Customer Segmentation and Future Purchase Prediction using RFM measures

Title

Customer Segmentation and Future Purchase Prediction using RFM measures

Creator

Patra A.; Khan R.; Vijayalakshmi S.

Description

Winning in the E-Commerce business race at a competitive age like this requires proper usage of Customer data. Using that database and grouping it in similar segments in terms of spending expenditure, observation time, sex, and location so that every customer falls in a segment of characteristics. This mechanism is called Customer Segmentation. In the modern era of highly compatible technological advancements, Machine Learning Algorithms are being vastly used to bring solutions to these difficult yet essential services. In the field of research methods like simple clustering based on purchase behaviour, buyer targeting or automated customer promotion mechanism by dividing into two major categories, have been worked on. However, ensemble algorithms have come handy where different clustering algorithms are combined to deliver best segmentation. Lately combination techniques like clustering and classification mechanism have also delivered good results where, not only segmentation is done but also classification of existing and new customers are possible into the clusters. Depending on that an effective customer relationship management can really benefit the company to a huge extent. Unlike other studies where clustering was performed directly on RFM table, a different approach was taken in this study where, one dimensional clustering was done individually on Recency, Frequency, Monetary columns, then an overall score was calculated and customers were classified into three segments. However, for a new customer depending on his purchase behaviour he/she also can be classified into any of the categories. 2022 IEEE.

Source

Proceedings - 2022 4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022, pp. 753-759.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Classification; Clustering; Customer Segmentation; E-Commerce; K-means; Machine Learning; RFM

Coverage

Patra A., Christ University, Department of Data Science, Lavasa, Pune, India; Khan R., Christ University, Department of Data Science, Lavasa, Pune, India; Vijayalakshmi S., Christ University, Department of Data Science, Lavasa, Pune, India

Rights

Restricted Access

Relation

ISBN: 978-166547436-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAC3N56670.2022.10073993

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