DocumentCode :
1165602
Title :
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
Author :
Jiang, Tianyi ; Tuzhilin, Alexander
Author_Institution :
Dept. of Inf., Oper., & Manage. Sci., New York Univ.
Volume :
18
Issue :
10
fYear :
2006
Firstpage :
1297
Lastpage :
1311
Abstract :
There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a study and compared the predictive performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings, such as multiple segmentation levels, multiple real-world marketing data sets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our experiments show that both 1-to-1 and segmentation approaches significantly outperform aggregate modeling. Reaffirming anecdotal evidence of the benefits of 1-to-1 marketing, our experiments show that the 1-to-1 approach also dominates the segmentation approach for the frequently transacting customers. However, our experiments also show that segmentation models taken at the best granularity levels dominate 1-to-1 models when modeling customers with little transactional data using effective clustering methods. In addition, the peak performance of segmentation models are reached at the finest granularity levels, skewed towards the 1-to-1 case. This finding adds support for the microsegmentation approach and suggests that 1-to-1 marketing may not always be the best solution
Keywords :
customer services; marketing data processing; pattern clustering; clustering method; customer behavior; marketing computing; microsegmentation approach; Aggregates; Clustering methods; Context modeling; Data mining; Demography; Information management; Predictive models; 1-to-1 marketing; Personalization; clustering; microsegmentation.; segmentation;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2006.164
Filename :
1683767
Link To Document :
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