A Two Stage Classification Model for Call Center Purchase Prediction
In call center  product recommendation field, call center as an organization between users and telecom operator, doesn’t have permission to access users specific information and the detailed products information. Accordingly, rule-based selection method is common used to predict user purchase behavior by the call center. Unfortunately, rule-based approach not only ignores the user’s previous behavior information entirely, and it is difficult to make use of the existing interaction records between users and products. Consequently, it will not get desired results if we just use the basic selection method to predict user purchase behavior directly, because the problem is that the features straightly extracted from the interaction data records are limited. In order to solve the problem above, this paper proposes a two-stage algorithm that based on K-Means Clustering Algorithm  and SVM [3, 4] Classification Algorithm. Firstly, we get the potential category information of products by K-Means Clustering Algorithm, then use SVM Classification Model to predict users purchasing behavior. This two-stage prediction model not only solves the feature shortage problem, but also gives full consideration to the potential features between users and product categories, which can help us to gain significant performance in call center product recommendation field.
Article MetricsAbstract view : 262 times
PDF - 290 times
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604