TY - JOUR
T1 - Learning user real-time intent for optimal dynamic web page transformation
AU - Ding, Amy Wenxuan
AU - Li, Shibo
AU - Chatterjee, Patrali
N1 - Publisher Copyright:
© 2015 INFORMS.
PY - 2015
Y1 - 2015
N2 - Many e-commerce websites struggle to turn visitors into real buyers. Understanding online users' real-time intent and dynamic shopping cart choices may have important implications in this realm. This study presents an individual-level, dynamic model with concurrent optimal page adaptation that learns users' real-time, unobserved intent from their online cart choices, then immediately performs optimal Web page adaptation to enhance the conversion of users into buyers. To suggest optimal strategies for concurrent page adaptation, the model analyzes each individual user's browsing behavior, tests the effectiveness of different marketing and Web stimuli, as well as comparison shopping activities at other sites, and performs optimal Web page transformation. Data from an online retailer and a laboratory experiment reveal that concurrent learning of the user's unobserved purchase intent and real-time, intent-based optimal interventions greatly reduce shopping cart abandonment and increase purchase conversions. If the concurrent, intent-based optimal page transformation for the focal site starts after the first page view, shopping cart abandonment declines by 32.4% and purchase conversion improves by 6.9%. The optimal timing for the site to intervene is after three page views, to achieve efficient learning of users' intent and early intervention simultaneously.
AB - Many e-commerce websites struggle to turn visitors into real buyers. Understanding online users' real-time intent and dynamic shopping cart choices may have important implications in this realm. This study presents an individual-level, dynamic model with concurrent optimal page adaptation that learns users' real-time, unobserved intent from their online cart choices, then immediately performs optimal Web page adaptation to enhance the conversion of users into buyers. To suggest optimal strategies for concurrent page adaptation, the model analyzes each individual user's browsing behavior, tests the effectiveness of different marketing and Web stimuli, as well as comparison shopping activities at other sites, and performs optimal Web page transformation. Data from an online retailer and a laboratory experiment reveal that concurrent learning of the user's unobserved purchase intent and real-time, intent-based optimal interventions greatly reduce shopping cart abandonment and increase purchase conversions. If the concurrent, intent-based optimal page transformation for the focal site starts after the first page view, shopping cart abandonment declines by 32.4% and purchase conversion improves by 6.9%. The optimal timing for the site to intervene is after three page views, to achieve efficient learning of users' intent and early intervention simultaneously.
KW - Concurrent page adaptation
KW - Hidden Markov models
KW - Hierarchical Bayes models
KW - Optimization
KW - Real-time learning
KW - Shopping intent
KW - Website productivity
UR - http://www.scopus.com/inward/record.url?scp=84959188351&partnerID=8YFLogxK
U2 - 10.1287/isre.2015.0568
DO - 10.1287/isre.2015.0568
M3 - Article
AN - SCOPUS:84959188351
SN - 1047-7047
VL - 26
SP - 339
EP - 359
JO - Information Systems Research
JF - Information Systems Research
IS - 2
ER -