Optimum Dynamic Pricing has Application Beyond the Airlines Industry


Have you ever asked the person sitting beside you on an airplane what airfare he or she paid? If you have, it‘s likely you learned that your neighbor‘s ticket price was different from yours.


We‘re all familiar with price differences for tickets by type of seat (business vs. economy) and even by flexibility of ticket (refundable vs. non-refundable). Less well known, however, is the fact that ticket prices for similar seats on the same flight can vary every day that tickets for that flight are sold. It‘s all due to a pricing technique called Optimum Dynamic Pricing, and the concept has application well beyond the airlines.




Price Follows Demand


Optimum Dynamic Pricing was developed by American Airlines in the 1980‘s to address the issue of seat pricing. American Airlines’ management realized that revenues would be maximized if they could sell every seat on a plane, regardless of the actual price charged for the last seat sold. The problem they faced was that demand for airplane seats varies for every flight, every day. Charging a fixed price per seat cannot provide enough flexibility to ensure that all seats on an airplane will be sold. American solved the problem by developing a computer model that assesses the demand for seats by flight on a daily basis and adjusts price accordingly.


Here‘s how it works:


At the outset, each airplane is assigned several fare classes, such as business, economy, and discount. The number of seats in each class is fixed. From the day the flight is first available for reservations, the number of seats sold is compared with a forecast of demand for that flight. If the number of seats sold is below forecast, the program will adjust seat prices downward to encourage more sales. If it is above forecast demand, prices remain unchanged. Although prices could be increased when seats on a given flight are selling more quickly than forecast, the rationale used is that the lower priced seats are always the ones sold first and, therefore, those buying later will automatically be pushed to a higher priced seat. Individual pricing decisions are handled entirely by computer, with algorithms that have been developed solely for this purpose. Data input is provided by the company‘s reservation system.


Profit Maximization at American & Elsewhere


American Airlines


The impact of this type of pricing on profits was substantial: American Airlines estimated that the benefits over a three-year period exceeded $1.4 billion. Since it was introduced, Optimum Dynamic Pricing has become a standard practice in the airline industry, largely because of its impact on profits and competitiveness.


The principle of Optimum Dynamic Pricing has application beyond airfares. In the hotel industry, for example, room prices vary according to demand, vacancy rates, and type of customer. A last minute customer without a reservation typically pays a higher price for a room than someone with a reservation – especially if the hotel‘s vacancy rate is low.


Likewise, the same methodology has been effectively applied in the self-storage industry to adjust prices based on vacancy rates and forecast demand. In each of these cases, the same principle applies: the incremental value of selling a service that would otherwise not be sold is always positive, regardless of the price charged.


Furthermore, and perhaps most importantly, customers are willing to accept that prices vary according to some ground rules, and those who are price sensitive are able to adjust their buying behavior accordingly. The same approach can also be applied to electronic commerce, where sales levels can be tracked and prices changed on a daily basis. For example, a bookseller might establish sales targets for a particular title and, depending on sales, adjust the price accordingly. If sales fail to reach the expected volume within a certain period the price can be gradually lowered. This approach could well have a better impact on profitability than waiting until the book has been on the market for some time and then trying to dump excess stock at bargain prices.

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