Kristina Lerman

Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

TitlePortrait of an Online Shopper: Understanding and Predicting Consumer Behavior
Publication TypeConference Paper
Year of Publication2016
AuthorsF. Kooti, K. Lerman, L. M. Aiello, M. Grbovic, N. Djuric, and V. Radosavljevic
Conference NameThe 9th ACM International Conference on Web Search and Data Mining

Consumer spending accounts for a large fraction of US economic activity. Increasingly, consumer activity is moving online, 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 purchasing down 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 compared to women. Linking online shopping to income, we find that shoppers from more affluent areas purchase somewhat 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. Similar to other online activities, we observe daily and weekly cycles in purchasing behavior. More interestingly, we observe temporal patterns in individual 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 they 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.