A Classification Approach For Naive Bayes of Online Retailers
Journal: Acta Informatica Malaysia (AIM)
Author: Aida Mustapha, Shazwani Mustapa, Nurfarahim Md.Azlan, Noor Fatin Ishmah Saifarrudin, Shahreen Kasim, Mohd Farhan Md Fudzee, Azizul Azhar Ramli, Hairulnizam Mahdin, Seah Choon Sen
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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.
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