Name
Abstract | ||
|---|---|---|
On the Web, where the search costs are low and the competition is just a mouse click away, it is crucial to segment the customers intelligently in order to offer more targeted and personalized products and services to them. Traditionally, customer segmentation is achieved using statistics-based methods that compute a set of statistics from the customer data and group customers into segments by applying distance-based clustering algorithms in the space of these statistics. In this paper, we present a direct grouping-based approach to computing customer segments that groups customers not based on computed statistics, but in terms of optimally combining transactional data of several customers to build a data mining model of customer behavior for each group. Then, building customer segments becomes a combinatorial optimization problem of finding the best partitioning of the customer base into disjoint groups. This paper shows that finding an optimal customer partition is NP-hard, proposes several suboptimal direct grouping segmentation methods, and empirically compares them among themselves, traditional statistics-based hierarchical and affinity propagation-based segmentation, and one-to-one methods across multiple experimental conditions. It is shown that the best direct grouping method significantly dominates the statistics-based and one-to-one approaches across most of the experimental conditions, while still being computationally tractable. It is also shown that the distribution of the sizes of customer segments generated by the best direct grouping method follows a power law distribution and that microsegmentation provides the best approach to personalization. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TKDE.2008.163 | IEEE Trans. Knowl. Data Eng. |
Keywords | Field | DocType |
groups customer,customer behavior,customer base,computing customer segment,optimal customer partition,customer segment,customer bases,suboptimal direct grouping segmentation,customer segmentation,group customer,optimal segmentation,improving personalization solutions,customer data,electronic commerce,classification,statistics,clustering algorithms,personalization,power generation,transactional data,cost function,statistical analysis,np hard problem,data mining,demography,transaction data,clustering,search cost,context modeling,association rule,power law distribution | Data mining,Market segmentation,Affinity propagation,Voice of the customer,Segmentation,Computer science,Artificial intelligence,Personalized marketing,Cluster analysis,Customer base,Machine learning,Personalization | Journal |
Volume | Issue | ISSN |
21 | 3 | 1041-4347 |
ISBN | Citations | PageRank |
0-7695-2701-9 | 13 | 0.78 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyi Jiang | 1 | 79 | 6.50 |
| Alexander Tuzhilin | 2 | 6901 | 489.00 |