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An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce

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  • Xiaoyi Deng

    (College of Business Administration, Huaqiao University, Xiamen, China)

Abstract

Customer segmentation can enable company administrators to establish good customer relations and refine their marketing strategies to match customer expectations. To achieve optimal segmentation, a hybrid Artificial Bee Colony algorithm (ABC) is proposed to classify customers in mobile e-commerce environment, which is named KP-ABC. KP-ABC is based on three famous algorithms: the K-means, Particle Swarm Optimization (PSO), and ABC. The author first applied five clustering algorithms to a mobile customer segmentation problem using data collected from a well established chain restaurant which has operations throughout Japan. The results from the clustering were compared to the existing company customer segmentation data for verifications. Based on the initial analysis, special characteristics from those three algorithms were extracted and modified in our KP-ABC method which performed extremely well with mobile e-commerce applications. The result shows that KP-ABC is at least 2% higher than that of other three algorithms.

Suggested Citation

  • Xiaoyi Deng, 2013. "An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce," Journal of Electronic Commerce in Organizations (JECO), IGI Global, vol. 11(2), pages 53-63, April.
    • Handle: RePEc:igg:jeco00:v:11:y:2013:i:2:p:53-63
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    Cited by:

    1. Masoud Rabbani & Mahdi Mokhtarzadeh & Neda Manavizadeh & Azadeh Farsi, 2021. "Solving a bi-objective mixed-model assembly-line sequencing using metaheuristic algorithms considering ergonomic factors, customer behavior, and periodic maintenance," OPSEARCH, Springer;Operational Research Society of India, vol. 58(3), pages 513-539, September.

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