Customer Lifetime Value Prediction in Mobile Gaming Industry: Fuzzy Logic Approach

Abstract

The Customer Lifetime Value (CLV) is an essential metric in customer relationship management (CRM), allowing companies to identify valuable customers and refine their advertising strategies. Traditional customer lifetime value prediction methods, including regression and machine learning techniques, frequently depend on accurate and predictable input data, making them less effective at capturing the inherent uncertainty and unpredictability in customer behavior. This research presents a fuzzy logic-based Customer Lifetime Value prediction model that integrates Recency, Frequency, and Monetary Value (RFM) as essential input factors. The proposed approach utilizes fuzzy membership functions and fuzzy inference systems (FIS), enabling consumers to possess partial membership in different CLV categories, hence offering a more adaptable and comprehensible framework for CLV calculation. A rule-based IF-THEN fuzzy system is established to categorize clients into various CLV segments, and defuzzification methods are employed to derive a precise CLV score. Experimental results indicate that the fuzzy logic model adeptly manages uncertainty and imprecision, outperforming traditional hard-segmentation methods by providing a continuous and adaptable strategy for CLV prediction. This research underscores the benefits of fuzzy logic in customer analytics, offering enterprises an easy and flexible instrument for customer segmentation, retention strategies, and revenue optimization.

Keywords

• Customer lifetime value (CLV)
• Fuzzy logic
• Fuzzy inference system
• Customer segmentation
• Recency-frequency-monetary (RFM) model

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