Approximating Purchase Propensities and Reservation Prices from Broad Consumer Tracking
44 Pages Posted: 17 Jan 2020
Date Written: November 5, 2018
A consumer’s web-browsing history, now readily available, may be much more useful than demographics for both behaviorally targeting advertisements and personalizing prices. Using a method that combines economic modeling and powerful machine learning techniques, I ﬁnd a striking diﬀerence. Using demographics yields purchase probabilities at observed prices ranging across individuals from about 8% to about 30%. Adding consumers’ web-browsing histories increases this range to about 5% to 90%, allowing more precise behavioral targeting. I further ﬁnd that personalizing prices based on web browsing histories increases proﬁts by 12.99% and results in some consumers paying substantially more than others for the same product. Using only demographics to personalize prices raises proﬁts by only 0.25%, suggesting the percent proﬁt gain from personalized pricing has increased 50-fold.
Keywords: First-Degree Price Discrimination, Price Discrimination, Big Data, Behavioral Targeting, Personalized Pricing, Algorithmic Pricing
JEL Classification: D42, L130
Suggested Citation: Suggested Citation