I have been looking at machine learning for customer churn and there are lots of examples of customer churn that are really helpful. My question is when I am training my model and I have all my customers and attributes how do I take into consideration normal transactional information.
Every example I have seen there is only one row for each customer with several columns of attributes. What do I do if I want to take transactional data in consideration i.e. normal spending habits? Do I need to make attributes that compare certain transactions for the current month vs the average of the last 12 months(an example) or is there a way to just include the transactional information?
|Customer ID||Number of transactions||month||number of transaction type 1||number of transaction type 2|
The transaction type 2 has reduced therefore the customer is likely to leave or
Customer ID, Number of transactions, Current number of transaction type 1 v average, Current number of transaction type 1 v average
I could have got it wrong completely both ways but any constructive input would be much appreciated.