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I am working with a dataset with about 10,000 customers. About 3,000 engaged with dozens of marketing campaigns over the years.

I am trying to create a model to find which marketing campaign to use on a given customer. I was thinking of creating affinity scores based on conversions from these campaigns and do some sort of random forest/logistic regression. It gets tricky because only 3,000 have engaged and I am not sure what to recommend for the other 7,000. Any suggestions?

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Use an unsupervised method such as clustering to group users, then assign marketing campaigns that have been used by others within the same cluster.

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  • $\begingroup$ thanks, but let's say in cluster one, there are dozens of campaigns that were successful for those customers. which campaign do you then choose? $\endgroup$
    – Alex
    Commented Dec 2, 2022 at 22:45
  • $\begingroup$ The most common/successful ones $\endgroup$
    – Thomas Mikhail
    Commented Dec 3, 2022 at 23:32

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