Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Analysis of shopping website visit types and shopping pattern

쇼핑 웹사이트 탐색 유형과 방문 패턴 분석

  • Choi, Kyungbin (Department of Business Informatics, Hanyang University) ;
  • Nam, Kihwan (Information Systems, Management Engineering, College of Business, Korea Advanced Institute of Science and Technology (KAIST))
  • 최경빈 (한양대학교 일반대학원 비즈니스인포매틱스학과) ;
  • 남기환 (KAIST 경영대학원 경영공학부)
  • Received : 2018.11.16
  • Accepted : 2019.03.11
  • Published : 2019.03.31
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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.

온라인 소비자는 쇼핑 웹사이트에서 특정 제품군이나 브랜드에 속한 제품들을 둘러보고 구매를 진행할 수 있고, 혹은 단순히 넓은 범위의 탐색 반경을 보이며 여러 페이지들을 돌아보다 구매를 진행하지 않고 이탈할 수 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 단계까지 전환될 확률은 매우 낮은 실정이다. 이는 온라인 소비자들이 단지 제품 구매를 위해 웹사이트를 방문하는 것이 아니라 그들의 쇼핑 동기 및 목적에 따라 웹사이트를 다르게 활용하고 탐색하기 때문이다. 따라서 단지 구매가 진행되는 방문 외에도 다양한 방문 형태를 분석하는 것은 온라인 소비자들의 행동을 이해하는데 중요하다고 할 수 있다. 이러한 관점에서 본 연구에서는 온라인 소비자의 탐색 행동의 다양성과 복잡성을 설명하기 위해 실제 E-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 소비자 개인이 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.

Keywords

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Online Consumer Decision Process (Karimi et al., 2015)

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Analysis Process in this study

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Constructing Sequence data

Online shopping types (Moe, 2003)

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Characteristics of visit types (Moe, 2003; Liu et al., 2012; Pallant et al., 2017)

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Definition session-level variables

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Session-level variable statistics

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Cluster Solution

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Frequent pattern of user who finished product purchase

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Frequent patterns of non-purchasing users

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Mean support(confidence) of Common patterns derived from the group of users

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References

  1. Agrawal, Rakesh, and Ramakrishnan Srikant. "Mining sequential patterns.", Data Engineering, 1995. Proceedings of the Eleventh International Conference on. IEEE, 1995.
  2. Ba, Sulin, Andrew B. Whinston, and Han Zhang. "Building trust in online auction markets through an economic incentive mechanism.", Decision Support Systems, Vol.35, No.3 (2003), 273-286. https://doi.org/10.1016/S0167-9236(02)00074-X
  3. Babin, Barry J., William R. Darden, and Mitch Griffin. "Work and/or fun: measuring hedonic and utilitarian shopping value.", Journal of consumer research, Vol.20, No.4(1994), 644-656. https://doi.org/10.1086/209376
  4. Bae, S., and Park, D., "The Effect of Mobile Advertising Platform through Big Data Analytics: Focusing on Advertising, and Media Characteristics" Journal of Intelligence and Information Systems, Vol24, No.2(2018), 37-57. https://doi.org/10.13088/JIIS.2018.24.2.037
  5. Bettman, James R., Mary Frances Luce, and John W. Payne. "Constructive consumer choice processes.", Journal of consumer research, Vol.25, No3 (1998), 187-217. https://doi.org/10.1086/209535
  6. Bradlow, Eric T., et al. "The role of big data and predictive analytics in retailing.", Journal of Retailing, Vol.93, No.1(2017), 79-95. https://doi.org/10.1016/j.jretai.2016.12.004
  7. Bridges, Eileen, and Renee Florsheim. "Hedonic and utilitarian shopping goals: The online experience.", Journal of Business research, Vol.61, No.4 (2008), 309-314. https://doi.org/10.1016/j.jbusres.2007.06.017
  8. Bronnenberg, B. J., Kim, J. B., & Mela, C. F. "Zooming in on choice: How do consumers search for cameras online?", Marketing Science, Vol.35, No.5 (2016), 693-712. https://doi.org/10.1287/mksc.2016.0977
  9. Bucklin, Randolph E., and Catarina Sismeiro. "Click here for Internet insight: Advances in clickstream data analysis in marketing.", Journal of Interactive marketing, Vol.23, No.1 (2009), 35-48. https://doi.org/10.1016/j.intmar.2008.10.004
  10. Bucklin, Randolph E., et al. "Choice and the Internet: From clickstream to research stream.", Marketing Letters, Vol.13, No.3 (2002), 245-258. https://doi.org/10.1023/A:1020231107662
  11. Chiu, Chao-Misn, et al. "Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents.", Decision Support Systems, Vol.53, No.4(2012), 835-845. https://doi.org/10.1016/j.dss.2012.05.021
  12. Eirinaki, Magdalini, and Michalis Vazirgiannis. "Web mining for web personalization.", ACM Transactions on Internet Technology (TOIT), Vol3, No.1(2003), 1-27. https://doi.org/10.1145/643477.643478
  13. Elkan, Charles. "Using the triangle inequality to accelerate k-means.", Proceedings of the 20th International Conference on Machine Learning (ICML-03), (2003) 147-153.
  14. Engel, James F., Roger D. Blackwell, and Paul W. Miniard, Consumer Behavior, Chicago: Dryden, 1993.
  15. Ganesh, Jaishankar, et al. "Online shopper motivations, and e-store attributes: an examination of online patronage behavior and shopper typologies.", Journal of retailing, Vol.86, No.1(2010), 106-115. https://doi.org/10.1016/j.jretai.2010.01.003
  16. Ghose, A., Goldfarb, A., & Han, S. P. "How is the mobile Internet different? Search costs and local activities.", Information Systems Research, Vol.24, No.3(2012), 613-631. https://doi.org/10.1287/isre.1120.0453
  17. Girard, Tulay, and Paul Dion. "Validating the search, experience, and credence product classification framework.", Journal of Business Research, Vol.63, No.9-10 (2010), 1079-1087. https://doi.org/10.1016/j.jbusres.2008.12.011
  18. Goh. K, Y.,, Chu. J., and Wu. J., "Mobile advertising: an empirical study of temporal and spatial differences in search behavior and advertising response.", Journal of Interactive Marketing, Vol.30(2015), 34-45. https://doi.org/10.1016/j.intmar.2014.12.002
  19. Guerbas, Abdelghani, et al. "Effective web log mining and online navigational pattern prediction.", knowledge-based systems, Vol.49 (2013), 50-62. https://doi.org/10.1016/j.knosys.2013.04.014
  20. Ha, Y., and Stephen J., H., "Ambiguity, Processing Strategy, and Advertising-Evidence Interactions," Journal of Consumer Research, Vol.16, No.3(1989), 354-60. https://doi.org/10.1086/209221
  21. Holbrook, Morris B., and Elizabeth C. Hirschman. "The experiential aspects of consumption: Consumer fantasies, feelings, and fun.", Journal of consumer research, Vol.9, No.2 (1982), 132-140. https://doi.org/10.1086/208906
  22. Hong, S., and Kar Yan Tam. "Understanding the adoption of multipurpose information appliances: The case of mobile data services.", Information systems research, Vol.17, No.2 (2006), 162-179. https://doi.org/10.1287/isre.1060.0088
  23. Huang, Peng, Nicholas H. Lurie, and Sabyasachi Mitra. "Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods.", Journal of marketing, Vol.73, No.2 (2009), 55-69. https://doi.org/10.1509/jmkg.73.2.55
  24. Howard, J., A., and Sheth. J., N., The theory of buyer behavior. New York, Wiley. 1969.
  25. Huang, Peng, Nicholas H. Lurie, and Sabyasachi Mitra. "Searching for experience on the web: an empirical examination of consumer behavior for search and experience goods.", Journal of marketing, Vol.73, No.2 (2009), 55-69. https://doi.org/10.1509/jmkg.73.2.55
  26. Huang, Yueh-Min, et al. "NP-miner: A real-time recommendation algorithm by using web usage mining.", Knowledge-Based Systems, Vol.19, No.4(2006), 272-286. https://doi.org/10.1016/j.knosys.2006.04.001
  27. Jacoby, Jacob, et al. "Tracing the impact of item-by-item information accessing on uncertainty reduction.", Journal of Consumer Research, Vol.21, No.2(1994), 291-303. https://doi.org/10.1086/209398
  28. Jain, A., K., "Data clustering: 50 years beyond K-means.", Pattern recognition letters, Vol.31, No.8 (2010), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011
  29. Janiszewski, Chris. "The influence of display characteristics on visual exploratory search behavior.", Journal of Consumer Research, Vol.25, No.3(1998), 290-301. https://doi.org/10.1086/209540
  30. Jansen, Bernard J., and Simone Schuster. "Bidding on the buying funnel for sponsored search and keyword advertising.", Journal of Electronic Commerce Research. Vol.12, No.1 (2011), 1.
  31. Karimi, S., Holland, C. P., and Papamichail, K. N. "The impact of consumer archetypes on online purchase decision-making processes and outcomes: A behavioural process perspective.", Journal of Business Research, Vol.91(2018), 71-82. https://doi.org/10.1016/j.jbusres.2018.05.038
  32. Karimi, Sahar, K. Nadia Papamichail, and Christopher P. Holland. "The effect of prior knowledge and decision-making style on the online purchase decision-making process: A typology of consumer shopping behaviour.", Decision Support Systems, Vol.77(2015), 137-147. https://doi.org/10.1016/j.dss.2015.06.004
  33. Kim, Y.S, and Krishnan R., "On product-level uncertainty and online purchase behavior: An empirical analysis.", Management Science, Vol.61, No.10(2015), 2449-2467. https://doi.org/10.1287/mnsc.2014.2063
  34. KOSIS, "2018 online shopping trends", 2018, http://kostat.go.kr/portal/korea/kor_nw/2/1/index.board?bmode=read&aSeq=370428
  35. Kukar-Kinney, Monika, and Angeline G. Close. "The determinants of consumers' online shopping cart abandonment.", Journal of the Academy of Marketing Science, Vol.38, No.2(2010), 240-250. https://doi.org/10.1007/s11747-009-0141-5
  36. Lee, D., "A Regression-Model-based Method for Combining Interestingness Measures of Association Rule Mining", Journal of Intelligence and Information Systems, Vol23, No.1(2017), 127-141. https://doi.org/10.13088/jiis.2017.23.1.127
  37. Liaukonyte, Jura, Thales Teixeira, and Kenneth C. Wilbur. "Television advertising and online shopping.", Marketing Science, Vol.34, No.3(2015), 311-330. https://doi.org/10.1287/mksc.2014.0899
  38. Liu, Fang, et al. "A Typology Of Online Window Shopping Consumers." PACIS. 2012, 128.
  39. Liu, Haibin, and Vlado Keselj. "Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users' future requests.", Data & Knowledge Engineering, Vol.61, No.2(2007), 304-330. https://doi.org/10.1016/j.datak.2006.06.001
  40. Lu, Jie, et al. "Recommender system application developments: a survey.", Decision Support Systems, Vol.74(2015), 12-32. https://doi.org/10.1016/j.dss.2015.03.008
  41. Mallapragada, Girish, Sandeep R. Chandukala, and Qing Liu. "Exploring the effects of "What"(product) and "Where"(website) characteristics on online shopping behavior.", Journal of Marketing, Vol.80, No.2 (2016), 21-38. https://doi.org/10.1509/jm.15.0138
  42. Moe, W. W., & Fader, P. S., "Dynamic conversion behavior at e-commerce sites.", Management Science, Vol.50, No.3(2004) 326-335. https://doi.org/10.1287/mnsc.1040.0153
  43. Moe, W. W., "An empirical two-stage choice model with varying decision rules applied to internet clickstream data.", Journal of Marketing Research, Vol.43, No.4 (2006), 680-692. https://doi.org/10.1509/jmkr.43.4.680
  44. Moe, W. W., "Buying, searching, or browsing: Differentiating between online shoppers using in-store navigational clickstream.", Journal of consumer psychology, Vol.13, No.1(2003), 29-39. https://doi.org/10.1207/153276603768344762
  45. Montgomery, A. L., et al. "Modeling online browsing and path analysis using clickstream data.", Marketing science, Vol.23, No.4(2004), 579-595. https://doi.org/10.1287/mksc.1040.0073
  46. Nelson, Phillip. "Advertising as information.", Journal of political economy, Vol.82, No.4 (1974), 729-754. https://doi.org/10.1086/260231
  47. Nelson, Phillip. "Information and consumer behavior.", Journal of political economy, Vol.78, No.2(1970), 311-329. https://doi.org/10.1086/259630
  48. Pabarskaite, Zidrina, and Aistis Raudys. "A process of knowledge discovery from web log data: Systematization and critical review.", Journal of Intelligent Information Systems, Vol.28, No.1(2007), 79-104. https://doi.org/10.1007/s10844-006-0004-1
  49. Pallant, J. I., et al. "An empirical analysis of factors that influence retail website visit types.", Journal of Retailing and Consumer Services, Vol.39(2017), 62-70. https://doi.org/10.1016/j.jretconser.2017.07.003
  50. Park J., and Cho Y., "Clickstream Big Data Mining for Demographics based Digital Marketing", Journal of Intelligence and Information Systems, Vol22, No.3(2016), 143-163. https://doi.org/10.13088/jiis.2016.22.3.143
  51. Pavlou, P. A., and Fygenson, M., "Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior.", MIS quarterly, Vol.30, No.1(2006), 115-143. https://doi.org/10.2307/25148720
  52. Peck, J., and Childers, T. L., "To have and to hold: The influence of haptic information on product judgments.", Journal of Marketing, Vol.67, No.2(2003), 35-48. https://doi.org/10.1509/jmkg.67.2.35.18612
  53. Pei, Jian, et al. "Mining sequential patterns by pattern-growth: The prefixspan approach.", IEEE Transactions on Knowledge & Data Engineering, Vol.11(2004), 1424-1440. https://doi.org/10.1109/TKDE.2004.77
  54. Phang, C. W., Kankanhalli, A., Ramakrishnan, K., & Raman, K. S. "Customers' preference of online store visit strategies: an investigation of demographic variables." European Journal of Information Systems, Vol.19, No.3(2010), 344-358. https://doi.org/10.1057/ejis.2010.32
  55. Raphaeli, O., Goldstein, A., and Fink, L., "Analyzing online consumer behavior in mobile and PC devices: A novel web usage mining approach.", Electronic Commerce Research and Applications, Vol.26(2017), 1-12. https://doi.org/10.1016/j.elerap.2017.09.003
  56. Rohm, A. J., and Swaminathan, V., "A typology of online shoppers based on shopping motivations.", Journal of business research, Vol.57, No.7(2004), 748-757. https://doi.org/10.1016/S0148-2963(02)00351-X
  57. Scarpi, D., "Work and fun on the internet: the effects of utilitarianism and hedonism online.", Journal of interactive marketing, Vol.26, NO.1(2012), 53-67. https://doi.org/10.1016/j.intmar.2011.08.001
  58. Sculley, David, "Web-scale k-means clustering.", Proceedings of the 19th international conference on World wide web. ACM, 2010.
  59. Sismeiro, C., and Bucklin, R. E., "Modeling purchase behavior at an e-commerce web site: A task-completion approach.", Journal of marketing research, Vol.41, No.3(2004), 306-323. https://doi.org/10.1509/jmkr.41.3.306.35985
  60. Statista, "Conversion rate of online shoppers in the United States as of 2nd quarter 2018", 2018, https://www.statista.com/statistics/439558/us-online-shopper-conversion-rate/
  61. Statista, "Retail e-commerce sales worldwide from 2014 to 2021 (in billion U.S. dollars)" 2018, https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/