How to normalize data from multiple sources? 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?
Make sure that when you merge your datasets, the column information is as generic as possible, which means: When you merge A with B, make sure you name "Purchases at (any of the) online channel" not "Purchases at A's online channel" and "Purchases at B's online channel.

Merging datasets from completely different datasources is very difficult because they were not thought with the same basic ideas.