Mobile Phone Providers

Prediction Example

Chapter 8: Predicting Churn for Mobile Phone Providers

In Book 12: Operations Research Applications for Decision Analysis and Prediction

ISBN: 978-620-5-49772-2

Author: Vojo Bubevski (Independent Researcher)

Abstract 

This chapter presents a prediction of the churn for mobile phone providers. The term “churn” is a marketing term, meaning that a customer transfers loyalty from one provider to another. This prediction is particularly relevant in the industry, where companies try to keep their customers from churning. The prediction will give the company a better understanding of what behaviour leads to churning, and implement actions for preventing such behaviour. The snapshots of 1005 customer data are available, about 14% of whom have churned. Thus, this data is used to predict the Churn “Yes-No” variable. The Churn values for the first 15 customers are predicted by using Neural Network.

Keywords: Prediction, Risk Analysis, Mobile Phone Providers, Churn Prediction, Neural Networks.

 The Results

The prediction is that 10 customers will not churn (i.e., Churn is “No”) and five customers will churn (i.e., Churn is “Yes”). For example:

1.      Customer 1 will not churn with a probability of 0.941 (94.1%); There is a 0.059 (i.e., 5.9%) probability that this customer will churn;

2.      Customer 2 will not churn with a probability of 0.24 (24%); There is a 0.76 (76%) probability that this customer will churn; etc.

Prediction Model results are presented in Table 2.

Table 2: Prediction Results for 15 Customers

Customer Index

Tag Used

Churn Prediction

Prediction Probability

Churn No Probability

Churn Yes Probability

1

predict

No

0.940650119

0.940650119

0.059349881

2

predict

Yes

0.7596276

0.2403724

0.7596276

3

predict

No

0.994378864

0.994378864

0.005621136

4

predict

Yes

0.887945223

0.112054777

0.887945223

5

predict

Yes

0.721058989

0.278941011

0.721058989

6

predict

No

0.986949178

0.986949178

0.013050822

7

predict

No

0.798413796

0.798413796

0.201586204

8

predict

Yes

0.616005121

0.383994879

0.616005121

9

predict

No

0.998980502

0.998980502

0.001019498

10

predict

No

0.707566873

0.707566873

0.292433127

11

predict

No

0.991408537

0.991408537

0.008591463

12

predict

No

0.990296261

0.990296261

0.009703739

13

predict

No

0.653186806

0.653186806

0.346813194

14

predict

No

0.997827701

0.997827701

0.002172299

15

predict

Yes

0.969873187

0.030126813

0.969873187