To Buy or Not to Buy: Mining Airfare Data to Minimize Ticket Purchase Price

Speaker: Edmond WU

Abstract

As product prices become increasingly available on the World Wide Web, consumers attempt to understand how corporations vary these prices over time. However, corporations change prices based on proprietary algorithms and hidden variables (e.g., the number of unsold seats on a flight). Is it possible to develop data mining techniques that will enable consumers to predict price changes under these conditions?

The authors of this paper report on a pilot study in the domain of airline ticket prices where the authors recorded over 12,000 price observations over a 41 day period. When trained on this data, Hamlet--the multi-strategy data mining algorithm which the authors propose, generated a predictive model that saved 341 simulated passengers $198,074 by advising them when to buy and when to postpone ticket purchases. Remarkably, a clairvoyant algorithm with complete knowledge of future prices could save at most $320,572 in the simulation, thus Hamlet's savings were 61.8% of optimal. The algorithm's savings of $198,074 represents an average savings of 23.8% for the 341 passengers for whom savings are possible. Overall, Hamlet saved 4.4% of the ticket price averaged over the entire set of 4,488 simulated passengers. The pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum.

Referred Papers