Online Auction Customer Segmentation Using a Neural Network Model

Chu-Chai Henry Chan

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Customer churn and segmentation Data Mining and Machine Learning Applications Auction Theory and Applications

Abstract

Abstract: 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 prob-lem, this research proposes a neural network model called SOM to segment online auction cus-tomers 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’sbehave rationally and patiently.

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References

1999Electronic Commerce: A Managerial Perspective 2001Knowledge management and data mining for marketing 2007PENNIES FROM EBAY: THE DETERMINANTS OF PRICE IN ONLINE AUCTIONS*2001Reputation and e-commerce: eBay auctions and the asymmetrical impact of positive and negative ratings 1996A comparison of artificial neural network and time series models for forecasting commodity prices 2004An integrated data mining and behavioral scoring model for analyzing bank customers 2000Pennies from eBay: The Determinants of Price in Online Auctions 2000Option price forecasting using neural networks

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