[zechs]

How correlated is your models output with a simple measure of the volume of interactions a user has with an app?

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?

[bmahmood]

Great question! It is true that generic engagement/activity metrics tend to be highly correlated to conversion, and the absence of any activity tends to be correlated to churn. We see those features show up often.

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.