Modeling Online Browsing and Path Analysis Using Clickstream Data

2004

353 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Marketing, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/94adb8078cf515a0412fafa8f0bc7d06e22e5561

Citations in the corpus

• eTransQual: A Transaction Process-Based Approach for Capturing Service Quality in Online Shopping (2006, neutral): “The idea of improving the measurement of service quality by grouping user activities into a number of discrete stages is supported by the task completion approach (Montgomery et al., 2004; Sismeiro and Bucklin, 2004).”
• Conditioning Prices on Purchase History (2005, neutral): “Such inducements keep some users loyal, but others may find it attractive to repeatedly join various clubs in order to cream-skim the low-price offers.”
• Website Morphing (2009, neutral): “( We provide greater detail and citations in a later section.)”
• Searching for Experience on the Web: An Empirical Examination of Consumer Behavior for Search and Experience Goods (2009, neutral): “The availability of URL information would permit a more detailed assessment of the underlying data viewed by the consumer (Montgomery et al. 2004).”
• Modeling Purchase Behavior at an E-Commerce Web Site: A Task-Completion Approach (2004, neutral): “While Montgomery et al (2003) model the entire path of page choices, we identify and model the user’s progress past critical points of attrition in the tasks required for purchase.”
• Marketing Analytics for Data-Rich Environments (2016, positive): “Such models have been successfully applied to advertisement eye tracking (Wedel and Pieters 2000), e-mail marketing (Ansari and Mela 2003), web browsing (Montgomery et al. 2004), social networks (Moe and Trusov 2011), and paid search advertising (Rutz, Trusov, and Bucklin 2011).”
• Recent Developments in Dynamic Pricing Research: Multiple Products, Competition, and Limited Demand Information (2015, neutral): “There are models (e.g., Moe and Fader 2004, Montgomery et al. 2004) that can predict with a certain accuracy whether a customer is going to make a purchase based on the customer’s browsing history.”
• The Role of Big Data and Predictive Analytics in Retailing (2017, neutral): “…be tracked using RFID chips inside shopping carts (see, e.g., Larson, Bradlow and Fader 2005) or inside virtual stores using clickstream data (e.g., Montgomery et al. 2004), the distribution of firstcut emotional responses to varied product and context stimuli which neuro-marketing researchers are…”
• Online Demand Under Limited Consumer Search (2010, neutral): “i=1 NS(S∗ i |L = {Lj})−NS(S i |L = ∅), (30)”