Purchase Prediction in Free Online Games via Survival Analysis

Yang, Wanshan; Huang, Ting; Zeng, Junlin; Tang, Yan; Chen, Lijun; Mishra, Shivakant; Liu, Youjian

doi:10.1109/bigdata47090.2019.9006031

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Yang et al [35] focused on predicting the purchase probability of paying players and map the most important features related to the player's playing and purchase behavior. Moreover, two different models were used to predict the survival probability.…”

Section: Company Motivation and Revenue Maximizationmentioning

confidence: 99%

See 1 more Smart Citation

Towards Suitable Free-to-Play Games for Children

Melzer

Roarsen

Hagen

et al. 2021

Entertainment Computing – ICEC 2021

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Section: Company Motivation and Revenue Maximizationmentioning

confidence: 99%

“…As a result, data analysis and the use of metrics have become essential factors. Four primary studies tried to find factors that influence the player's purchase behavior [35,34,33,36], which indicates a focus on revenue maximization. Further, the study by Hamari et al [1] revealed six dimensions for players' purchase motivations.…”

Section: Company Strategiesmentioning

confidence: 99%

Towards Suitable Free-to-Play Games for Children

Melzer

Roarsen

Hagen

et al. 2021

Entertainment Computing – ICEC 2021

View full text Add to dashboard Cite

perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games

Zhao¹,

Wu²,

Tao³

et al. 2023

ACM Trans. Inf. Syst.

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Online games make up the largest segment of the booming global game market, in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. Therefore, Customer Lifetime Value (CLTV) is particularly vital for game companies to improve marketing decisions and increase game revenues. Nowadays, as virtual game worlds are becoming increasingly innovative, complex, and diverse, the CLTV of massive players is highly personalized. That is, different players may have very different patterns of CLTV, especially on churn and payment. However, current solutions are inadequate in terms of personalization and thus limit predictive performance. First, most methods just attempt to address either task of CLTV, i.e., churn or payment, and only consider the personalization from one of them. Second, the correlation between churn and payment has not received enough attention and its personalization has not been fully explored yet. Last but not least, most solutions around this line are conducted based on historical data where the evaluation is not convincing enough without real-world tests. To tackle these problems, we propose a general system to predict personalized customer lifetime value in online games, named perCLTV. To be specific, we revisit the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion. On this basis, we develop a generalized framework to model CLTV across games in distinct genres by heterogeneous player behavior data, including individual behavior sequential data and social behavior graph data. Comprehensive experiments on three real-world datasets validate the effectiveness and rationality of perCLTV, which significantly outperforms other baseline methods. Our work has been implemented and deployed in many online games released from NetEase Games. Online A/B testing in production shows that perCLTV achieves a prominent improvement in two precision marketing applications of popup recommendation and churn intervention.

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Purchase Prediction in Free Online Games via Survival Analysis

Cited by 2 publications

References 18 publications

Towards Suitable Free-to-Play Games for Children

Towards Suitable Free-to-Play Games for Children

perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games

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