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Customer Churn Augmented Analytics Use Case - Smarten Slide 1 Customer Churn Augmented Analytics Use Case - Smarten Slide 2 Customer Churn Augmented Analytics Use Case - Smarten Slide 3 Customer Churn Augmented Analytics Use Case - Smarten Slide 4 Customer Churn Augmented Analytics Use Case - Smarten Slide 5 Customer Churn Augmented Analytics Use Case - Smarten Slide 6 Customer Churn Augmented Analytics Use Case - Smarten Slide 7 Customer Churn Augmented Analytics Use Case - Smarten Slide 8 Customer Churn Augmented Analytics Use Case - Smarten Slide 9 Customer Churn Augmented Analytics Use Case - Smarten Slide 10 Customer Churn Augmented Analytics Use Case - Smarten Slide 11 Customer Churn Augmented Analytics Use Case - Smarten Slide 12 Customer Churn Augmented Analytics Use Case - Smarten Slide 13 Customer Churn Augmented Analytics Use Case - Smarten Slide 14
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Customer Churn Augmented Analytics Use Case - Smarten

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

Customer Churn Augmented Analytics Use Case - Smarten

  1. 1. Customer Churn Predictive Analytics Use Case
  2. 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. 3. Customer Churn Sample Application Target Customer Churn Status
  4. 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. 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. 6. Customer Churn Sample Application Model Visualisation
  7. 7. Customer Churn Sample Application Model Visualisation
  8. 8. Customer Churn Sample Application Model Visualisation
  9. 9. Customer Churn Sample Application Model Summary
  10. 10. Interpretation Customer Churn Sample Application
  11. 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. 12. Customer Churn Sample Application Result Churn prediction with probability value can be carried out using APPLY functionality shown below.
  13. 13. Customer Churn Sample Application Result
  14. 14. Customer Churn Predictive Analytics Use Case For more information, contact us today. www.Smarten.com contact@Smarten.com Smarten – Customer Churn Use Case - 2019
  • SarahWyant2

    Nov. 26, 2021

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

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