Using real online retail data from a store, customer value was predicted and segmented using two probability models: BG/NBD and Gamma-Gamma. An example was used to illustrate the implementation process of the customer lifetime value analysis model and segmentation model.
As the basic theory of customer relationship management continues to improve, customer lifetime value, as an important part of this theory, has gradually been valued by enterprises. However, the measurement of value under non-contractual relationships has always been a difficult problem in research. This article mainly uses real-store online retail data, and uses two probability models, BG/NBD and Gamma-Gamma, to predict customer value and segment the customer groups. Through examples, the article elaborates on the implementation process of customer lifetime value analysis model and segmentation model, verifies the effectiveness of the model fitting, and proposes personalized customer retention and marketing strategies for different types of customers.
data source: https://archive.ics.uci.edu/ml/datasets/Online+Retail+II
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