Main question...is this a domain where ML really adds a lot of value beyond the basic concept of High Interaction (lots of time spent and clicks/pages visited) -> High conversion Probability?
But the propensity models built in ClearBrain tend to be more specific. The target variable can be defined as any client or server-side event you've tracked in Segment, or any trait/attribute of your user. As such, common use cases tend to be around predicting conversion events to discrete stages of a user journey - separate models for whom will move from plan type A --> B --> C, etc. So even if a generic engagement metric shows up as highly correlated for these discrete stages, the benchmark in engagement would be different and hence still intuitively helpful to diffrentiate groups of users.