How much can I spend in acquiring a new player? What is the potential value of this user versus another one? When will my players churn and what can I do to prevent it? And of course, building models that help to predict the lifetime value (LTV) of your game players. We are here to take advantage of the benefits data can bring. Here, capability to detect and predict the behaviour of players as early as possible is the most important factor for promising mobile game.
LTV prediction and its period should based on the your game type. Here we applied 14-day LTV prediction for Hyper Casual Game while we apply longer time period for Casual Game such as 30-days, 60-days etc. So firstly, AppNava detect “Valuable” users to predict LTV. Next step is predicting number of purchases. Last step is predicting the amount of purchase. All these three steps take you to successful LTV prediction.
Let’s see the result of the ongoing partnership with Idle Tycoon Game. The LTV of "unvalued players" increased by 200% by accurately identifying their Life Time Value. There is no change in Day-7 and Day -10 retention rates (even slightly higher like 0.80% points less than 1%) of "unvalued players". But Day-14 retention rate of "unvalued players" drop from 9.5% to 8.16%.
