Customer Profiling using Classification Approach for Bank Telemarketing
customer profiling
QA76.75-76.765
330
classification
HF5410-5417.5 Marketing. Distribution of products
decision tree
0211 other engineering and technologies
0202 electrical engineering, electronic engineering, information engineering
data mining
Computer software
02 engineering and technology
650
DOI:
10.30630/joiv.1.4-2.68
Publication Date:
2018-02-14T15:58:50Z
AUTHORS (4)
ABSTRACT
Telemarketing is a type of direct marketing where a salesperson contacts the customers to sell products or services over the phone. The database of prospective customers comes from direct marketing database. It is important for the company to predict the set of customers with highest probability to accept the sales or offer based on their personal characteristics or behavior during shopping. Recently, companies have started to resort to data mining approaches for customer profiling. This project focuses on helping banks to increase the accuracy of their customer profiling through classification as well as identifying a group of customers who have a high probability to subscribe to a long term deposit. In the experiments, three classification algorithms are used, which are Naïve Bayes, Random Forest, and Decision Tree. The experiments measured accuracy percentage, precision and recall rates and showed that classification is useful for predicting customer profiles and increasing telemarketing sales.
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