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A classification approach for Naïve Bayes of online retailers

Mustapha, Aida and Mustapa, Shazwani and Md. Azlan, Nurfarahim and Saifarrudin, Noor Fatin Ishmah and Kasim, Shahreen and Md Fudzee, Mohd Farhan and Ramli, Azizul Azhar and Mahdin, Hairulnizam and Seah, Choon Sen (2017) A classification approach for Naïve Bayes of online retailers. Acta Informatica Malaysia (AIM), 1 (1). pp. 26-28. ISSN 25210505

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Abstract

Many small online retailers and new entrants to the online retail sector are keen to practice data mining and consumer-centric marketing in their businesses yet technically lack the necessary knowledge and expertise to do so. In this article a case study of using data mining techniques in customer-centric business intelligence for an online retailer is presented. The main purpose of this analysis is to help the business better understand its customers and therefore conduct customer-centric marketing more effectively. On the basis of the Recency, Frequency, and Monetary model, customers of the business have been segmented into various meaningful groups using the classification and naïve bayes algorithm, and the main characteristics of the consumers in each segment have been clearly identify ed. Accordingly a set of recommendations is further provided to the business on consumer-centric marketing.

Item Type: Article
Uncontrolled Keywords: Consumer-centric marketing; online retailer
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Mar 2019 07:37
Last Modified: 31 Mar 2019 07:37
URI: http://eprints.uthm.edu.my/id/eprint/10907
Statistic Details: View Download Statistic

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