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Customer Analysis Model for Trusted Online Merchant

Osama Emam, Arafa Taher, Hanan Fahmy. Published in Information Sciences.

Communications on Applied Electronics
Year of Publication: 2020
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Osama Emam, Arafa Taher, Hanan Fahmy
10.5120/cae2020652872

Osama Emam, Arafa Taher and Hanan Fahmy. Customer Analysis Model for Trusted Online Merchant. Communications on Applied Electronics 7(34):18-24, October 2020. BibTeX

@article{10.5120/cae2020652872,
	author = {Osama Emam and Arafa Taher and Hanan Fahmy},
	title = {Customer Analysis Model for Trusted Online Merchant},
	journal = {Communications on Applied Electronics},
	issue_date = {October 2020},
	volume = {7},
	number = {34},
	month = {Oct},
	year = {2020},
	issn = {2394-4714},
	pages = {18-24},
	numpages = {7},
	url = {http://www.caeaccess.org/archives/volume7/number34/874-2020652872},
	doi = {10.5120/cae2020652872},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Trust is one of the most important factors which influence the behavior of online shopping. Previous researchers indicated several factors that affect online brand shopping. Security, privacy, and trust are among these factors. This paper tries to propose a new model is called Merchant Segmentation Trust (MST) based on the huge amount of data comes from online shopping not on the last-mentioned factors using bigdata techniques. The propose model (MST) consist of four phases, the first phase is processing the data (cleaned, prepared, transformed) to extract the useful transactions attributes like order date, ship date, number of complaints, duration of complaints, net profit, etc. And aggregate all merchant with its transactions in form that can be used by the clustering algorithm. The second phase is to using the bigdata techniques as the huge amount of data comes from the rapid growth of online shopping by using K-means clustering algorithm. The third phase using traditional Recency, Frequency, Monetary (RFM) model to rank the resulting clusters to define the most trustable merchant, finally evaluate the clustering accuracy by using sum square error (SSE) and extract the most trustable merchant by the propose MST model compared against the traditional factors which result from the review or from the survey to ensure that merchant’s behavior can rate the trust among other merchants.

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Keywords

Bigdata; CRM; K-means clustering algorithm; RFM model; clv rank; sum square error (SSE)