Churn prevention in online marketing

Machine Learning for Marketing Analytics in R

Verena Pflieger

Data Scientist at INWT Statistics

Churn prevention

Binary logistic regression

1) Probability to churn

$$ P(Y=1)$$

2) Log odds

$$ \log \frac{P(Y=1)}{P(Y=0)} = \beta_0 + \sum\limits_{p=1}^P \beta_p x_p $$

3) Odds

$$ {\frac{P(Y=1)}{P(Y=0)} = e^Z ,\text{ with} \quad Z = \beta_0 + \sum\limits_{p=1}^P \beta_p x_p } $$

4) Probability to churn

$$ P(Y=1) = \frac{e^Z}{1 + e^Z} $$

Data discovery I

## 'data.frame': 45236 obs. of 21 variables:

## $ ID            : Factor w/ 45236 levels "1","3","5","7",..
## $ orderDate     : Date, format: "2014-12-23" "2014-09-10" ....
## $ title         : Factor w/ 4 levels "Mr","Company",..: 1 1 1 ...
## $ newsletter    : Factor w/ 2 levels "No","Yes": 0 0 0 1 ...
## $ websiteDesign : Factor w/ 3 levels "1","2","3": 2 1 1 3 ...
## $ paymentMethod : Factor w/ 4 levels "Cash","Credit Card",..: 3 4 ...
## $ couponDiscount: Factor w/ 2 levels "No","Yes": 1 0 0 0 0 1 ...
...
## $ returnCustomer: Factor w/ 2 levels "No","Yes": 0 0 0 0 ...

Data discovery II

ggplot(churnData, aes(x = returnCustomer)) +
    geom_histogram(stat = "count")

Let's start analyzing!

Machine Learning for Marketing Analytics in R