Classification Trees

Objective

Using the Credit Card data set, we are trying to predict how many people will leave and how much it would cost to retain them and costs associated with a custom retention program.

Data

In the data set there are 10,127 observations. We are splitting it into a training and validation data set, consisting of 70% and 30% of the observations respectively. There are 19 predictors total.

Variable Importance

Interesting how Age is perceived by many as a predictor for customers leaving. However, the data shows that it is not a good predictor.

Model Accuracy

Using the validation data, it is observed that the model is able to:

• Predict 419 would leave and 2571 would stay
• Incorrectly predicted that someone would leave when they actually stayed 6.4% of the time
• Correctly predicted someone would leave 93.6% of the time

## 
##  
##    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 |                   | 
## -------------------|-------------------|-------------------|-------------------|
## 
## 

Retention Program Cost Analysis

The financial impact of the model, given the following assumptions:

• The customer retention program consists of forgiving the annual fee of $100 (on average)
  • 419 x $100 = $41,900
• The value of a customer is $2,300 per year (on average)
  • Customer retention programs have a 30% success rate
    • 30% of 392 = 118
    • 118 * $2,300 = $271,400
    • $271,400 - $41,900 = $229,500 / 2,990 = $76.76 per customer