Comparisons of Website Visit Behavior between Purchase Outcomes and Product Categories
Keywords:Website Visit Behavior, Purchase Outcomes, Product Categories
The online retail business has grown substantially. Given distinctive product categories (e.g. search or experience goods), owners must put an effort in the design of websites so every visit may end with a purchase. Clickstream panel data allowing examination into website visiting behavior (i.e. the number of pages viewed (or pageview) or the visit duration) are increasingly accessible. However, it is unclear whether the differences of the two visiting behavior between purchase outcome or product categories are significant. The present study hopes to fill the void. An analysis of 27,528 visit sessions extracted from ComScore verifies that (1) the difference of page views between purchase outcomes and that between product categories were significant and (2) only the difference of visit duration between the product categories was significant but that between purchase outcomes was insignificant. In addition to theoretical insight into online behavior across purchasing horizons and product categories using clickstream data, online retail practitioners could apply the findings to enhance the possibility of the purchases at their online stores.
Baird, N. (2017). Five Predictions For Retail in 2017. Available at https://www.forbes.com/ sites/nikkibaird/2017/01/04/five-predictions-for-retail-in-2017/#692da78d4882 Accessed on March 28, 2017.
Bucklin, R., Lattin, J., Ansari, A., Gupta, S., Bell, D., Coupey, E. Little, J. Mela, C., Montgomery, A. & Steckel, J. (2002). Choice and the Internet: From Clickstream to Research Stream, Marketing Letters, 13(3), 245-258.
Claase, M. (2014). Why Do Users Bond with Online Services?: A Literature Review on The Conceptualization of Online User Bonding in the Context of Online Services. Unpublished master Thesis. University of Twente. Available at http://essay.utwente.nl/65432/1/Claase_MA_ Behavioural_Management_ and_Social_Sciences.pdf. Accessed on March 28, 2017
Danaher, P. J. (2007). Modeling page views across multiple websites with an application to Internet reach and frequency prediction, Marketing Science, 26(3), 422-437. DOI: 10.1287/mksc.l060.022.
Danaher, P., Mallarkey, G., & Essegaier, S. (2006). Affecting web site visit duration: A cross-domain analysis, Journal of Marketing Research, 43(2), 182-194.
Gustafson, K. (2017). Retailers brace for 2017: Expect More Store Closings and Major Changes under Trump's Tax Policy. Available at http://www.cnbc.com/2016/12/27/retail-predictions-for-2017.html Accessed on March 28, 2017.
Hair, J. F. (2013). Essentials of marketing research, 3rd edition, NY: McGraw-Hill.
Huang, L., Jia, L., & Song, J. (2015). Antecedents of User Stickiness and Loyalty and Their Effects on Users’ Group-Buying Repurchase Intention. Available at http://aisel.aisnet.org/amcis2015/e-Biz/ GeneralPresentations/ 1/ Accessed on March 28, 2017.
Kim, S., Baek, T., Kim, Y.-K., & Yoo, K. (2016). Factors Affecting Stickiness and Word of Mouth in Mobile Applications, Journal of Research in Interactive Marketing, 10(3),177-192. https://doi.org/10.1108/JRIM-06-2015-0046.
Kousha, K., & Thelwall, M. (2016). Can Amazon. com reviews help to assess the wider impacts of books?. Journal of the Association for Information Science and Technology, 67(3), 566-581. DOI: 10.1002/asi.23404.
Lin, J. (2007). Online Stickiness: Its Antecedents and Effect on Purchasing Intention. Behaviour & Information Technology, 26(6), 507-516. http://dx.doi.org/10.1080/01449290600740843.
Lin, L., Hu, P. J. H., Sheng, O. R. L., & Lee, J. (2010). Is stickiness profitable for electronic retailers?. Communications of the ACM, 53(3), 132-136. DOI: 10.1145/1666420.1666454
Mallapragada , G., Chandukala, S. R., & Liu, Q. (2016). Exploring the Effects of “What” (Product) and “Where” (Website) Characteristics on Online Shopping Behavior. Journal of Marketing, 80(2), 21-38. http://dx.doi.org/10.1509/jm.15.0138.
Moe, W. W. (2003). Buying, Searching, or Browsing: Differentiating Between Online Shoppers Using In-Store Navigational Clickstream. Journal of Consumer Psychology, 13(1&2), 29-39. https://doi.org/10.1207/S15327663JCP13-1&2_03.
Moe, W. W. & Fader, P. S. (2004). Dynamic Conversion Behavior at E-Commerce Sites. Management Science, 50(3), 326-335. https://doi.org/10.1287/mnsc.1040.0153.
Montgomery, A. L., Li, S., Srinivasan, K., & Liechty, J. C. (2004). Modeling Online Browsing and Path Analysis Using Clickstream Data. Marketing Science, 23(4), 579-595. https://doi.org/10.1287/mksc.1040.0073.
Okada, K. & Takahashi, H. (2015). Page View-Based Investor Attention and IPO Pricing. Unpublished manuscript. Available online at http://sfm.finance.nsysu.edu.tw/php/Papers/ CompletePaper/066-279035158.pdf. Accessed on March 28, 2017.
Olbrich, R. & Holsing, C. (2011). Modeling Customer Purchasing Behavior in Social Shopping Communities with Clickstream Data. International Journal of Electronic Commerce, 16(2), 15-40. http://dx.doi.org/10.2753/JEC1086-4415160202.
Panagiotelis, A., Smith, M., & Danaher, P. (2014). From Amazon to Apple: Modeling Online Retail Sales, Purchase Incidence, and Visit Behavior, Journal of Business & Economic Statistics, 32(1), 14-29. http://dx.doi.org/10.1080/07350015.2013.835729.
Roy, S., Lassar, W., & T. Butaney, G. (2014). The Mediating Impact of Stickiness and Loyalty on Word-of-Mouth Promotion of Retail Websites: A Consumer Perspective. European Journal of Marketing, 48(9/10), 1828-1849. https://doi.org/10.1108/EJM-04-2013-0193.
Similarweb (2016). Amazon Analytics. Available at https://www.similarweb.com/website/ amazon.com Accessed on March 28, 2017.
Sunikka, A., Bragge, J., & Kallio, H. (2011). The Effectiveness of Personalized Marketing in Online Banking: A Comparison between Search and Experience Offerings. Journal of Financial Services Marketing, 16(3-4), 183-194. https://doi.org/10.1057/fsm.2011.24.
Verheijden, R. (2012). Predicting purchasing behavior throughout the clickstream. Unpublished master thesis, Master of Science in Innovation Sciences, Eindhoven University of Technology.
Xun, J. (2015). Return on Web Site Visit Duration: Applying Web Analytics Data. Journal of Direct, Data and Digital Marketing Practice, 17(1), 54-70. https://doi.org/10.1057/dddmp.2015.33.
Yan, L., & Li, Chunping. (2006). Incorporating Pageview Weight into an Association-Rule-Based Web Recommendation System. in Australasian Joint Conference on Artificial Intelligence. Springer Berlin Heidelberg, 577-586.
How to Cite
Copyright (c) 2017 Chatpong Tangmanee
This work is licensed under a Creative Commons Attribution 4.0 International License.
For all articles published in IJRBS, copyright is retained by the authors. Articles are licensed under an open access Creative Commons CC BY 4.0 license, meaning that anyone may download and read the paper for free. In addition, the article may be reused and quoted provided that the original published version is cited. These conditions allow for maximum use and exposure of the work, while ensuring that the authors receive proper credit.