Consumer spending accounts for a large fraction of the US economic activity. Increasingly, consumer activity is moving to the web, where digital traces of shopping and purchases provide valuable data about consumer behavior. We analyze these data extracted from emails and combine them with demographic information to characterize, model, and predict consumer behavior. Breaking down purchasing by age and gender, we find that the amount of money spent on online purchases grows sharply with age, peaking in late 30s. Men are more frequent online purchasers and spend more money when compared to women. Linking online shopping to income, we find that shoppers from more affluent areas purchase more expensive items and buy them more frequently, resulting in significantly more money spent on online purchases. We also look at dynamics of purchasing behavior and observe daily and weekly cycles in purchasing behavior, similarly to other online activities. More specifically, we observe temporal patterns in purchasing behavior suggesting shoppers have finite budgets: the more expensive an item, the longer the shopper waits since the last purchase to buy it. We also observe that shoppers who email each other purchase more similar items than socially unconnected shoppers, and this effect is particularly evident among women. Finally, we build a model to predict when shoppers will make a purchase and how much will spend on it. We find that temporal features improve prediction accuracy over competitive baselines. A better understanding of consumer behavior can help improve marketing efforts and make online shopping more pleasant and efficient.
in offline stores have been extensively investigated as they have direct consequences on the revenue potential of retailers and advertisers. Survey-based studies attempted to isolate the factors that lead a customer to buy an item or, in other words, to understand what the strongest predictors of a purchase are. Although the mere amount of online activity of a customer can predict to some extent the occurrence of a future purchase @cite_1 , multifaceted predictive models have been proposed in the past. Features related to the phase of information gathering (access to search features, prior trust of the website) and to the purchase potential (monetary resources, product value) can often predict whether a given item will be purchased or not @cite_0 @cite_16 .
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This paper tests the ability of two consumer theories-the theory of reasoned action and the theory of planned behavior-in predicting consumer online grocery buying intention. In addition, a comparison of the two theories is conducted. Data were collected from two web-based surveys of Danish (n=1222) and Swedish (n=1038) consumers using self-administered questionnaires. These results suggest that the theory of planned behavior (with the inclusion of a path from subjective norm to attitude) provides the best fit to the data and explains the highest proportion of variation in online grocery buying intention. This paper extends Ajzen's (1991) theory of planned behavior (TPB) to explain and predict the process of e-commerce adoption by consumers. The process is captured through two online consumer behaviors: (1) getting information and (2) purchasing a product from a Web vendor. First, we simultaneously model the association between these two contingent online behaviors and their respective intentions by appealing to consumer behavior theories and the theory of implementation intentions, respectively. Second, following TPB, we derive for each behavior its intention, attitude, subjective norm, and perceived behavioral control (PBC). Third, we elicit and test a comprehensive set of salient beliefs for each behavior. A longitudinal study with online consumers supports the proposed e-commerce adoption model, validating the predictive power of TPB and the proposed conceptualization of PBC as a higher-order factor formed by self-efficacy and controllability. Our findings stress the importance of trust and technology adoption variables (perceived usefulness and ease of use) as salient beliefs for predicting e-commerce adoption, justifying the integration of trust and technology adoption variables within the TPB framework. In addition, technological characteristics (download delay, Website navigability, and information protection), consumer skills, time and monetary resources, and product characteristics (product diagnosticity and product value) add to the explanatory and predictive power of our model. Implications for Information Systems, e-commerce, TPB, and the study of trust are discussed. Consumers worldwide can shop online 24 hours a day, seven days a week, 365 days a year. Some market sectors, including insurance, financial services, computer hardware and software, travel, books, music, video, flowers, and automobiles, are experiencing rapid growth in online sales. For example, in Jan. 1999, Dell Computer Corp. was selling an average of @math 294 billion by 2002, online retailing raises many questions about how to market on the Net.