Name
Abstract | ||
|---|---|---|
Customer segmentation is a key element for target marketing or market segmentation. Although there are quite a lot of ways available for segmentation today, most of them emphasize numeric calculation instead of commercial goals. In this study, we propose an improved segmentation method called transaction pattern based customer segmentation with neural network (TPCSNN) based on customer's historical transaction patterns. First of all, it filters transaction data from database for records with typical patterns. Next, it reduces inter-group correlation coefficient and increases inner cluster density to achieve customer segmentation by iterative calculation. Then, it utilizes neural network to dig patterns of consumptive behaviors. The results can be used to segment new customers. By this way, customer segmentation can be implemented in very short time and costs little. Furthermore, the results of segmentation are also analyzed and explained in this study. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.eswa.2008.07.073 | Expert Syst. Appl. |
Keywords | Field | DocType |
customer segmentation,association rule mining,neural network,historical transaction pattern,transaction pattern analysis system,market segmentation,transaction data,numeric calculation,improved segmentation method,segment new customer,transaction pattern,clustering,iterative calculation,pattern analysis | Data mining,Market segmentation,Network segmentation,Computer science,Segmentation,Segmentation-based object categorization,Association rule learning,Artificial intelligence,Artificial neural network,Database transaction,Transaction data,Machine learning | Journal |
Volume | Issue | ISSN |
36 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.43 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzu-Chuen Lu | 1 | 374 | 33.17 |
| Kun-Yi Wu | 2 | 11 | 2.33 |