  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.
