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Authors: Indra Ranggadara 1 ;  Sfenrianto 2 ; Ifan Prihandi 1 and Nilo Legowo 3

Affiliations: 1 Universitas Mercu Buana, Indonesia ; 2 Information Systems Management Department, BINUS Graduate Program – Master of Information Systems Management, Bina Nusantara University, Jakarta, Indonesia 11480, Indonesia ; 3 Bina Nusantara University, Indonesia

Keyword(s): Classification; Decision Making; E-commerce; Naïve Bayes; RFM Model

Abstract: The problem faced by the e-commerce industry in determining customer loyalty is that it is challenging to be classified because to set strategy in every year the company should define customers who are feasible in terms of loyalty to the company. The differentiator in this study uses Naive Bayes as a classification method in detail to the attributes that are tested and the customer is classified by the RFM method and in previous studies that have been conducted by other researchers are still little discussing the combining of these two methods between Naive Bayes and RFM, then positioning in this research between ecommerce business actors, the business competition to get customer loyalty is very important as a basis for taking appropriate decision making for stakeholders. Then the result from Naive Bayes is 62% feasible and not feasible 38% then assisted by RFM method as data analysis to each customer based on segmentation use "usage rate" attribute on data so that with processed dat a can make an essential reference in making decisions (More)

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Paper citation in several formats:
Ranggadara, I.; Sfenrianto.; Prihandi, I. and Legowo, N. (2020). Customer Loyalty Classification with RFM and Naïve Bayes for Decision Making in Indonesia E-Commerce Industry. In Proceedings of the International Conference on Creative Economics, Tourism and Information Management - ICCETIM; ISBN 978-989-758-451-0, SciTePress, pages 147-152. DOI: 10.5220/0009866201470152

@conference{iccetim20,
author={Indra Ranggadara. and Sfenrianto. and Ifan Prihandi. and Nilo Legowo.},
title={Customer Loyalty Classification with RFM and Naïve Bayes for Decision Making in Indonesia E-Commerce Industry},
booktitle={Proceedings of the International Conference on Creative Economics, Tourism and Information Management - ICCETIM},
year={2020},
pages={147-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009866201470152},
isbn={978-989-758-451-0},
}

TY - CONF

JO - Proceedings of the International Conference on Creative Economics, Tourism and Information Management - ICCETIM
TI - Customer Loyalty Classification with RFM and Naïve Bayes for Decision Making in Indonesia E-Commerce Industry
SN - 978-989-758-451-0
AU - Ranggadara, I.
AU - Sfenrianto.
AU - Prihandi, I.
AU - Legowo, N.
PY - 2020
SP - 147
EP - 152
DO - 10.5220/0009866201470152
PB - SciTePress

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