A Two Stage Classification Model for Call Center Purchase Prediction

Kai Shuang, Kai-Ze Ding, Xi-Hao Liu, Xiao-Le Wen

Abstract


In call center [1] 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 [2] 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.


Keywords


call center; K-Means; purchase prediction; SVM;

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DOI: http://dx.doi.org/10.12928/telkomnika.v15i1.4269

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