Implementing churn prediction involves identifying the customers most likely to leave a company at the end of their contract, so as to be able to offer them a targeted retention package. In R, this problem is treated as a classification orclass probability estimation task, since the objective is to predict a categorical variable (the customer leaves or stays).
Here are the key steps for implementing this prediction with R, based on supervised learning:
1. Data preparation and attribute selection Prior to modeling, it is crucial to gather historical data (age, income, service usage, customer service calls, etc.) where the target (the actual departure of the customer) is already known. The R packages dplyr and tidyr facilitate the cleaning and manipulation of these complex data sets. Then, to identify the most predictive variables, we use metrics such as information gain (based on entropy). In practical examples on churn, variables such as the value of the customer’s home or package overruns often turn out to have a higher information gain than the declared level of satisfaction.
2. To avoid overfitting, where the model simply memorizes the data without being able to generalize, the dataset is randomly divided into a training set (to build the model) and a test or validation set (to evaluate its effectiveness). You can implement several algorithms via R :
- Logistic regression: models the probability (log-odds) of a customer cancelling. In R, we use the native function
glm()by specifying the binomial distribution:glm(formule, family=binomial(link="logit"), data=mydata). - Decision Trees: recursively segment customers into subgroups sharing similar attrition probabilities, resulting in highly intelligible rules. R’s rpart package can be used to generate them with the
rpart()function. The resulting tree can then be simplified via the functionprune()to improve its generalization capability. - Random Forests: This ensemble method creates multiple decision trees to increase classification accuracy. It is easily implemented with the
randomForest()function in the eponymous R package.
3. Performance evaluation Once the model has been trained, R’s predict() function is applied to the test set to check how the model reacts to new data. To evaluate the quality of the model, several methods are essential:
- Confusion matrix: This crosses predictions (e.g.: the model says “will leave”) with reality (the customer has actually left), to extract the rates of true positives, false positives, etc…
- Precision metrics: R can be used to calculate sensitivity, specificity and overallaccuracy. However, in the case of churn, where departures are in the minority (class imbalance), accuracy alone is often misleading.
- Graphical visualizations: ROC, Lift and Profit curves can be used to visually compare several models (e.g. to compare the performance of a
rparttree against aglmregression) and to adjust the probability threshold triggering the retention offer.
4. Integration into the Expected Value Framework A common mistake is to target only those customers with the highest mathematical probability of leaving. In business, the real problem is not the customer’s departure per se, but the associated financial loss. The final step is to couple the probabilities derived from R with an Expected Value equation integrating the costs and benefits of the retention offer. The optimal decision targets those customers for whom the campaign is most profitable. Mathematically, we seek to maximize the gain:VT=Δ(p)⋅uS(x)-c, where Δ(p) is the difference in probability that the customer will stay thanks to the offer, uS(x) its financial value if he stays, and c the cost of the campaign. This analytical engineering step requires the creation of two distinct predictive models: one estimating the probability of the customer staying if targeted, and another if not.
