Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior Farshad Kooti* Kristina Lerman* Luca Aiello † Mihajlo Grbovic † Nemanja Djuric † Vladan Radosavljevic † *USC ISI † Yahoo! Labs 1

Online Shopping is on the Rise $305B spent online in 2014, 15% increase from [Source: Census data] Online purchases as a percentage of total sales

Related work Gender plays a significant role in offline shopping [Dholakia’99, Hayhoe’00]. Online shoppers are younger, wealthier, and more educated [Swinyard’11, Farag’07]. Prediction of online purchases has been conducted mostly using activity logs [Linden’03]. 3

Online Shopping Extracted data from confirmations Users who opted-in for research studies Feb-Sep M users 121M purchases 5.1B dollars Demographics –age, gender, zip code 4

Factors: Demographic, Temporal, Social Predicting Purchases 5

Who are the online shoppers? 6 % of online shoppers: men: 30% women:39%

Who spends more money? 7 Median: men: $15 women: $13

Role of Income 8 Amount of money spent online is highly correlated with income.

Daily and Weekly Patterns 9 More purchases happen early afternoons and first days of the week.

Limited Budget 10 $$ $$$ $ $$

Limited Budget 11 5 Purchases

Role of Social Interactions 12 Are pairs of users interacted with each other more similar than a random pair? Cosine similarity of vectors of categories of the purchases. 12 PairsAverage cosine similarity Friends0.188 (± ) Random0.145(± ) Woman-woman friends0.192(± ) Man-man friends0.186(± ) Woman-man friends0.182(± )

Prediction 13 Predicting –Price of the next item purchased (5 buckets: <$6; <$12; <$20; <$40; $40+) –Time of the next purchase (5 buckets: <1 day; <5 days; <14 days; <33 days; 33 days+) 37 features motivated by our analysis –Previous demographics, purchase price history, etc. 75/25 temporal split Bayesian Network Classification

Prediction Results 14 Item pricePurchase time Majority vote20.7%22.8% Previous purchase 29.3%24.9% Most used29.8%22.2% Our Classifier31.0% (+50%)31.1% (+36%) AUC RMSE

Studying online purchase behavior using confirmations. Women are more likely to shop online. Men make more expensive purchases. People wait longer for a more expensive purchase. Role of income and homophily/social influence is considerable. Prediction of time and price of purchases. 15 Summary

Thanks! 16

Motivation Actual purchases are recorded, unlike click and browsing. Includes all purchases from different platforms. Detailed large-scale data with demographics. Social interactions between shoppers. 17

Who makes more purchases? 18 Median: men: 6.1 women: 5.8

Who makes more expensive purchases? 19 Median: men: $36 women: $31

Role of Income 20

Limited Budget 21 5 Purchases 9-11 Purchases Purchases

Factors: Demographic, Temporal, Social Predicting Purchases 22

Top Features 23 Item price Most used class earlier Number of <$6 purchases Median price of earlier purchases Purchase time Number of earlier purchases Median time between purchases Time since the first purchase

Future Work Price variations of items over time Analyzing and predicting the returns Improving predictions 24