Robust CLV Measurement in Non-contractual Settings:A Study of CLV Measurement Combining Probability Models and Machine Learning Algorithms

Abstract

Abstract:

Customer lifetime value (CLV) is a key concept in customerrelationship management. However, measuring CLV, especially CLV in non-contractual settings is one of the most challenging research topics. In this paper, we explore two probability models (Pareto/NBD and BG/NBD) and two machine learning models (GAM and SVM) in CLV modeling. Through two empirical studies, we compare the characteristics and prediction performance of the four mentioned approaches. The study shows that the prediction performance of classical probability models is very stable, which means that probability models are able to capture the regular patterns of consumers' daily consumption. While GAM has a good performance in capturing extreme data, making that an advantage to reflect irregular consumptions such as stock up resulted from store promotion, online promotion or holidays and festivals. Since each approach has its own strong points, we believe that an integrated CLV measurement combining probability models and machine learning algorithms is an ideal solution to ensure the robustness of CLV measurement.

Key words: customer lifetime value (CLV), Pareto/NBD, BG/NBD, generalized additive model (GAM), support vector machine (SVM)

Cheng Dong, Sun Yinglu, Xue Wei. Robust CLV Measurement in Non-contractual Settings:A Study of CLV Measurement Combining Probability Models and Machine Learning Algorithms[J]. , 2019, 31(4): 83-98.

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