Assignment 9

Objective

Predict Credit Card Attrition using the CreditCardData, using the randomforest algorithm from the randomForest package.

Data Prep

• Split the data (10127 observations), into training and validation datasets (70%/30% , 7056/3071) ## Variable Importance
• The total transaction count is the variable with the greatest importance in determining Attrition

Model Accuracy

Using the validation data, it is observed that the model can:

• Incorrectly predicted 90 attrited customers as existing customers
• Correctly predicted an existing customer

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  2990 
## 
##  
##                    | val$predicted_Attrition_Flag 
## val$Attrition_Flag | Attrited Customer | Existing Customer |         Row Total | 
## -------------------|-------------------|-------------------|-------------------|
##  Attrited Customer |               392 |                90 |               482 | 
##                    |          1558.549 |           253.999 |                   | 
##                    |             0.813 |             0.187 |             0.161 | 
##                    |             0.936 |             0.035 |                   | 
##                    |             0.131 |             0.030 |                   | 
## -------------------|-------------------|-------------------|-------------------|
##  Existing Customer |                27 |              2481 |              2508 | 
##                    |           299.530 |            48.815 |                   | 
##                    |             0.011 |             0.989 |             0.839 | 
##                    |             0.064 |             0.965 |                   | 
##                    |             0.009 |             0.830 |                   | 
## -------------------|-------------------|-------------------|-------------------|
##       Column Total |               419 |              2571 |              2990 | 
##                    |             0.140 |             0.860 |                   | 
## -------------------|-------------------|-------------------|-------------------|
## 
## 

Financial Impact

• We offered to forgive 419 annual fees at $100 each (419 x $100 = $41900)
• 27 customers of the 419 were going to stay anyway. However, using our customer retention program we can keep 30% of the 392 customers that will actually leave
• This means that we will convince 118 customers to stay (30% x 392)
• The company makes $2300 per customer so through the retention program we keep $271400 (118 x $2300 = $271400)
• We net $229500 ($271400 - $41900)
• So for every 2990 customers we generate $229500
• We save approximately $77 per customer