Data driven approach to define a churn user
Another approach would be to model "churn" (aka "diminished use of the service, including non-use") as a process and not an event.  Years ago in retention marketing this was called a "defection funnel", to mirror the "sales funnel" on the customer acquisition side (suspect -> prospect -> trial customer -> repeat customer -> loyal customer).  So a defection funnel might look like this:


  loyal/frequent customer -> disaffected customer -> infrequent customer -> non-customer


It is proper to label this a "funnel" because a subset of each stage move on to the next stage.  Not all "disaffected customers" become "infrequent customers", and so on.

There are two keys to defining a defection funnel.  First, define the behavioral characteristics for the funnel as a whole, and for each stage (category) within the funnel.  In your case, it might be a variation of the familiar RFM ("recency" "frequency" and "monetary") score.  Second, you need to identify other behavioral characteristics or patterns that show a propensity to move from one stage to another.  These might include: complaints, being victim of trolling, experiencing service interruptions, experiencing betting losses, how long they have been using the service, or maybe it is just demographics.

With these definitions in place, you are in a position to build a predictive model and also track over time the population of customers in each category.



By the way, this approach is not dependent on dividing the funnel into "stages", nor does it matter fundamentally how many stages you have.  However, defining discrete stages has many practical benefits, including communicating your model and results to other people who are not quantitative or statistically minded.