So far, this is not the reinvention of the wheel; probably every company has a similar calculation. So why do we need ML models and advanced statistics for churn prevention?
Because without ML models, churn is only an event. At a certain point, a customer decides to leave, and we have no tools in our hands to do anything about it. We can only see what happened in the past and evaluate it, but it does not provide a data-driven approach to detect which customers are likely to leave in the future.
ML models, on the other hand, analyze historical data and try to find recurring patterns in customer behavior that led to churn. This, however, is only the first part of the task. Once these patterns are identified, the model applies them to the current customer base and provides signals like:
“Based on the common behavior patterns of customers who left, these are the customers in the current base who behave similarly, indicating that they are likely to churn in the near future.”
And this is exactly what a company needs: signals or early warnings that identify potential leavers, so proactive retention actions can be taken.
How do ML models work in churn analytics, and which models are commonly used?
- Feature selection – understanding what drives churn
First, we analyze the characteristics and behavior of each customer: age, geographical location, income, usage patterns, and everything available to us. Our goal is to identify which variables have an impact on the target event: whether the customer leaves (churn = 1) or stays (churn = 0).
At this stage, we focus on identifying relevant features, meaning the variables that potentially influence churn. The exact combination of features is not fixed; it is recommended to test different combinations and refine them iteratively.
- Model building – learning patterns from historical data
Once we have selected the features, we build an ML model that learns from historical data. The model identifies patterns and relationships between customer behavior and churn, and uses these patterns to predict the likelihood of churn for current customers.
- Commonly used models in churn analytics
- Logistic Regression
A simple and widely used model. It assigns weights to each feature and combines them into a score between 0 and 1, which represents the probability that a customer will churn. It is easy to interpret and useful as a baseline model. - Random Forest
An ensemble model that builds multiple decision trees and combines their results. Each tree captures different patterns in the data, and together they provide a more robust and accurate prediction, especially when relationships are complex. - XGBoost
A more advanced boosting algorithm that builds trees sequentially, where each new tree focuses on correcting the errors of the previous ones. It is often one of the best-performing models in churn prediction tasks.
It is important to note that the model itself does not make business decisions. It does not know the cost of retention or the value of a customer. It only provides a probability. The final decision always belongs to the business.
How to evaluate and use the results in a business context
Example:
- Average cost of retaining a customer (e.g., campaign, discount): 50 USD
- Average customer value: 200 USD
Case 1: High churn probability
If the churn probability is 0.9, the expected loss is:
0.9 × 200 = 180 USD
If we spend 50 USD on retention and reduce the churn probability to 0.2:
0.2 × 200 = 40 USD
The reduction in expected loss is:
180 − 40 = 140 USD
Since we spent 50 USD to save 140 USD, the intervention is clearly profitable.
Case 2: Lower churn probability
If the initial churn probability is 0.4:
0.4 × 200 = 80 USD
After the campaign (reducing it to 0.2):
0.2 × 200 = 40 USD
The reduction in expected loss is:
80 − 40 = 40 USD
Here, we spent 50 USD to save 40 USD, which means the intervention is not profitable.
Conclusion
ML models help us identify who is likely to leave, but the real value comes from combining these predictions with business metrics. The goal is not only to predict churn, but to take actions where the expected benefit is higher than the cost.