Assignment 9
Victoria Pitonzo & Matthew Volpe
2024-04-18
Objective
Predict Customer Attrition using the CreditCardData from the randomForest package, using the randomforest algorithm.
Data Prep
Split the data (10127 observations), into training and valdiation datasets (70%/30% , 7116/3011)
The total transaction count is the variable with the most importance.
Accuracy of the Model
Using the validation data, it is observed that the model:
- Predictly predicted an existing customer
- Incorrectly predicted 90 attritted customers
##
##
## 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 430 annual fees at $100 each.
- 39 of the 430 were going to stay away anyway. However, using our customer retention program we can keep 30% of the 391 customers that will actually leave.
- The company makes $2300 per customer so through the retention program we keep $271400 (118 x $2300 = $271400)
- We net $229500 ($271400 - $41900)