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Customer lifetime value (LTV) prediction is essential for mobile gamepublishers trying to optimize the advertising investment for each useracquisition based on the estimated worth. In mobile games, deployingmicrotransactions is a simple yet effective monetization strategy, whichattracts a tiny group of game whales who splurge on in-game purchases. Thepresence of such game whales may impede the practicality of existing LTVprediction models, since game whales' purchase behaviours always exhibit varieddistribution from general users. Consequently, identifying game whales can openup new opportunities to improve the accuracy of LTV prediction models. However,little attention has been paid to applying game whale detection in LTVprediction, and existing works are mainly specialized for the long-term LTVprediction with the assumption that the high-quality user features areavailable, which is not applicable in the UA stage. In this paper, we proposeExpLTV, a novel multi-task framework to perform LTV prediction and game whaledetection in a unified way. In ExpLTV, we first innovatively design a deepneural network-based game whale detector that can not only infer the intrinsicorder in accordance with monetary value, but also precisely identify highspenders (i.e., game whales) and low spenders. Then, by treating the game whaledetector as a gating network to decide the different mixture patterns of LTVexperts assembling, we can thoroughly leverage the shared information andscenario-specific information (i.e., game whales modelling and low spendersmodelling). Finally, instead of separately designing a purchase rate estimatorfor two tasks, we design a shared estimator that can preserve the inner taskrelationships. The superiority of ExpLTV is further validated via extensiveexperiments on three industrial datasets.

Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection