There are many ways to do it.
The simple way would be the following:
You need to identify a cut-off point for which, when surpassed at least $\alpha$% of people
never come back to play. Never can be defined as a) a big time-frame e.g:3 years or b) Uninstall of the game. You fix the $\alpha$ (for this example I'll go for $\alpha=95$) and you then try to find the minimum cut-off point. For instance, For all cases of gamers in the dataset that have surpassed the cutoff point of 6-months without playing, 90% never come back. Thus the 6 month is too low. You continue until you identify a cut-off point e.g: 7 months for which $\alpha$% so 95% of gamers who have surpassed it never came back and that would be the simplified answer. You can basically define you own parameters and do an ROC analysis or something similar to identify the optimal cut-off point.
The complicated way would be to do it a bit more "personalised". In that case you would need to cluster people based on their "natural return rates" (every time they come back to play is a time difference). There are many ways to cluster them but the idea here is the you create groups of people that have "as similar return rates as possible". Afterwards you identify cut-off points for each group based on the analysis above.
These are just some examples but it's a really open question in general.