Hi @JasmineM4474,

In a way the choice between a "context key" and a predictor is somewhat arbitrary. In general, if you expect radically different behaviours, if there is enough volume to deal with "cold start" and the combinational explosion that results from it is manageable, choosing context keys is not a bad idea at all, as - if these conditions hold - will give you better predictive performance than pulling the (context) data in as predictors. Examples from other customers we've seen are for example anonymous vs. known customers, and we've also seen customers that consider a page almost like a channel, so they added an additional context key for page.

You listed all these arguments already. And by the way, instead of additional context keys, you could also configure different adaptive model (rules) for each page, but that is more maintenance and the only reason to do that would be if they had different configurations in terms of (hyper)parameters, or different sets of predictors (governance etc).

More context keys gives more context key combinations which gives you more model instances. That is a load on the system of course. Your Pega staff can help with sizing. More context keys also make the models more granular, so every model will receive fewer responses. If your response volume is high this is not a problem, but if it takes weeks for models to gather sufficient responses to become reliable, it probably is not a good idea to configure it like this.

As always, I would start simply by adding the page context as predictors. Then in a controlled experiment measure if models with customized context really work better - before bothering. Consider the data extract from the models ("Historical Data Capture") to pull the data and run an experiment off-line. 

-Otto