Solved – Temporal abstraction in Churn analysis: Why do we need it

Could you explain the need of temporal abstraction in churn analysis intuitively with a simple example? I tried Google but there are not any clear answers , especially for churn analysis.

Temporal abstraction is fancy pants jargon for changing the reference point of a time series variable. It is a type of variable transformation.

A common example is that you have behavioral data that is indexed by calendar time, like number of minutes used in a given month on a cellular plan. This is typically how this sort of data is stored in a database. You can change the reference point to be time before churning takes place, or perhaps the current month if the customer has not churned. This often allows patterns to emerge that predict churn. For instance, a customer might slow down how quickly she ships back Netflix DVDs in the three months before terminating the subscription service completely. You can use that information to intervene somehow.

Here's graphical example, where the colors correspond to behavior and T-1 is the month before churning:

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Source: Handbook of Statistical Analysis and Data Mining Applications, p.341.

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