Internet Recommendation System-Take Amazon.com as an Example
Date Issued
2008
Date
2008
Author(s)
Chen, Wan-Ling
Abstract
It’s getting obvious that customer relationship management could be viewed as a lethal weapon. To give the customers exactly what they want in affordable price can easily enhance the customer satisfaction. And this study is based on database marketing techniques, which is the key point of CRM, to analyze each customer’s purchasing behavior. By examining the transaction records, we’ll predict each customer’s next purchasing behavior, and apply the concept of customer recommendation systems to customize their recommended products. We believe that with one strong and precise recommendation system, we could encourage cross-buying, develop customer loyalty, and finally improve the customer retention, which would lead to great profits.n this research, we hold two purposes, one is to find out the online books buying preference, the other is to compare the different kinds of recommendation systems. We separate all customers into two groups by their repetitive purchasing in turn representing“Old Customers” and “New Customers”. Later we try three types of statistical models, “the Common Average Method”, “the Aggregate Logit Recommendation System”, and the “Hierarchical Bayesian Logit Recommendation System”, and see which one of them can perform the best in the accumulated hit ratio for predicting customers purchasing possibilities. he study result shows several buying habits on different cluster of customers, for example, people with only high school educational backgrounds prefer buying “Business and Finance” to “Lifestyle” types of books, they would like to buy books that are thicker, and so on. In addition to the purchasing behavior, we also found that Hierarchical Bayesian Logit Recommendation System do the best prediction, just like the Hierarchical Bayes theory proposed, no matter for old customers or new customers. It’s is quite evident that more and more customers now they share heterogeneity needs,. In that, to best serve all individual’s need, the company better keep on customizing the recommendation system.
Subjects
Cusomer Relationship Management
Database Marketing
Internet Recommendation systems
Hierarchical Bayes Model
Amazon.com
Type
thesis
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