An efficient customer classification framework to identify target customers by Bayesian networks in automotive industry Online publication date: Fri, 06-Jan-2017
by Amir Ebrahimzadeh Pilerood; Mohammad Reza Gholamian
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 8, No. 4, 2016
Abstract: In this paper, an efficient customer classification framework is proposed which can be used instead of recency-frequency-monetary-based analysis as it preserves much more information about customers and at the same time it can consider different types of dependencies among purchases. Our proposed model is the best for industries with dependent and low number of purchases for each customer like automobile industry. In this model, by segmenting customers into different subsidiaries based on their number of purchases, an efficient customer classification model is developed to identify target customers. The proposed model is applied on one of the largest automobile manufacturer of the Middle-East and different performance metrics verified its efficiency.
Online publication date: Fri, 06-Jan-2017
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com