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

As product prices become increasingly available on the World Wide Web, consumer 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? This paper reports on a pilot study in the domain of airline ticket prices where we recorded over 12,000 price observations over a 41 day period. When trained on this data, Hamlet - our multi-strategy data mining algorithm - generated a predictive model that saved 607 simulated passengers 283,904 dollars 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 dollars in our simulation, thus Hamlets savings were 88.6 percent of optimal. The algorithms savings of 283,904 dollars represents an average savings of 27.1 percent per simulated passenger for whom savings are possible. Our pilot study suggests that mining of price data available over the web has the potential to save consumers substantial sums of money per annum, at least until corporations begin to fight back.