Name
Abstract | ||
|---|---|---|
This paper presents investigating the customer characteristics of payment method change in the mail order industry. This time we are focusing on the transactional activity of bad debt customers. These kinds of investigations have not made intensively, such as the shipping address, the recipient name, and the payment method so far and the conventional method for predicting such knowledge depends on the employees’ working experiences. For these backgrounds, we observed the transaction data with the bad debt customer information gathered from a mail order company and characterized the customer with machine learning. From the results of the analysis, we are succeeded in characterizing the potential customers. Intensive research revealed that the characteristics of customers who make fraud transactions. This result will make use of the revenue expansion with the improvement of the bad debt collections in the target industry. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.procs.2014.08.254 | Procedia Computer Science |
Keywords | Field | DocType |
Mail Order,Customer Analysis,Risk Management,Machine Learning,Self-Organizing Maps (SOM),Service Science and Management Engineering | Revenue,Customer retention,Data mining,Bad debt,Computer science,Risk management,Customer advocacy,Payment,Transaction data,Transactional leadership | Conference |
Volume | ISSN | Citations |
35 | 1877-0509 | 1 |
PageRank | References | Authors |
0.41 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masakazu Takahashi | 1 | 4 | 7.09 |
| Hiroki Azuma | 2 | 1 | 1.09 |
| Kazuhiko Tsuda | 3 | 108 | 47.18 |