interactions between the user and the product, service, or platform. (It can also include interactions with other users, mediated by the platform.) It is common to refer to such interactions as events for short, because interactions tracked in a data warehouse invariably have a timestamp telling you when they happened.

DEFINITION

Event —Any fact about user behavior, stored in the data warehouse with a specific timestamp.

I am not going to teach you how to collect that data, but I am going to teach you how to put that data to good use. The first step in using raw data to fight churn is to turn the event data into a set of measurements that summarize the events and collectively produce a profile of the users’ behaviors. These measurements are often called behavioral metrics, or just metrics, for short.

DEFINITION

Metric —Any summary measurement of user behavior. Metrics also have a timestamp, although they summarize behavior over more than just one point in time.

3.1 From events to metrics

3.2 Event data warehouse schema

3.3 Counting events in one time period

3.4 Details of metric period definitions

3.4.1 Weekly behavioral cycles

3.4.2 Timestamps for metric measurements

3.5 Making measurements at different points in time

3.5.1 Overlapping measurement windows

3.5.2 Timing metric measurements

3.5.3 Saving metric measurements