Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior. Kooti, F., Lerman, K., Aiello, L. M., Grbovic, M., Djuric, N., & Radosavljevic, V. In The 9th ACM International Conference on Web Search and Data Mining, 2016.
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior [link]Paper  abstract   bibtex   
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.
@INPROCEEDINGS{Kooti16wsdm,
  author =       {Farshad Kooti and Kristina Lerman and Luca Maria Aiello and Mihajlo Grbovic and Nemanja Djuric and Vladan Radosavljevic},
  title =        {Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior},
  booktitle =    {The 9th ACM International Conference on Web Search and Data Mining},
  year =         {2016},
  keywords =     {social-dynamics},
  abstract = {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.},
url={http://arxiv.org/abs/1512.04912},
}
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