I have conversion rate data from two largely similar marketing campaigns: source & target.
The source (1m events, 3% conversion) and target (20k events, 1% conversion) data come from the same source, and share the same ~10 numerical & categorical features (e.g. day of week, local time of day, device type, device OS, etc) & over 90% similar values by feature.
To account for the different conversion rates, one feature is the n-sample trailing conversion rate.
I have tried different mixes of source & target data, but none can predict test target events.
Given events are recorded by the same measurement techniques, and there are 20k target events, I'm hesitant to conclude the target data is too noisy.
What techniques can I try?