Does churn prediction require a FLAT table?

Hello friends,

I am building the churn prediction model.

Most of my data consists of  transactions (several rows for each customer) with dates: purchases, logins,

calls, etc. I also have a table that has a churn date for each customer.

Is there a way / algorithm that will make a prediction without me having to "flatten" the table to one row per customer?

I am asking not because of the amount of work, but because when flattening the data to measures I might miss out some important explanatory variable that I will not think about.

For example: let's say I will create a measure "Number of purchases in the last month" but it will not predict the churn. While the real sign of the forthcoming churn is the "decrease percentage of number of purchases in this month compared to two months before".... Or the real explaining variable would be the "weekly frequence of purchases", etc.

I hope you understand what I mean. 

Is there a way to do it?

Thank you


Hi Michael,

There is no algorithm that could automatically guess such a variety of features - you will have to create them yourself.

For example, you could use a Lag Column node and then calculate the difference between current and previous purchases. Or you could aggregate the data to get weekly purchases and use that as a feature. It is certainly true that you miss some information when you flatten the data, but if you create your features of interest beforehand, that should be less of a problem.