Reduce customer churn in a telco using machine learning

Contents

1. Application type.
2. Data set.
3. Neural network.
4. Training strategy.
5. Model selection.
6. Testing analysis.
7. Model deployment.

1. Application type

This is a classification project since the variable to be predicted is binary (churn or loyal customer).

The goal is to model churn probability conditioned on the customer features.

2. Data set

The data file telecommunications_churn.csv contains a total of 19 features for 3333 customers.

Each row corresponds to a client of a telecommunications company for whom information has been collected about the type of plan they have contracted, the minutes they have talked, or the charge they pay every month.

Variables

The data set includes the following variables:

Variables distribution

The first step of this analysis is to check the distributions of the variables. The following figure shows a pie chart of churn and loyal customers.

As we can see, the annual churn rate in this company is almost 15%. Also, we observe that the dataset is unbalanced.

Inputs-targets correlations

The inputs-targets correlations might indicate to us what factors are most influential for the churn of customers.

Here, the most correlated variable with churn is international_plan. A positive correlation here means that a high ratio of customers with an international plan leaves the company.

2. Neural network

The second step is to choose a neural network to represent the classification function. For classification problems, it is composed of:

• A scaling layer.
• Two perceptron layers.
• A probabilistic layer.

Scaling layer

For the scaling layer, the mean and standard deviation scaling method is set.

Perceptron layers

We set 2 perceptron layers, one hidden layer with 3 neurons as a first guess and one output layer with 1 neuron, both layers having the logistic activation function.

Probabilistic layer

Finally, we will implement the continuous probabilistic method for the probabilistic layer.

Network architecture

The architecture shown below consists of 18 scaling neurons (yellow), five neurons in the first layer (blue), and one probabilistic neuron (red).

4. Training strategy

The next step is to select an appropriate training strategy that defines what the neural network will learn. A general training strategy is composed of two concepts:

• A loss index.
• An optimization algorithm.

Loss index

As we saw before, the data set is unbalanced. In that way, we set the weighted squared error.

Training process

The following chart shows how the loss decreases during the training process with the iterations of the Quasi-Newton method.

As we can see, the final training and selection errors are training error = 0.384 WSE and selection error = 0.455 WSE, respectively.

5. Model selection

The objective of model selection is to find the network architecture with the best generalization properties, which minimizes the error on the selected instances of the data set.

We aim to develop a neural network with a selection error of less than 0.455 WSE, which is the value we have achieved so far.

Order selection algorithms train several network architectures with a different number of neurons and select that with the most minor selection error.

The incremental order method starts with a few neurons and increases the complexity at each iteration.
The following chart shows the training error (blue) and the selection error (orange) as a function of the number of neurons.

As we can see, the optimal number of perceptrons in the first layer is 2, and the optimum error on the selected instances is 0.455 WSE.

6. Testing analysis

The testing analysis aims to evaluate the performance of the trained model on new data that have not been used for training or selection.

For that, we use the testing instances.

ROC curve

The ROC curve measures the discrimination capacity of the classifier between positive and negative instances.

The following chart shows the ROC curve of our problem.

The proximity of the curve to the upper left corner means that the model has an excellent capacity to discriminate between the two classes.

The most important parameter from the ROC curve is the area under the curve (AUC). This value is 0.5 for a random classifier and 1 for a perfect classifier.

For this example, we have AUC = 0.896, indicating that the model performs well in predicting our customers’ churn.

Confusion matrix

The following figure shows the confusion matrix.

The binary classification tests are calculated from the values of the confusion matrix.

• Classification accuracy: 91.2% (ratio of correctly classified samples).
• Error rate: 8.8% (ratio of misclassified samples).
• Sensitivity: 76.9% (percentage of actual positives classified as positive).
• Specificity: 93.9% (percentage of actual negatives classified as negative).

These binary classification tests show that the model can predict most instances correctly.

7. Model deployment

In the model deployment phase, the neural network can predict outputs for inputs it has never seen.

Neural network outputs

We can calculate the neural network outputs for a given set of inputs:

• account_length: 101.065.
• voice_mail_plan: 1.
• voice_mail_messages: 8.09901.
• day_mins: 179.775.
• evening_mins: 200.98.
• night_mins: 200.871.
• international_mins: 10.2373.
• customer_service_calls: 1.56286.
• international_plan: 0.
• day_calls: 100.436.
• day_charge: 30.5623.
• evening_calls: 100.114.
• evening_charge: 17.0835.
• night_calls: 100.108.
• night_charge: 9.03933.
• international_calls: 4.47945.
• international_charge: 2.76457.
• total_charge: 59.4498.

The predicted churn for these inputs is the following:

• churn: 0.047.

Conclusions

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