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.
We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective. While social interactions are critical to understanding consumer behavior, the relationship between social and commerce networks has not been explored on a large scale. We analyze Taobao, a Chinese consumer marketplace that is the world's largest e-commerce website. What sets Taobao apart from its competitors is its integrated instant messaging tool, which buyers can use to ask sellers about products or ask other buyers for advice. In our study, we focus on how an individual's commercial transactions are embedded in their social graphs. By studying triads and the directed closure process, we quantify the presence of information passing and gain insights into when different types of links form in the network. Using seller ratings and review information, we then quantify a price of trust. How much will a consumer pay for transaction with a trusted seller? We conclude by modeling this consumer choice problem: if a buyer wishes to purchase a particular product, how does (s)he decide which store to purchase it from? By analyzing the performance of various feature sets in an information retrieval setting, we demonstrate how the social graph factors into understanding consumer behavior. This document describes techniques for identifying purchase intent in social posts. In one or more implementations, a topic is received and social posts to one or more social networks that are related to the topic are collected. Then, one or more purchase intent posts expressing purchase intent towards the topic are identified from the collected social posts. In one or more implementations a purchase intent model, usable to identify social posts expressing purchase intent, is built from a training corpus of annotated social posts. This article examines how men and women differ in both their perceptions of the risks associated with shopping online and the effect of receiving a site recommendation from a friend. The first study examines how gender affects the perceptions of the probability of negative outcomes and the severity of such negative outcomes should they occur for five risks associated with buying online (i.e., credit card misuse, fraudulent sites, loss of privacy, shipping problems, and product failure). The second study examines gender differences in the effect of receiving a recommendation from a friend on perceptions of online purchase risk. The third study experimentally tests whether, compared to men, women will be more likely to increase their willingness to purchase online if they receive a site recommendation from a friend. The results suggest that, even when controlling for differences in Internet usage, women perceive a higher level of risk in online purchasing than do men. In addition, having a site recommended by a friend leads to both a greater reduction in perceived risk and a stronger increase in willingness to buy online among women than among men.