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I need to cluster new customer according to their future potential, but I have only information about their first transaction.

I have access to all transactions for the other customers.

So I can do a non-supervised learning on the customer who subscribe since at least 12 months in order to group them according to their potential. Then do a supervised learning to recover these groups, but only with variables from their first purchase.

But this kind of method can lead to a conceptual drift because a lot of new customers appear every day in the databases

Do you have any other idea?

Thanks,

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  • $\begingroup$ I don't think this question should be closed as primarily opinion based as it seems to be asking for strategies to attack a problem rather than weigh up whether an approach is "good" or "bad". Identifying which new customers are of the highest priority is a common business challenge (in my own work I use an informal scoring system, for instance), and it would surprise me if there weren't a few fairly standard techniques to automate the identification process in larger firms. $\endgroup$ – Silverfish May 28 '15 at 23:40
  • $\begingroup$ It might be beneficial if you could give some examples of the data that is captured upon the first transaction. $\endgroup$ – Silverfish May 29 '15 at 20:34

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