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CATEGORIES:Seminar
DESCRIPTION:We study the shopping patterns of online customers and how a se
 ries of store visits lead to purchase conversions. Toward this end\, we dev
 elop a dynamic timing model that explicitly captures the lumpy patterns of 
 online store visits. Our model is based on the notion that the arrival proc
 ess of customer visits consists of multiple visit clusters with relatively 
 short intervisit times within a cluster and a longer intervisit time betwee
 n clusters. Because the start and the end of each visit cluster are unobser
 ved\, we employ a changepoint modeling framework and statistically infer th
 e cluster formation on the basis of customer visit patterns through data au
 gmentation in Bayesian approach. Our model provides a set of novel inferenc
 es about the patterns underlying online shopping behavior. In our empirical
  analysis\, we find strong empirical evidence of lumpy shopping patterns by
  online customers with significant heterogeneity in the extent of the lumpi
 ness. As part of our substantive contribution\, we highlight how the cluste
 red-based inferences of store visit patterns can be leveraged to examine co
 nversion behavior of online customers.
DTEND:20111108T221500Z
DTSTAMP:20260416T112642Z
DTSTART:20111108T211500Z
GEO:42.443451;-76.481506
LOCATION:Frank H. T. Rhodes Hall\, 253
SEQUENCE:0
SUMMARY:ORIE Colloquium: Modeling Online Visitation and Conversion Dynamics
UID:tag:localist.com\,2008:EventInstance_176523
URL:https://events.cornell.edu/event/orie_colloquium_modeling_online_visita
 tion_and_conversion_dynamics
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