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Author's profile photo Tammy Powlas

Customer Retention Use Case Updated

I’m following up from an old document Burn that Churn?  Checking Retention of Customers in Predictive Analysis and doing the same analysis but this time using SAP Predictive Analytics 2.2 Expert Analytics – the same data, the same model.  We’re looking at the attributes of customers retained and comparing them to those who have left (banking sample customers)


Since we are trying to predict churn, that is the target variable, using the amount of deposits, investment amount, income and number of checking transactions.


After running the model, you can see the execution status above.


Above is the model summary


What is different from when I originally posted?  The above shows a confusion matrix, with a false positive 6% of the time, true positive 94% and a false negative 1% of the time after outputting the results to Excel:

Predicted Predicted
Actual No Yes Total Actual No Yes
No 1458 85 1543 No 94% 6%
Yes 14 986 1000 Yes 1% 99%
Total 1472 1071 2543


I think you also need a big screen to better see/view the decision tree:


The predicted values are the same as before, with younger people likely to “churn” and not be retained by the bank:



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      Author's profile photo Antoine CHABERT
      Antoine CHABERT

      Nice example Tammy - I enjoyed reading it!

      Based on the analysis and due to my soon-to-be-40 age, I am not very likely to "churn" my bank.