Name
Abstract | ||
|---|---|---|
In this paper we propose a methodology for extracting complex sales expert rules by analyzing the data from the past lost/won deals stored in Customer Relationship Management Systems. We first used Multi-Adaptive Regression Splines model to identify the features importance, then we created a classification tree of lost/won sales using Random Forest and lastly we used the tree for extraction of the expert rules that gives insights into the rules of successful complex sales. The proposed methodology was successfully validated using complex sales data from a CRM application and the results are presented and discussed in this paper. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/INISTA.2017.8001179 | 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) |
Keywords | Field | DocType |
Customer Relationship Management,Classification,Multi-Adaptive Regression Splines,Expert Systems,Random Forests,Decision Trees,Java Expert System Shell | Data science,Customer relationship management,Customer intelligence,Data modeling,Knowledge management,Feature extraction,Business | Conference |
ISBN | Citations | PageRank |
978-1-5090-5796-2 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doru Rotovei | 1 | 0 | 1.69 |
| Viorel Negru | 2 | 311 | 47.71 |