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Clustering mixed-type player behavior data for churn prediction in mobile games

Author

Listed:
  • Ana Perišić

    (University of Split
    Polytechnic of Šibenik)

  • Marko Pahor

    (University of Ljubljana)

Abstract

Marketers have long since understood the importance of customer segmentation and customer churn prediction modelling. However, linking these processes remains a challenge. Customer segmentation is often performed by applying a clustering algorithm on customer behavioral data, which is another challenging task since datasets on customer behavior typically comprise mixed-data types. This research focuses on clustering player behavior data for churn prediction modelling in the mobile games market and constructing a dissimilarity measure capable of simultaneously handling categorical and quantitative data. The problem of finding an appropriate dissimilarity measure for mixed-type data with unbalanced categorical features and highly skewed numerical features is handled by establishing a hybrid dissimilarity measure constructed as a normalized linear combination of distances. Distances are calculated conditional on feature type following the principles of Gower’s coefficient calculation where for numerical features, distances are calculated by applying a modified winsorized Huber loss, while for categorical features, we incorporate a distance measure based on variable entropy. In conjunction with the PAM clustering algorithm, the established dissimilarity measure is applied on real-world datasets and the performance is compared to several state-of-the-art clustering algorithms. Secondly, this research investigates the potential of customer segmentation as an integral part of churn prediction modelling in online games which is operationalized by applying the proposed clustering method on a real dataset comprising mixed-type data originating from a casual mobile game. The benefits of customer segmentation are supported by the data since churn prediction models exhibit higher performance when the clustering is performed prior to churn classification.

Suggested Citation

  • Ana Perišić & Marko Pahor, 2023. "Clustering mixed-type player behavior data for churn prediction in mobile games," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 165-190, March.
  • Handle: RePEc:spr:cejnor:v:31:y:2023:i:1:d:10.1007_s10100-022-00802-8
    DOI: 10.1007/s10100-022-00802-8
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    References listed on IDEAS

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    2. Zdeněk Šulc & Hana Řezanková, 2019. "Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 58-72, April.
    3. Zhang, Peng & Wang, Xiaogang & Song, Peter X.K., 2006. "Clustering Categorical Data Based on Distance Vectors," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 355-367, March.
    4. Seungwook Kim & Daeyoung Choi & Eunjung Lee & Wonjong Rhee, 2017. "Churn prediction of mobile and online casual games using play log data," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-19, July.
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    Cited by:

    1. Duygu Üçüncü & Süreyya Akyüz & Erdal Gül, 2024. "A novel auto-pruned ensemble clustering via SOCP," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(3), pages 819-841, September.

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