Customer lifetime value prediction for gaming industry: fuzzy clustering based approach

Article type: Research Article

Authors: Tekin, Ahmet Tezcan^{; *} | Kaya, Tolga | Cebi, Ferhan

Affiliations: Istanbul Technical University Management Engineering Department, Besiktas, Istanbul, Turkey

Correspondence: [*] Corresponding author. Ahmet Tezcan Tekin. E-mail: [email protected].

Abstract: The use of fuzzy logic in machine learning is becoming widespread. In machine learning problems, the data, which have different characteristics, are trained and predicted together. Training the model consisting of data with different characteristics can increase the rate of error in prediction. In this study, we suggest a new approach to assembling prediction with fuzzy clustering. Our approach aims to cluster the data according to their fuzzy membership value and model it with similar characteristics. This approach allows for efficient clustering of objects with more than one cluster characteristic. On the other hand, our approach will enable us to combine boosting type ensemble algorithms, which are various forms of assemblies that are widely used in machine learning due to their excellent success in the literature. We used a mobile game’s customers’ marketing and gameplay data for predicting their customer lifetime value for testing our approach. Customer lifetime value prediction for users is crucial for determining the marketing cost cap for companies. The findings reveal that using a fuzzy method to ensemble the algorithms outperforms implementing the algorithms individually.

Keywords: CLTV prediction, fuzzy model selection, fuzzy regression, ensemble learning, the gaming industry

DOI: 10.3233/JIFS-219177

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 1, pp. 87-96, 2022