Demystifying Revenue Management - Three most common myths

Given its widespread adoption, revenue management (RM) approach is now an industry benchmark in airline, hospitality, and car rental industries for pricing. Over the past decade, revenue management approaches have gained popularity in other industries, most notably electricity (for dynamic pricing in conjunction with adoption of smart grids and smart meters), sports tickets (for pricing of both seasonal passes and individual game tickets), restaurant (for dynamic menu pricing to adjust for demand variations), and digital advertisements (for dynamic pricing of digital advertisement space in alignment with the demand across advertisers).

While there is a consensus on the potential offered by revenue management (RM) in achieving revenue goals, however in my interactions with business leaders across industries I have encountered few myths associated with RM approach. The three most common of these are:

• Myth 1: Revenue management increases the average price paid by customers 
• Myth 2: Revenue management is useful only in competitive markets 
• Myth 3: Revenue management systems reduce the significance of the role of the revenue managers

 In this and the next couple of articles, I will address these myths and separate facts from friction, so that business leaders can put their concerns at ease and make the best of the opportunities offered by RM.

Myth 1: Revenue management increases the average price paid by customers

Whenever the available inventory (of airline seats, hotel rooms, etc.) is greater than the demand at high price, revenue management will allocate a portion of the inventory for selling at low price. This maximizes the capacity utilization, and also brings down the average price paid by customers.

Revenue management dynamically allocates the available inventory for selling at different price points. As a result, if the future demand at high price points is lower than the inventory available, RM will allocate a part of the inventory for selling at lower price points, which will pull down the average price paid per customer.

For illustration, let’s consider an airline ticket pricing scenario for a flight on July 2^nd, 2015 from NYC to SFO, with 100 seats in the aircraft, which can be sold at the following four price points: $360, $440, $520, and $600. Assume that the airline starts accepting reservations from one year in advance, and the demand is uniform over the one year booking horizon. The total demand forecast over the year and the corresponding revenues at each of the price points in consideration are shown in Figure 1 below.

(Capacity = 100)

 

If the airline adopts an optimal single price policy for the July 2^nd flight, it will pick $440 because at this price its revenues are maximized at $37,840. However, notice that only 86% of the flight capacity is utilized at this price. If it prices at $360, it will fill-up all the seats, however it will generate only $36,000 in revenues.

If the airline adopts a simplistic revenue management approach, and for the first 237 days in the booking horizon (from July 2, 2014 to Feb 25, 2015) sells July 2^nd, 2015 flight tickets at $360, and for the balance 128 days (from Feb 26, 2015 to July 1, 2015) sells the tickets at $600, it can expect to sell 78 (=237/365*120) seats at $360, and 21 (=128/365*60) seats at $600. In this manner, for the July 2^nd, 2015 flight the airline can expect to generate revenues of $40,680 ($360*78 + $600*21).

In comparison to the optimal single price policy ($440), the RM approach here has contributed 7.5% higher revenues ($40,680 vs. $37,840), lead to a better flight capacity utilization (99% vs. 86%), and lowered the average price paid per customer from $440 to $411 (= $40,680 / 99).

Truly a win-win proposition!!