DocumentCode :
2193880
Title :
Contextual Segmentation: Using Context to Improve Behavior Predictive Models in E-commerce
Author :
Faraone, Maria Francesca ; Gorgoglione, Michele ; Palmisano, Cosimo
Author_Institution :
Dept. of Bus. Eng., Politec. di Bari, Bari, Italy
fYear :
2010
fDate :
13-13 Dec. 2010
Firstpage :
1053
Lastpage :
1060
Abstract :
In e-commerce, where the search costs are low and the competition is just a mouse click away, it is crucial to accurately predict customer purchasing behavior in order to offer more targeted and personalized products and services. Recent research has demonstrated that including the context in which a transaction occurs in customer behavior models improves their predictive performance, especially when studying individual customer behavior. However, several practical and managerial issues can arise, thus driving companies to focus on segments rather than on individuals. The main contribution of this work lies in presenting a conceptual framework to incorporating context when building predictive models of market segments, and in comparing different approaches, across a wide range of experimental conditions. Our experiments show that the most accurate approach is not the most efficient from a managerial perspective. Our findings provide insights of how companies can exploit context at best to support marketing decision-making.
Keywords :
consumer behaviour; customer services; decision making; electronic commerce; purchasing; transaction processing; contextual segmentation; customer purchasing behavior prediction model; customer service; e-commerce; managerial issue; market segment; marketing decision making; personalized product; transaction; contextual segmentation; customer behavior; e-commerce;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining Workshops (ICDMW), 2010 IEEE International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9244-2
Electronic_ISBN :
978-0-7695-4257-7
Type :
conf
DOI :
10.1109/ICDMW.2010.101
Filename :
5693411
Link To Document :
بازگشت