I am trying to model an individuals' purchasing behavior using different data sources (ex: Zalando, Otto, etc.,). When I combine data sources, I see that the data across these channels is very different.
For example, 5% heavily shop using a particular channel, but do not utilize other channels. When I try to model anything from this information, it performs poorly because overall, its a sparse dataset, but a % of each column should be a very good predictor of a small subset of population.
My question is: How do I combine/normalize such a dataset wherein the data is super sparse?