How Cookies Can Help To Calculate Prices For Airline Tickets: [Essay Example], 2106 words GradesFixer

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How Cookies Can Help To Calculate Prices For Airline Tickets

  • Category: Traveling
  • Topic: Airline
  • Pages: 5
  • Words: 2106
  • Published: 26 April 2019
  • Downloads: 26
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In the age of the Internet, each and every website can know everything about you from just one little click. Your age, your location, your race, your income: there are an infinite number of variables that can be determined simply from your browsing history and/or past purchases. These packets of information have led to a form of price discrimination where in certain websites and/or services can now charge each consumer a different price based off how great their need is for the product. In more economic terms, producers now have an easy way to know how elastic a consumer’s demand is. No industry is this more clear than in the business of air travel. Airplanes and airliners are quite big-ticket items, they are expensive to fuel, expensive to fix and overall expensive to market and run. So for airlines to ensure it makes the most effective profit it needs to look at and diversify its consumers for each and every flight. By using cookies, click tracking technology, and location information an airline is able to most effectively calculate a price for the tastes and needs of the consumer and in their profit margin.

Elasticity is the measure of need for a product, from something as basic as bread, to big figure luxuries such as sport cars. Elasticity when looked at through the lens of goods, is called the Elasticity of Demand. In basic terms, the more needed something is, and has few or no substitutes the more inelastic the demand for the item will be. For example, let’s say we are looking at the demand and revenues for gasoline. Gasoline is an essential good, used to power any variety of machinery from mowers to cars and has no real substitute. This means that a change in price, be it up or down, will do very little to the amount demanded. Even if gas gets more expensive, the consumer will still need the same amount to fill their car to commute, etc. This means that for inelastic goods, demand and revenue are symmetrical.When the price rises, so do the profits, and this behavior follows for a drop in price and revenue as well. For goods with elastic demand, however, the opposite is true. Elastic goods tend to be easily substituted, or luxury items, and the quantity demanded is very reactive to any change in price. When the price falls for an elastic good, the demand increases substantially, and falls when prices rise. Revenue too, acts very different for elastic goods. When the price for elastic goods falls, revenue increases, with the opposite occurring when prices rise. However prices and goods do not live in a static vacuum free from other factors, and here is where Income Elasticity comes into play.

Income Elasticity dictates how the demand for a product changes based on a rise or fall in income. How each good is affected by change in income is due to all goods falling into either one of two categories: Normal goods or inferior goods. When income increases and demand increases, that means it is a normal, or higher quality good. However if a falling demand for a product correlates to a rise in income, this means it is less desirable to a wealthier market, an inferior good. These are the basic components of the elasticity of income and demand. It is these concepts that cement the foundation for how a business, in this case, an airline knows how to set prices and sell it’s product most efficiently.

How do these shifting factors of demand and income playout in real life? And how does this allow an airline company to pursue the most profit? For the sake of example let us look at two hypothetical airline travelers. Traveler A is a stereotypical 9-to-5 businessman. Based in New York City, he needs to get to Los Angeles the very next day for a last-minute board meeting. Being a mid tier executive at a prestigious firm, we also know that his income is a solid six figures. Traveler B however, is a high school English teacher who lives in a small town in New York, a train ride away from New York City. He has a sizable salary of $60,000 a year and has just gotten out on summer break. Both travelers go online to look for flights from New York city to Los Angeles. Now based off this information the website and thus the airline companies have to figure out what prices they need to set to increase the probability of making a sale. In the case of Traveler A, his demand is fairly inelastic. He needs to be at the meeting in Los Angeles the next day so the price of the plane ticket will not really make an effect on his demand. Businessmen need to get to their meetings, their demand of plane tickets does not rapidly fall or rise on price, it remains consistent due to the demands of their work. Yet this demand is still slightly elastic due to the factor of various competing airlines.

In the space of air travel, while there are a multitude of options, most business tends to congregate around 5 or 6 big firms such as Delta, or American Airlines. This means air travel is an Oligopolistic market, meaning few players control most of the air travel supply. However as I will argue further down, these big players when combined with the personal details of each consumer, allows them to charge different prices for each consumer, thus making them appear to be more like a cartel. However due to these firms still needing to compete against one another, it ensures the consumer surplus remains, as they are fighting to have their product bought over the others on the market. This means both Traveler A and B are not beholden to one price set by one manufacturer as would exist in a monopoly (or multiple colluding manufacturers in a cartel), but rather a price set by various competing firms. In layman’s terms, he does not always buy a ticket from Polar bear airlines, he has a choice to buy from Pickle, Cheetah, or a handful of other flights to his chosen destination, thus making the demand curve slightly more elastic. Now let’s look at the case of Traveler B. As a consumer from a less affluent area and a smaller income, his demand for a flight to Los Angeles is not as high as that of Traveler A. He also has less of a time constraint, having all summer to choose when to go to LA. All of these factors make his demand fairly elastic. There is not one set price he will pay and nothing else, as is true with perfect elasticity, but his demand will vastly change depending on a flight being high or low priced. Knowing both of these factors, the airline would know to market a higher priced ticket to Traveler A, and a lower priced ticket to get the sale from a consumer like Travler B. However here is where we get into the nitty gritty. How exactly does the modern airline industry know about these different consumers and their likely buying habits? The answer is simple, as I stated above, these companies are changing their pricing by gleaming information off potential consumers online.

When it comes to companies, and in this case Airlines, gathering more information on their consumers there are two main methods they use. Cookies, which are essentially Hansel and Gretel-esque bread crumbs that can tell a site where and what you have done on the web, and a consumer’s physical location. Through these past interactions and generalizations about your purchasing habits based on where you live, it’s never been easier for a company to tailor a perfect price for each and every potential consumer. This strategy is especially appealing to producers with high fixed cost such as an Airline. Airlines have very high start up, or fixed costs. To start such a businesses you need a fleet of airplanes, the space to fly them out of, as well as staff and fuel and that’s even before you think of marketing or branding. While these baseline costs do loom high, their marginal costs, the additional expenditure it takes to put one more person on a plane, is incredibly low. The price of A bag of pretzels or a can of Coke pales in comparison to the cost of an airline ticket. This means that the main goal for any airline is to cover those fixed costs, and reach the point of profit, and luring those extra sales has a minimum effect on their bottom line. They want to fill their seats and this case of high fixed cost and low marginal cost is even greater with the advent of the internet. Now changing prices does not incur barely or even any marginal cost to the producer at all. There is no poster, or physical tag that needs to be reprinted and changed. All that needs to be done, is for software to quickly scan the data, and change the numbers on the screen in the blink of an eye.

They know how to create these changes through the information gathered from cookies.This identifying information can range from hard drives, to a user’s past actions on a specific site. These packets once downloaded are able to follow users as they viewed ads, different sites and most importantly, their past purchases. So when User 303 has a robust history of visiting hawaii every christmas, it’s easier to gauge how elastic his demand is for those specific set of flights each december. This when correlated with a user’s location, which is usually gleamed through purchase of material goods, or billing can create an even more vivid picture of a consumer. Once they know a user’s Address or zip code, A company can gleam the median income of a user’s area, how likely they are to be of a specific background, as well as their lifestyle and shopping preferences. These factors are signs of differing demand, and along with internet cookies are being used to the advantage of the airline industry. Just as was shown above with hypothetical travelers A and B now two people both of whom are on the same flight, and even the same section can have wildly different prices for their otherwise identical tickets. These new invasive pricing practices were investigated in 2007 by Bill Mcgee, a journalist and author of the book Attention All Passengers: The Truth About the Airline Industry for Consumer Report. He wanted to see how this new wealth of digital data affected prices for individual users booking flights online. By looking at various flights in a handful of browsers he eventually uncovered a pattern. The greater demand of the consumer due to flight history, the more money that flew out of their pockets. When looking at two flights from NYC to Sydney he “searched a major travel site for a fare…under identical, real-time, apples-to-apples circumstances with two different browsers’ the only difference being that “one was cleared of all cookies and one had a robust history of purchasing flights.” The data went on to show that “the cleared browser offered fares ranging from $1,770 to $1,950, while the second” history ladened “browser could only find a fare of $2,116”(McGee.) This one recorded instance that shows how simply from online history alone, a website and thus an entire industry is able to manipulate prices for those with greater or lesser demand. With additional input such as income factors, and past purchase history the sky’s the limit when it comes to custom prices for consumers.

This is controlled pricing based on differing demand in action. A real world example of how elasticity, while a concept and theory on paper, truly does change the actions and events in our everyday world. Someone who has a history of certain flight patterns, or has the consumer type of the extoic travler, or budget saver, all are targeted by airlines with differing prices. By ensuring they are able to charge each seat on a flight to the highest amount possible, while ensuring a sale, they are increasing profit, producer surplus but limiting the effect of free competition in the market place. When the airline sector knows what you need, it has the ability to create deadweight loss by making sure to show you the same high prices across the board. Unless a customer is using a private browser that hides their cookies and location, these new tracking services are a blessing for Airline’s profits, and a boon for economic growth.

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