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by Alejandro Avalos Mar

Have you ever noticed how flight prices get higher as the departure date approaches? This seems fairly intuitive: more seats are being booked, so the airline is charging more per seat as the number of available seats decreases. But sometimes you buy an expensive ticket a couple of weeks before departure, and, surprise surprise, the flight is not even half full! Sigh… that doesn’t make sense, does it?

In this blog post, I will explain possible reasons why this situation is common in the airline and hospitality industries, and illustrate common strategies that can be used for other pricing decisions.

Background

The main objective of revenue management (RM) is to maximize a company’s revenue. Organizations use RM techniques, including forecasting models, consumer behavior analysis, and optimization, to find prices that accomplish this goal.

This discipline is generally associated with the airline industry, but it has quite a few other real-world applications: hotels, stadiums, cruises, etc. Revenue management is particularly prevalent in industries/cases where you have a fixed amount of inventory that can only be sold up until a particular deadline, after which the inventory becomes worthless. For example, airlines schedule flights at least a year in advance, and (in theory) these flights will depart whether they’re full or not. Since a company’s main goal is to maximize revenue, it will employ specific strategies to price its inventory under these conditions.

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So why did you pay so much money for your ticket if your flight isn’t even full? Could the airline have charged $180 instead of $250, and consequently have filled 80% of the plane? What if they had charged $150 and filled 100% of the plane?

Let’s examine these scenarios for a plane with a capacity of 200 passengers:

(50% of 200 seats) * $250: $25,000

(80% of 200 seats) * $180: $28,800

(100% of 200 seats) * $150: $30,000

In this situation, it appears that filling the plane to capacity at that price results in the greatest revenue. Additionally, more passengers translates to more on-board purchases, which means even more opportunity for profit.

So why don’t airlines do this? Well, the above scenario doesn’t tell the full story.

Why?

In the airline industry, there are mainly two types of flyers: business travelers and leisure travelers. These two groups often behave very differently, both in terms of when they book and their willingness to pay. Business travelers tend to book their flights closer to their dates of departure, and they tend not to shop for cheaper alternatives because they can just expense their fares to their companies. They put a higher value on reduced total travel time and increased general convenience.

Leisure travelers tend to be more patient. They book flights further in advance due to the increased planning required (requesting vacation time, booking a hotel, etc.), and they look for better prices since they have to bear the cost.

In addition to the different booking behavior (i.e. demand), these two types of travelers also differ on their price sensitivity, or how they react to price changes. The measurement of price sensitivity is referred to as elasticity. In our example, elasticity gives us pairs of values for the following statement:

“If the airfare for a flight increases/decreases by $X, then the demand for that flight will decrease/increase by Y bookings.”

Or, slightly more formally:

Price Elasticity of Demand = % Change in Demand / % Change in Price

From the previous example, if the price of airfare falls from $250 to $180 (28% price drop), and the demand rises from 100 passengers to 160 (a 60% increase), then the elasticity would be approximately 2.14.

If you know a good’s elasticity, you can extrapolate (with caution) and calculate the predicted change in demand resulting from a price change. Here, if the price increases from $250 to $300, based on the previous elasticity of 2.14, the demand should decrease by about 43%. However, not all elasticities are linear: a 10% price from $100 to $90 is likely to have a greater impact on demand than a 10% drop from $1 to $0.90.

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Elasticity is often helpful in revenue management because it helps distinguish between customer groups. Generally speaking, business travelers are fairly inelastic: changes in airfare price tend not to affect their demand. By contrast, leisure travelers’ demand is more elastic due to their increased price sensitivity. However, when the targeted flight is close to departure, both travelers are inelastic:

Leisure travelers are less likely to procure the requisite vacation time, book hotels at an acceptable rate, find someone to watch the kids/pets, and take care of all the other related logistics on short notice. As a result, there’s limited remaining demand to be captured in this category.

Business travelers, however, lack the constraints mentioned above. They know they want to go home on a specific date, and after a day of work at the client’s office, that one is the only flight that suits their needs.

Now let’s tie the demand and price elasticity explanations to our example: Let’s say today is Monday, we’re looking at a flight that departs on Thursday night, and the airline knows that the flight is 40% full right now. Should the airline revenue managers charge $20 dollars per ticket and fill the plane? Could they charge $30 and still fill the plane? Charging $50 might fill the plane to 98% capacity, which would surely be better than a plane full of $20 or $40 fares.

Let’s make some more assumptions to make the example a bit easier:

There’s only one fare type available (all economy).

Our target leisure travelers are young couples.

Our target business travelers are young consultants.

What should the airline do in this situation? Let’s analyze the different scenarios:

1. Airline keeps the same airfare ($250).

2. Airline decreases the airfare ($200).

3. Airline increases the airfare ($300).

Keeping the price static is the base case. Leisure demand: 0, business demand: 10.

Remaining Expected Revenue: 10*$250 = $2,500

Decreasing the airfare doesn’t make much sense here: lowering the price won’t garner leisure demand, and business demand is price inelastic. Leisure demand: 0, business demand: 10.

Remaining Expected Revenue: 10*$250 = $2,500

Increasing the airfare therefore makes the most sense here, as it won’t affect the business demand (as long as the price changes are reasonable). Leisure demand: 0, business demand: 10.

Remaining Expected Revenue: 10*$300 = $3,000

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Optimization

A revenue manager could run the above analysis and come to a conclusion about which scenario would bring the most revenue to the company. However, there are thousands of combinations of demands, price sensitivities, and forecasts that could be tested. Here is where optimization and the field of operations research (OR) can help.

The optimization algorithm could examine these permutations in an attempt to answer some fundamental questions like:

How much can airlines increase prices before they start losing demand?

Could losing demand be worth it?

If the airline can increase the airfare price by $100 and lose only one passenger, then in certain situations they can make more money than if they filled the plane. For example:

$250 * 10 passengers: $2,500

$350 * 9 passengers: $3,150

In real-world scenarios, other variables are also considered that could influence pricing decisions:

On-board revenue — How much “extra” revenue a passenger can provide once they are on board? Business travelers might get a beer or two and expense it, while leisure travelers may simply order water.

— How much “extra” revenue a passenger can provide once they are on board? Business travelers might get a beer or two and expense it, while leisure travelers may simply order water. Competition — How close does the company want to keep its prices to competitors’ fares? For example, if the competition’s price is higher than yours, then there may be an opportunity to raise fares and only minimally affect demand.

Keeping track of all of these factors is a very hard task for revenue managers, and optimization can help find the best combination.

Conclusion

Revenue management has come a long way from its inception. The field is now able to use advanced analytics and optimization techniques to find the price point that will maximize revenue for a particular period of time. It’s a combination of customer segmentation, forecasting, and elasticity estimation, all feeding an optimization algorithm to identify the best possible price.

Some important takeaways:

Understand your customer groups and their sensitivity to price. This seems difficult, but it can be done. The rewards can be immediate and significant. Think about other factors that could impact a customer’s potential revenue. Maybe you’re under pressure to move your price targets up or down based on competition or other external factors. Maybe there are downstream revenue sources that differ based on customer segments. Taking these factors into consideration helps with pricing.

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