Sifting customers from the clickstream : behavior pattern discovery in a virtual shopping environment

While shopping online, customers\u27 needs and goals may change dynamically, based on a variety of factors such as product information and characteristics, time pressure and perceived risk. While these changes create emergent information needs, decisions about what information to present to customers are typically made before customers have visited a web site, using data such as purchase histories and logs of web pages visited. Better understanding of customer cognition and behavior as a function of various factors is needed in order to enable the right information to be presented at the right time. One approach to achieving this understanding is to develop predictions about what information to present based on inferences made from cognitively-grounded models of the customer, calibrated according to an analysis of what behaviors can be observed during the online shopping experience (e.g., clickstream produced by mouse clicks and typing). As a step in achieving this objective, this research tests hypotheses about how differences in product involvement, time pressure, and uncertainty and riskiness of choice may impact a customer\u27s search and decision strategies, time on task, and perceived risk while shopping online. It draws upon the results of prior research, as well as two pilot studies, to motivate the design of a study involving human participants making purchasing decisions in an online shopping environment. The main data sources are the think-aloud protocols and clickstreams of the participants, as well as pre- and post-experiment questionnaires. This work is expected to improve understanding of how contextual, personal and product-related factors help shape online shopping behavior, and to generate insights into the cognitive processes that inform this behavior. Future work beyond the thesis is likely to involve more formal modeling of human cognition in online shopping environments

Inside the Customer: Modeling Cognition during Online Shopping

Online marketers want to present potential customers with the right information at the right time. Decisions about what information to present are typically made before the customer has visited a web site, using data such as purchase histories and logs of web pages visited (i.e., clickstream data). An alternative approach is to develop predictions about what information to present based on inferences made from cognitive models of the customer. This research presents one approach to collecting and analyzing data that could be used to construct such models. Two studies are presented on how differences in product type may impact customer cognition and browsing behavior. The results suggest that differences in product type may lead to differences waiting time before making a purchase. Product type may also influence the types of information people consult before making a purchase

Physical, Chemical, and Sensory Properties of a Turmeric-Fortified Pineapple Juice Beverage

Beverage mixtures based on pineapple (Ananas comosus) and turmeric (Curcuma longa) juice as a ready-to-drink product were developed, and their physicochemical, nutritional, and sensory properties were evaluated. Four different concentrations of turmeric juice (5%, 10%, 15%, and 20% (v/v)) were added to pineapple juice to make turmeric-fortified pineapple (TFP) juice samples. Pineapple juice without turmeric was the control. The L*, a*, b*, titratable acidity (TA), total antioxidant capacity, and %DPPH scavenging values, as well as the concentrations of the phenolic compounds curcumin and demethoxycurcumin, were significantly increased with increasing turmeric concentration. Thirty volatile compounds were detected in the mixed juice samples with turmeric. Most of the turmeric-specific compounds, including monoterpenes, sesquiterpenes and turmerones, were detected in the TFP juice samples. While the antioxidant activity of the juice samples increased with increasing turmeric concentration, the pineapple juice fortified with 10% turmeric (10%T) had the best overall quality as determined by panelists. Greater concentrations of turmeric were associated with decreased palatability due to reduced mouthfeel and sweetness and increased aftertaste and sourness. These results suggest that the 10%T juice could be developed into a commercial functional beverage with increased overall flavor and nutritional quality