I want to define customer churn accurately for the data showing seasonal patterns of not-purchasing.
Our customers purchase on the regular basis most time of the year, with approx. 97% of all orders having less than 31 days gap between them. However, about 75% of our customers will have at least 1 (max 9) long breaks between two orders: ranging from 32 to 200 days each. This pattern is characteristic for festive seasons: Christmas, Easter, summer holidays etc. Even though those orders with long gaps account for about 3% of all orders, less than 1% of them will become a "final" or last order, so they are not good at predicting churn.
How could I approach this problem to be able to better define and then model churn? Any suggestions are welcome. Feel free to ask for more info if needed