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Real-time and customized scoring of customer engagement

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Insight Data Science project

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Real-time and customized scoring of customer engagement

  1. 1. SaaS Customers SaaS Subscription Hierarchy
  2. 2. SaaS Customers SaaS Subscription Hierarchy Churn is Expensive
  3. 3. SaaS Customers The startup company that I consulted for provides an analytics platform for customer retention to SaaS companies
  4. 4. SaaS Customers 0 0 Deliverable: Metric that identifies which customers to contact first. + Happy Unhappy Very Unhappy
  5. 5. 8 GB 100+ companies 100+ tables
  6. 6. 8 GB 100+ companies 100+ tables Subscription price, period, quantity, renewal history
  7. 7. 8 GB 100+ companies 100+ tables Subscription price, period, quantity, renewal history Frequencies Contents
  8. 8. 8 GB 100+ companies 100+ tables Subscription price, period, quantity, renewal history Frequencies Contents
  9. 9. 8 GB 100+ companies 100+ tables Subscription price, period, quantity, renewal history Frequencies Contents Random Forest Classifier (Scikit-learn) Churn? Yes or No
  10. 10. 81% 19% True Non-Churn True Churn Hypothetical Total Number of Customers = 10000
  11. 11. ? RF gives out feature importance scores of individual companies Random Forest Classifier (Scikit-learn) ?
  12. 12. ? Random Forest Classifier (Scikit-learn) ? Churn? RF gives out feature importance scores of individual companies
  13. 13. → → False Positive False Negative Predicted ChurnNon-Churn True Non-ChurnChurn Confusion Matrix 928 49 45073 Precision-Recall Curve Precision Recall All customer numbers are hypothetical (scaled).
  14. 14. Metric: Sign of change in the satisfaction level of a customer when contacted today. 0 3 6 Number of Communications per Week Emma 1.0 0.8 0.6 0.4 0.2 0 ProbabilityforRenewal 9 12 15 18 21
  15. 15. Metric: Sign of change in the satisfaction level of a customer when contacted today. 0 3 6 Number of Communications per Week Emma 1.0 0.8 0.6 0.4 0.2 0 ProbabilityforRenewal 9 12 15 18 21
  16. 16. 0 3 6 Number of Communications per Week Customer A Customer B 1.0 0.8 0.6 0.4 0.2 0 ProbabilityforRenewal 9 12 15 18 21 Metric: Sign of change in the satisfaction level of a customer when contacted today. Emma Olivia
  17. 17. Metric: Sign of change in the satisfaction level of a customer when contacted today. 0 3 6 Number of Communications per Week Emma Olivia 1.0 0.8 0.6 0.4 0.2 0 ProbabilityforRenewal 9 12 15 18 21
  18. 18. • • • • •
  19. 19. Startup Co. (SaaS) SaaS 1 SaaS 2 SaaS 3 SaaS 4 Companies showing high feature importance scores SaaS 1: Database SaaS 2: Web design SaaS 3: Advertisement SaaS 4: Analytics platform / dashboard SaaS Group 1 Group 2 Group 3 Group 4 Customer segments showing high feature importance scores Group 1: Age > 40, Asian Group 2: Age < 40, Asian Group 3: Age > 40, Non-Asian Group 4: Age < 40, Non-Asian
  20. 20. SaaS 1 SaaS 2 …. SaaS 4 amount_per _day client_note_ total_count last_sub_ duration sentiment sub_ duration churn? Customer 1 1 0 …. 0 $10 1 365 -0.1 1000 Yes Customer 2 1 0 …. 0 $9 2 365 0.2 1230 No Customer 3 0 1 …. 0 $12 4 700 -0.4 700 Yes Customer 4 0 0 …. 1 $20 0.2 500 0 780 No ⁞ ⁞ ⁞ ⁞ ⁞ Customer 10000 0 1 …. 0 $10 0.1 180 0.5 180 No * Hypothetical numbers are used.
  21. 21. SaaS companies showing the large numbers of churn cases
  22. 22. customers (70%) (30%) Train set Validation set

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