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New Insight into Customer Value Analysis using Data Mining Techniques

by Nesma Taher, Shaimaa Salama, Doaa ElZanfaly
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 3
Year of Publication: 2017
Authors: Nesma Taher, Shaimaa Salama, Doaa ElZanfaly
10.5120/ijca2017915560

Nesma Taher, Shaimaa Salama, Doaa ElZanfaly . New Insight into Customer Value Analysis using Data Mining Techniques. International Journal of Computer Applications. 176, 3 ( Oct 2017), 27-38. DOI=10.5120/ijca2017915560

@article{ 10.5120/ijca2017915560,
author = { Nesma Taher, Shaimaa Salama, Doaa ElZanfaly },
title = { New Insight into Customer Value Analysis using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 176 },
number = { 3 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 27-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number3/28533-2017915560/ },
doi = { 10.5120/ijca2017915560 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:41:10.042568+05:30
%A Nesma Taher
%A Shaimaa Salama
%A Doaa ElZanfaly
%T New Insight into Customer Value Analysis using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 3
%P 27-38
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recency, Frequency, Monetary model (RFM) has been widely used to analyze the customers’ value in traditional market using three purchasing behavior attributes. This is considered one dimensional view of customers’ value that is based on profit and purchasing criteria and ignores other useful attributes. Online customers have additional attributes that when captured and analyzed can give more details about customers’ value other than provided by traditional RFM model. This gives companies better vision of their customers, and therefore serve them effectively, resulting in strong and long relationship with them. New Behavioral RFM Model (BRFM) is proposed in this paper to provide online retailers with a new customers' insight that reflects their web behavior beside their profitability. Three web behavioral attributes, represented in Recency of Session (Rs), Frequency of Session (Fs), and Number of clicks (NoC) are added to the traditional RFM attributes for customer value segmentation in online market using K-means clustering algorithm. The effectiveness of BRFM model is compared against the traditional RFM using Dunn index and Davies- Bouldin measures. Results show that the BRFM model enhances the clustering accuracy and reveals new customers’ clusters disregarded by the traditional RFM model.

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Index Terms

Computer Science
Information Sciences

Keywords

Customer value analysis Recency Frequency Monetary Model K-means clustering algorithm Dunn Index (DI) Davies Bouldin (DB)

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