Building a predictive churn model helps you make proactive changes to your retention efforts
It is no secret that customer retention is a top priority for many companies; acquiring new customers can be several times more expensive than retaining existing ones. Furthermore, gaining an understanding of the reasons customers churn and estimating the risk associated with individual customers are both powerful components of designing a data-driven retention strategy. A churn model can be the tool that brings these elements together and provides insights and outputs that drive decision making across an organization.
What is Churn?
In its simplest form, churn rate is calculated by dividing the number of customer cancellations within a time period by the number of active customers at the start of that period. Very valuable insights can be gathered from this simple analysis — for example, the overall churn rate can provide a benchmark against which to measure the impact of a model. And knowing how churn rate varies by time of the week or month, product line, or customer cohort can help inform simple customer segments for targeting as well.
However, churn is often needed at more granular customer level. Customers vary in their behaviors and preferences, which in turn influence their satisfaction or desire to cancel service. Therefore, a cohort-based churn rate may not be enough for precise targeting or real-time risk prediction. This is where churn modeling is usually most useful.
The output of a predictive churn model is a measure of the immediate or future risk of customer cancellation. This is what the term “churn modeling” most often refers to, and is the definition we will adhere to in this post.
Note that the rows in the above matrix are not mutually exclusive: Involuntary churn can be present in either contractual or non-contractual settings.
Churn is especially relevant in contractual circumstances, which are often referred to as a “subscription setting,” as cancellations are explicitly observed. However, non-contractual businesses also benefit from modeling churn. The challenge, in those case, lies in defining a clear churn event timestamp. This is often done by finding a certain threshold for a period of inactivity and using it as a definition for the churn event.
On the other hand, voluntary and involuntary churn might be caused by different underlying factors. Voluntary churn is often more prevalent than accidental churn due to events such as payment failures. It is also more difficult to determine the root cause of voluntary customer cancellations, which is why most churn literature focuses on voluntary churn events. While both voluntary and non-voluntary cancellations have a clear revenue impact, it is best to focus a churn model on only one type of churn.
We can classify customer churn by grouping cases into different categories:
Contractual Churn: This type of churn is applicable to businesses that provide different services such as cable companies. It happens when customers decide not to continue with their expired contracts.
Voluntary Churn: It refers to the situation in which customer decides to cancel their existing service and can be applicable for companies that provide a service not based in a fixed-term contract, such as prepaid cellphones or streaming subscriptions.
Non-contractual Churn: This type of churn, which is applicable to businesses that depend on retail locations or online stores as an example, can be associated with consumers leaving a possible purchase without completing the transaction.
Involuntary Churn: This happens when a customer can no longer stay with the credit card company or can not pay their credit card bill.
Use Cases
The probability of churn can be predicted using various statistical or machine learning techniques. These methods process historical purchase and behavior data in order to predict the probability of cancellation per customer.
A well-constructed model can inform a wide range of decisions and flow into numerous internal tools or applications. For example, some common use cases for a churn model are:
- Measuring feature impacts on the likelihood of churn in order to understand why customers choose to leave, which can inform long-term retention initiatives
- Creating churn risk scores that can indicate who is likely to leave, and using that information to drive retention campaigns
- Predicting the probability of churn and using it to flag customers for upcoming email campaigns
- Integrating outputs with internal apps, such as a customer call center, to provide relevant real-time churn risk information
- Discounting strategically with promotion campaigns to customers with a high cancellation risk
- And many more …
