A CLASSIFICATION APPROACH FOR NAÏVE BAYES OF ONLINE RETAILERS
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Zibeline International
2017-02-01
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| Series: | Acta Informatica Malaysia |
| Subjects: | |
| Online Access: | https://actainformaticamalaysia.com/archives/AIM/1aim2017/1aim2017-26-28.pdf |
| Summary: | 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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| ISSN: | 2521-0874 2521-0505 |