Aggregation & Segmentation Analysis

Understanding customers begins with the basics

Aggregation takes a set of data and pulls it into a single value. For example, the number of unique active users over the last 30 days, or the total number of times a given product feature was used in the last week. When viewed over time, aggregated metrics show a trend of changes in customer behavior.

Segmentation lets you define a group of customers by their characteristics. For example, accounts with MRR over $2,000, or users that have used a certain product feature in the last week. You can then view aggregated metrics over just the customer segments you are interested in, allowing you to compare and contrast these segments.

Aggregation and segmentation are a good place to start for any analysis. They give you a rapid understanding of an account, and allow you to test assumptions.

For example, you can compare and analyze the differences between successful accounts (e.g. those with a high health score) and accounts at risk of churning (e.g. those with open churn alerts).

  • How frequently does each group use the product?
  • Do they use different feature sets?

Identify factors that affect success within segmentation

You might discover that certain features used mainly by successful customers provide more value, and initiate campaigns to drive usage of those features by other customers.

You can dive even deeper using segmentation analysis to analyze sub-segments of your customers.

  • How do healthy and struggling companies compare within individual industries, stages, geographies, etc?

Understanding the factors that affect success within different segments can allow you to create action plans that more directly target those customers’ issues. You can even compare an individual at-risk account to similar accounts (e.g. same industry, stage, size) that are doing well to develop recovery strategies.

Map the profile of successful customers

Aggregation and segmentation can also help Customer Success Managers understand the profile of successful accounts.

  • Do healthy, active accounts tend to come from certain lead sources?
  • Do they use the product in different ways than other customers?
  • Are they of a certain size or in a specific market or geography?

By understanding the profile of successful customers, you can help your marketing team target more customers like these. Understanding and focusing acquisition programs on customers who are more likely to succeed with your solution can accelerate the growth of your company.  

Testing your hypotheses

To get the most value from these basic analytics, you should develop a set of hypotheses you want to test and then focus your efforts on exploring those. Also remember that correlation does not imply causation. Just because two metrics increase together does not mean that one is causing the other to increase --- there may be another factor at play.  

The challenge of using aggregations and segmentation is the vast amount of data there is to explore. Over time, customer success teams will develop intuitions that will speed this process, while other advanced analytics will help to gain additional insights.

Have more questions? Submit a request

Comments