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

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

If you think you can help in any of these areas or in many areas we haven't thought of yet, then please take a look at our Contributors' guidelines.

If you want to report a problem or suggest an enhancement we'd love for you to open an issue at this github repository because then we can get right on it. But you can also contact Yuki(Yuxin) by email [email protected].