The data you need to build a churn model
Building a predictive churn model for your business starts with categorizing everything you know about your customers. Most businesses are already tracking this data, so you just have to know how to use it. Every customer data point you have helps build a more targeted churn model.
The first step is building comprehensive customer profiles. At their core, these profiles should include the customer’s name and address but can be expanded to include job title, employment status, team size, and much more.
With this data, you can easily spot patterns in churned customers related to their demographics and segment them into cohorts for more granular analysis. Different customer types will churn in significantly different ways.
With Retain, businesses can track customer cohorts over time:
Expand on your customer profiles by including information about their purchase and billing history. Knowing when a customer signed up, when they canceled your service, their payment history, and overall lifetime value (LTV) helps you build a clear picture of how billing processes impact your churn.
As a SaaS company, it’s important to include a customer’s chosen pricing tier in your churn data as well. This information helps you see how your pricing decisions affect the way customers churn from your service.
One of the biggest contributors to voluntary churn is the customer experience. Make sure you’re tracking every interaction a customer has with your team as well as your product. Including this information in your customer profiles helps you see the impact of your product and the customer experience on churn rates.
Tracking past interactions can also be valuable for surfacing points along the customer journey where churn is more likely to occur.
Customer profiles are the basis for more in-depth churn analysis. With this data, you can start looking for patterns in how and why different types of customers leave your service.