Use customer churn predictive analytics to identify customers who are likely to leave and develop strategies to improve customer retention and reduce churn.

1. Customer Churn
Predictive Analytics Use Case

2. A utility company can determine what kinds of customers
are most likely to churn, turn over or leave, and which ones
are most likely to remain loyal. This technique can be used
to predict whether a particular customer will churn and
when it will happen and to understand why particular
customers leave.
Customer Churn
Sample Application
Description

3. Customer Churn
Sample Application
Target
Customer Churn Status

4. • Services that each customer uses – phone, multiple lines,
internet, online security, online backup, device
protection, tech support, and streaming TV and movies
• Customer account information – how long they have
been a customer, the contract, payment method,
paperless billing, monthly charges, and total charges
• Demographic information about customers – gender, age
range, and if they have partners and/or dependents
Customer Churn
Sample Application
Influencing
Factors

5. Binary Logistic Regression is the method used for classifying
numeric and/or categorical data into two groups based on
predefined categories.
• Higher classification accuracy (>=70%) means the results
are reliable and accurate.
• Lower classification accuracy (<70%) means the model
needs to be rebuilt using different input parameters.
Customer Churn
Sample Application
Algorithm(s)

6. Customer Churn
Sample Application
Model
Visualisation

7. Customer Churn
Sample Application
Model
Visualisation

8. Customer Churn
Sample Application
Model
Visualisation

9. Customer Churn
Sample Application
Model
Summary

10. Interpretation
Customer Churn
Sample Application

11. Customer Churn
Sample Application
Result
• Likelihood/probability of churn.
• Flag containing ‘likely to churn’ and ’unlikely to churn’
information with ‘yes’ and ‘no’ values.

12. Customer Churn
Sample Application
Result
Churn prediction with probability value can be carried out
using APPLY functionality shown below.

13. Customer Churn
Sample Application
Result

14. Customer Churn
Predictive Analytics Use Case
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Smarten – Customer Churn Use Case - 2019