Should the observation window include the same dates for all the users? I'm building a churn model that is trained on  users' historic data(observation window).
For this example, let's say that I want to train the model on the last week of use of each user.
Is it fine that the weeks of each window will be different according to the last week of use for each user? For example the next figure:

Are there are any advantages if the observation window starts and ends on the same dates for all the users?

Lets take the example of customers buying groceries.
Using different dates for each user based on the user's individual history may help in case the general trend of usage follows an expected pattern, i.e. - low frequency of purchase in the past indicates low frequency of purchase in future.
For example, if we know that all users with a similar purchase frequency indicate their tendency to churn.
On the other hand, when you take an observation window that is the same for every user, there are certain benefits:

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*The purchase trends for a population in a given location may provide a very good indicator for individuals buying behavior. For instance, people may stock up groceries before bad weather events - and everybody can be expected to behave similarly irrespective of their usual frequency of purchase.

*Following a marketing campaign, certain products may see higher purchase than others and thus customers of different purchase frequency may purchase them since they were influenced by recent advertising.

*This approach also lets you test the model's performance on new unseen data thus mimicking how a model is likely to be deployed in the real world.

For the end consumer of the model (e.g. grocery chain), a churn model that can pick up trends for customers for a given period is more relevant and useful as compared to a model that separately focuses on customers at different points of time ignoring the impact of how a population in a specific area or a population segment behaves as a group.
