## [1] "concrete-braid-170614"

1 Introduction

Segments are a fantastic way to organize the results of an analysis. There are, however, a few limitations of using segments in the standard (free) version of GA:

  1. They cause reports to become sampled after 50,000 sessions
  2. Only 4 segments can be compared at one time
  3. Segments are saved under your Google account which make them hard to share
  4. When comparing segments, it’s hard to tell how much they overlap

All of these limitations can be resolved by bringing your Google Analytics data into R with the googleAnalyticsR library, but this post will focus on #4 above: Understanding segment overlap.

2 The Problem with Segment Overlap

Segments are fairly straight forward to create in GA, but can trip users up in a number of ways. One common issue is when users fail to account for segment overlap. Why should you care whether your segments overlap? Because you’ll want to interpret your segment metrics entirely different depending on the answer. Let me explain via a scenario I see fairly often.

Sally is a marketing director in charge of a major pet retailer’s website redesign. She worked with her branding agency to develop 3 different personas that they expect to find on their website: Cat Lovers, Dog Lovers, and Wholesale distributors. The UX of the website is designed to tailor to these personas and Sally is confronted with the question of how to report on website success. A natural decision is to frame the reporting KPIs around the personas developed earlier. She instructs her analytics team to create segments based on their personas.

Here’s where things start to break down. The analytics team is left to decide what behavior on the website indicates whether a user is one of those 3 personas. A very reasonable-seeming decision may be as follows:

  • Users who visit the /cats section are included in the ‘Cat lovers’ segment
  • Users who visit the /dogs section are included in the ‘Dog lovers’ segment
  • Users who log in and visit the ‘bulk order’ section are included in the ‘Wholesalers’ segment.

A week after launch, the analytics team presents the following results:

  • Dog Lovers - 500 users, 14% conversion rate
  • Cat Lovers - 400 users, 15% conversion rate
  • Wholesalers - 200 users, 31% conversion rate

Amazing! Sally loves these numbers. The only problem is that they’re meaningless. What the analytics team failed to consider is that their wholesalers always browse the /cats and /