Applied Big Data Analysis to Build Customer Product Recommendation Model

Rong-Ho. Lin, Wei-Wei Chuang, Chun-Ling Chuang and Wan-Sin Chang
Additional contact information
Rong-Ho. Lin: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106, Taiwan
Wei-Wei Chuang: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106, Taiwan
Chun-Ling Chuang: Department of Information Management, Kainan University, Taoyuan 338, Taiwan
Wan-Sin Chang: Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 106, Taiwan

Sustainability, 2021, vol. 13, issue 9, 1-45

Abstract: With the development of the Internet environment, the trend of the retail industry in the future. It cannot be separated from the community, data and experience. Consumers’ lifestyles and purchasing behaviors are constantly changing and retailers must adopt policies to understand consumers. This research analyzes supermarkets most commonly touched by consumers in daily life. In order to find hidden information behind customer transaction data, it helps supermarkets to learn about the habits of customers to help them Formulate marketing strategies and improve the profitability of supermarkets and maintain long-term relationships with customers. Thus, the RFM model is used to convert customer transaction data into R, F, and M values and then clustering using the Ward’s method to combine with K-means, fuzzy C-means, and self-organizing maps. Using discriminant analysis find out the grouping method with the highest accuracy rate to calculate the customer lifetime value score. In terms of product recommendation, customers can be recommended to buy products in the top five categories or to use rules found in association rule to make recommendations. In terms of customers, we maintain long-term relationships with customers by recommending other related products, products for bundling sale, giving gifts or discount coupons, and regularly organizing promotional activities.

Keywords: supermarket; RFM model; ward’s method; K-means; fuzzy C-means; self-organizing maps; discriminant analysis; customer lifetime value score; association rules (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/9/4985/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/9/4985/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:9:p:4985-:d:545963

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().