Estimating consumer demand from high-frequency data

Price discrimination offers sellers the possibility of increasing revenue by capturing a market’s consumer surplus: arising from the low price elasticity segment of customers that would have been prepared to pay more than the current market price. First degree price discrimination requires a seller to know the maximum (reserve) price that each consumer is willing to pay. Discouragingly, this information is often unavailable; making the theoretical ideal a practical impossibility.

Electronic commerce offers a solution; with loyalty cards, transaction statements and online accounts all providing channels of customer monitoring. The vast amount of data generated—eBay alone produces terabytes daily—creates an invaluable repository of information that, if used intelligently, enables consumer behaviour to be modelled and predicted. Thus, once behavioural models are calibrated, price discrimination can be tailored to the level of the individual.

Here, we introduce a statistical method designed to model the behaviour of bidders on eBay to estimate demand functions for individual item classes. Using eBay’s temporal bidding data—arrival times, price, user id—the model generates estimates of individual reserve prices for market participants; including hidden, or censored, demand not directly contained within the underlying data. Market demand is then estimated, enabling eBay power sellers—large professional bulk-sellers—to optimize sales and increase revenue. Proprietary software automates this process: analyzing data; modelling behaviour; estimating demand; and generating sales strategy.

This work is a tentative first step of a wider, ongoing, research program to discover a practical methodology for automatically calibrating models of consumers from large-scale high-frequency data. Multi-agent systems and artificial intelligence offer principled approaches to the modelling of complex interactions between multiple individuals. The goal is to dynamically model market interactions using realistic models of individual consumers. Such models offer greater flexibility and insight than static, historical, data analysis alone.