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Analysis of shopping website visit types and shopping pattern
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Kyungbin Choi (Department of Business Informatics, Hanyang University)
Kihwan Nam (Information Systems, Management Engineering, College of Business, Korea Advanced Institute of Science and Technology (KAIST))
Vol. 25, No. 1, Page: 85 ~ 107
10.13088/jiis.2019.25.1.085
Keywords
E-Commerce, Online consumer behavior, Web usage mining, Sequential pattern mining, Clickstream data
Abstract
Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers.
In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster.
The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing.
We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time.
And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product.
The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.
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쇼핑 웹사이트 탐색 유형과 방문 패턴 분석
최경빈 (한양대학교 일반대학원 비즈니스인포매틱스학과)
남기환 (KAIST 경영대학원 경영공학부)
Keywords
E-Commerce, 온라인 소비자 행동, 웹 사용 마이닝, 순차 패턴 마이닝, 클릭스트림 데이터
Abstract
온라인 소비자는 쇼핑 웹사이트에서 특정 제품군이나 브랜드에 속한 제품들을 둘러보고 구매를 진행할 수있고, 혹은 단순히 넓은 범위의 탐색 반경을 보이며 여러 페이지들을 돌아보다 구매를 진행하지 않고 이탈할수 있다. 이러한 온라인 소비자의 행동과 구매에 관련된 연구는 꾸준히 진행되어왔으며, 실무에서도 소비자들의 행동 데이터를 바탕으로 한 서비스 및 어플리케이션이 개발되고 있다. 최근에는 빅데이터 기술의 발달로 소비자 개인 단위의 맞춤화 전략 및 추천 시스템이 활용되고 있으며 사용자의 쇼핑 경험을 최적화하기 위한 시도가 진행되고 있다. 하지만 이와 같은 시도에도 온라인 소비자가 실제로 웹사이트를 방문해 제품 구매 단계까지전환될 확률은 매우 낮은 실정이다. 이는 온라인 소비자들이 단지 제품 구매를 위해 웹사이트를 방문하는 것이아니라 그들의 쇼핑 동기 및 목적에 따라 웹사이트를 다르게 활용하고 탐색하기 때문이다. 따라서 단지 구매가진행되는 방문 외에도 다양한 방문 형태를 분석하는 것은 온라인 소비자들의 행동을 이해하는데 중요하다고 할수 있다. 이러한 관점에서 본 연구에서는 온라인 소비자의 탐색 행동의 다양성과 복잡성을 설명하기 위해 실제E-commerce 기업의 클릭스트림 데이터를 기반으로 세션 단위의 클러스터링 분석을 진행해 탐색 행동을 유형화하였다. 이를 통해 각 유형별로 상세 단위의 탐색 행동과 구매 여부가 차이가 있음을 확인하였다. 또한 소비자개인이 여러 방문에 걸친 일련의 탐색 유형에 대한 패턴을 분석하기 위해 순차 패턴 마이닝 기법을 활용하였으며, 같은 기간 내에 제품 구매까지 완료한 소비자와 구매를 진행하지 않은 채 방문만 진행한 소비자들의 탐색패턴에 대한 차이를 확인할 수 있었다. 본 연구의 시사점은 대규모의 클릭스트림 데이터를 활용해 온라인 소비자의 탐색 유형을 분석하고 이에 대한 패턴을 분석해 구매 과정 상의 행동을 데이터 기반으로 설명하였다는 점에 있다. 또한 온라인 소매 기업은 다양한 형태의 탐색 유형에 맞는 마케팅 전략 및 추천을 통해 구매 전환 개선을 시도할 수 있으며, 소비자의 탐색 패턴의 변화를 통해 전략의 효과를 평가할 수 있을 것이다.
Cite this article
JIIS Style
Choi, K., and K. Nam, "Analysis of shopping website visit types and shopping pattern", Journal of Intelligence and Information Systems, Vol. 25, No. 1 (2019), 85~107.

IEEE Style
Kyungbin Choi, and Kihwan Nam, "Analysis of shopping website visit types and shopping pattern", Journal of Intelligence and Information Systems, vol. 25, no. 1, pp. 85~107, 2019.

ACM Style
Choi, K., and Nam, K., 2019. Analysis of shopping website visit types and shopping pattern. Journal of Intelligence and Information Systems. 25, 1, 85--107.
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@article{Choi:JIIS:2019:762,
author = {Choi, Kyungbin and Nam, Kihwan},
title = {Analysis of shopping website visit types and shopping pattern},
journal = {Journal of Intelligence and Information Systems},
issue_date = {March 2019},
volume = {25},
number = {1},
month = Mar,
year = {2019},
issn = {2288-4866},
pages = {85--107},
url = {http://dx.doi.org/10.13088/jiis.2019.25.1.085 },
doi = {10.13088/jiis.2019.25.1.085},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { E-Commerce, Online consumer behavior, Web usage mining, Sequential pattern mining and Clickstream data
},
}
%0 Journal Article
%1 762
%A Kyungbin Choi
%A Kihwan Nam
%T Analysis of shopping website visit types and shopping pattern
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 25
%N 1
%P 85-107
%D 2019
%R 10.13088/jiis.2019.25.1.085
%I Korea Intelligent Information System Society