International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Chu-Chai Henry Chan*

E-Business Research Lab Department of Industrial Engineering and Management, Chaoyang University of Technology, Wufong, Taiwan, R.O.C.


Download Citation: |
Download PDF


Online auction is a huge growing business. Most of online buyers face a big problem of predicting the seller’s behavior to submit a reasonable price for wining a bid. Unfortunately, auction web sites e.g. eBay provide only a user ID or nickname to identify a consumer. This situation built up a wall between users for truly knowing each other. To overcome such a problem, this research proposes a neural network model called SOM to segment online auction customers into homogenous groups. Based on the segmented groups, the behavior of online bidders can be divided into three types: patient deals, impulsive deals and analytic deals. To demonstrate the feasibility of the proposed methodology, 1470 records retrieved from Taiwan eBay are used to conduct an empirical study. In conclusion, the percentages of each customer type are 39.3% (impulsive deals), 27.8% (analytic deals) and 32.2% (patient deals). The analyzed result shows that more than sixty percent of bidder’s behave rationally and patiently.

Keywords: customer segmentation; neural network; online auction.

Share this article with your colleagues



  1. [1] Bryan, D., Reiley, L. D., Prasad, N., and Reeves. 2000. Pennies from eBay: the Determinants of Price in Online Auction. Working Paper, Vanderbilt University: 1-24.

  2. [2] Chan, Chu-Chai Henry. 2005. Developing a Data mining-Based Spider Program for Price Prediction of Electronic Auction. Working Paper, Chaoyang University of Technology.

  3. [3] Deitel, H.M., P.J. Deitel, and K. Steinbuhler. 2001. “E-business and e-commerce for managers”. Prentice Hall Inc., New Jersey.

  4. [4] Hsieh, Nan-Chen. 2004. An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27: 623–633.

  5. [5] Jang, Jyh-Shing, Chuen-Tsai Sun, and Eiji Mizutani. 1997. “Neuro-Fuzzy and soft computing”. Prentice Hall, New York.

  6. [6] Jonker Jedid-Jan, Nanda Piersma, and Dirk Van den Poel. 2004. Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Systems with Applications, 27: 159-168.

  7. [7] Kohzadi, Nowrouz, Milton S. Boyd, Badman Kermanshahi, and Iebling Kaastra. 1996. A Comparison of artificial neural network and time series models for forecasting commodities prices. Neurocomputing, 10: 169-181.

  8. [8] Shaw, Michael J., Chandrasekar Subramaniam, Gek Woo Tan, and Michael E. Welge. 2001. Knowledge management and data mining for marketing. Decision Support Systems, 31, 127–137.

  9. [9] Sherstyuk, Katerina. 2002. Collusion in private value ascending price auctions. Journal of Economic Behavior and Organization, 48: 177–195.

  10. [10] Standifird, Stephen S. 2001. Reputation and e-commerce: eBay auctions and the asymmetrical impact of positive and negative ratings. Journal of Management, 27: 279–295.

  11. [11] Turban, E. 1997. Auctions and Bidding on the Internet: An Assessment. Electronic Markets, 7, 4: 7-11.

  12. [12] Turban, E, Jae Lee, David King, and H. Michael Chung. 2000. “Electronic Commerce A Managerial Perspective”. Prentice Hall International Inc., New Jersey.

  13. [13] Oh, Wonseok. 2002. C2C Versus B2C: A comparison of the Winner’s Curse in two types of electronic auctions. International Journal of Electronic Commerce, 6, 4: 115-138.

  14. [14] Rayals, Lynette. 2002. Are your customer worth more than money ? Journal of Retailing and Consumer Service, 9: 241-251.

  15. [15] Yao, Jingtao, Yili Li, and Chew Lim Tan, 2000. Option price forecasting using neural networks. The International Journal of Management Science, 28: 455-466.

  16. [16] Yeh, I-Cheng. 1993. “The applications and practices of neural networks”. Scholars Book, Taiwan, R.O.C.

  17. [17] Yuan, Soe-Tsyr and Wei-Lun Chang. 2001. Mixed-initiative synthesized learning approach for web-based CRM. Expert Systems with Applications, 20: 187-200.


Accepted: 2005-07-28
Available Online: 2005-10-03

Cite this article:

Henry Chan, C.-C., 2005. Online auction customer segmentation using a neural network model. International Journal of Applied Science and Engineering, 3, 101–109.