ABSTRACT
Electronic commerce (henceforth referred to as e-commerce) has attracted many people to buy things online because of its convenience. With Covid-19 pandemic, the popularity of e-commerce increases as many people are working from home. Ability to understand customers' surfing and buying behavior on the e-commerce platform provides competitive advantage to e-commerce companies by being able to devise specific marketing plans to increase their market coverage and subsequently revenues from online sales of products. This paper discusses how the results derived from both, the exploratory data analysis (EDA) and association rule mining (ARM) can assist e-commerce companies to design specific marketing plans. The methodology consists of data understanding, data pre-processing, EDA, ARM, and analysis of results. A public dataset that is made available in the year 2020 consisting of clickstream data that are collected in 2018 from a popular fashion e-commerce website is used as a case study to prove the viability of the methodology in deriving results that can be used to design specific marketing plans. This study proves that it is possible to use clickstream data consisting of customers' surfing and buying behavior and apply the methodology to derive analysis and devise better marketing plans. © 2022, Success Culture Press. All rights reserved.
ABSTRACT
Electronic commerce (henceforth referred to as e-commerce) has attracted many people to buy things online because of its convenience. With Covid-19 pandemic, the popularity of e-commerce increases as many people are working from home. Ability to understand customers' surfing and buying behavior on the e-commerce platform provides competitive advantage to e-commerce companies by being able to devise specific marketing plans to increase their market coverage and subsequently revenues from online sales of products. This paper discusses how the results derived from both, the exploratory data analysis (EDA) and association rule mining (ARM) can assist e-commerce companies to design specific marketing plans. The methodology consists of data understanding, data pre-processing, EDA, ARM, and analysis of results. A public dataset that is made available in the year 2020 consisting of clickstream data that are collected in 2018 from a popular fashion e-commerce website is used as a case study to prove the viability of the methodology in deriving results that can be used to design specific marketing plans. This study proves that it is possible to use clickstream data consisting of customers’ surfing and buying behavior and apply the methodology to derive analysis and devise better marketing plans. © 2022, Success Culture Press. All rights reserved.