AI Driven Customer Segmentation and Recommendation of Product for Super Mall

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Authors

  • Dipanwita Dey
     Department of Management Studies, Swami Vivekananda University Barrackpore, West Bengal 700121. ,IN
  • Kallal Banerjee
     Department of Management Studies, Swami Vivekananda University Barrackpore, West Bengal 700121. ,IN

DOI:

https://doi.org/10.18311/jmmf/2023/34166

Keywords:

Clustering, Segmentation, Retail Chain, Association Rules, RFM.

Abstract

The prime objective of the paper is to propose customer segmentation for the retail customer for a large retail chain super mall. Along with customer segmentation, product recommendation is integrated with the system. Segmentation is required to identify the customer with specific behaviour. The real-world data comes from the customer support department of a retail chain super mall. For customer segmentation, the k- means algorithm is applied by using RFM data and the Association Rule Mining algorithm is applied to create the mapping rules between customer segments and favourite products. The customers are segmented into “Traditional”, “High Spending”, “Occasional”, “Low Spending”, “Disloyal” and “Frequent Buyer”. Customer segmentation is necessary for efficient marketing strategy providing discounts, promotions, campaigns, etc. Moreover, the ARM algorithm is used to map the preferred combo for all customer segment. This combined approach is used to create an effective marketing strategy for retail chain management.

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Published

2023-07-04

How to Cite

Dey, D., & Banerjee, K. (2023). AI Driven Customer Segmentation and Recommendation of Product for Super Mall. Journal of Mines, Metals and Fuels, 71(5), 656–660. https://doi.org/10.18311/jmmf/2023/34166

Issue

Volume 71, Issue 5, May 2023

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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