Robert Magyar

Questions you need to ask yourself to find the most profitable players (for LAL on Android for example - changes coming to iOS):

1. Is this group great at buying IAPs, do they do it frequently?
2. Is this group heavily engaged, does their engagement grow over time?
3. Do this group of players watch ads frequently? Make sure you have a benchmark to compare these new LAL/audiences to.

Understanding the CPI to spend relationship is a key factor in understanding UA payback and how you can scale your campaign. Example chart here.

A benchmark comparing ROAS (e.g. D7/D28) for each week (X axis) with success thresholds allows you to evaluate your UA strategies. Example chart here.

Do not create too many groups/segments of players when looking at LTV. You need to make sure they are different so you can understand which group is better. It is not enough to segment based on how much they purchase however, you need to use other attributes too.

If your LTV curve looks like a step function with jumps, either your game is relying mainly on LiveOps offers (not ideal design) or the amount of payers/players is too low. Example chart here.

For special offers LTV prediction models start with a rule-based system, then a probabilistic system and finally machine learning system. With the rule-based system it is less risky and more transparent which also helps you identify the impact of the changes you make.

If your LTV curves are jumping around, one thing you might want to consider is testing your scale of spend against your ROAS target. Example: looking at D7 ROAS against daily spend to see if you have a knee bend. If you're unsure about spend you can "sit below the bend".

Chart your creatives on a X axis and check your D3/D7/D28 ROAS to quickly spot outlier creatives (both good and bad) so you can act on that (by reallocating spend for example). Example chart here.