Name
Abstract | ||
|---|---|---|
The prevention of subscriber churn through customer retention is a core issue of Customer Relationship Management (CRM). By minimizing customer churn a company maximizes its profit. This paper proposes a hybridized architecture to deal with customer retention problems. It does so not only through predicting churn probability but also by proposing retention policies. The architecture works in two modes: learning and usage. In the learning mode, the churn model learner seeks potential associations from the subscriber database. This historical information is used to form a churn model. This mode also calls for a policy model constructor to use the attributes identified in the churn model to divide all 'churners' into distinct groups. The policy model constructor is also responsible for developing a policy model for each churner group. In the usage mode, a churn predictor uses the churn model to predict the churn probability of a given subscriber. When the churn model finds that the subscriber has a high churn probability the policy model is used to suggest specific retention policies. This study's experiments show that the churn model has an evaluation accuracy of approximately eighty-five percent. This suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups. Furthermore, this study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with 'churners'. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.knosys.2006.10.003 | Knowl.-Based Syst. |
Keywords | Field | DocType |
policy model construction,hybrid data mining model,subscriber churn,policy model constructor,churn model learner,policy model,effective retention policy model,high churn probability,churn probability,churn model,classification,customer retention,clustering,self-organizing map,churn predictor,customer relationship management,profitability,self organizing map,data mining | Customer relationship management,Customer retention,Data mining,Architecture,Computer science,Self-organizing map,Hybrid data,Artificial intelligence,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 8 | Knowledge-Based Systems |
Citations | PageRank | References |
36 | 1.20 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bong-Horng Chu | 1 | 41 | 2.81 |
| Ming-Shian Tsai | 2 | 54 | 1.77 |
| Cheng-Seen Ho | 3 | 125 | 15.78 |