Open AccessA Prediction to Choose Customers in Auto Ancillary Automotive Products using K-Tree-Bayes Model for Improving Business Profits and Retention
Author(s) -
Dr K. Shyamala*,
C.S Padmasini
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d7391.118419
Subject(s) - automotive industry , customer retention , product (mathematics) , business , tree (set theory) , work (physics) , marketing , naive bayes classifier , promotion (chess) , bayes' theorem , computer science , decision tree , service (business) , machine learning , artificial intelligence , bayesian probability , engineering , mathematics , mechanical engineering , mathematical analysis , geometry , aerospace engineering , support vector machine , service quality , politics , law , political science
Customers are of paramount importance for running business enterprises. A K-Tree-Bayes model, when applied for the purpose of customer retention and business promotion, it retains them and their favorite choices. This model is extended to work in various aspects as and when new customers as well as existing customers provide their wishes and those data will become impertinent to improve the product in all aspects right from the manufacturing to reach to the customers. The model shows reasonable accuracy to predict the changing customer choices towards their desire to buy any automotive as companies are investing heavily on customer prediction thereby providing automotive of their choices to retain them forever.