Yield management

 ( Business Models ) 

Similarities that exist between the airline and telecom industries include a large sunk cost combined with low marginal cost, perishable inventory, reservations, pricing flexibility and the opportunity to upsell. Differences that present challenges for communications service providers include low-value transactions and overall network complexity. Suggested approaches to executing a successful yield management strategy include accurate network information collection, bandwidth capacity allocation that does not impact service quality, the deployment of service management software such as real time policy and real-time charging, and using new marketing channels to target consumers with innovative services.

In practice, the segmentation approach relies on adequate fences between consumers so that everyone doesn't buy at the lowest price offered. The airlines use time of purchase to create this segmentation, with later booking customers paying the higher fares. The Fashion uses time in the opposite direction, discounting later in the selling season once the item is out of fashion or inappropriate for the time of year. Other approaches to fences involve attributes that create substantial value to the consumer at little or no cost to the seller. A backstage pass at a concert is a good example of this. Initially yield management avoided the complexity caused by the interaction of absolute price and price position by using surrogates for price such as booking class. By the mid-1990s, most implementation incorporated some measures of price elasticity. The airlines were exceptional in this case, preferring to focus on more detailed segmentation by implementing O&D (Origin & Destination) systems.

At the heart of yield management decision-making process is the trade-off of marginal yields from segments that are competing for the same inventory. In capacity-constrained cases, there is a bird-in-the-hand decision that forces the seller to reject lower revenue generating customers in the hopes that the inventory can be sold in a higher valued segment. The tradeoff is sometimes mistakenly identified as occurring at the intersection of the marginal revenue curves for the competing segments. While this is accurate when it supports marketing decisions where access to both segments is equivalent, it is wrong for inventory control decisions. In these cases the intersection of the marginal revenue curve of the higher valued segment with the actual value of the lower segment is the point of interest.

In the case illustrated here, a car rental company must set up protection levels for its higher valued segments. By estimating where the marginal revenue curve of the luxury segment crosses the actual rental value of the midsize car segment the company can decide how many Luxury vehicles to make available to midsize car renters. Where the Vertical bar from this intersection point crosses the demand (horizontal) axis determines how many luxury cars should be protected for genuine luxury car renters. The need to calculate protection levels has led to a number of heuristic solutions, most notable EMSRa and EMSRb, which stands for Expected Marginal Seat Revenue version a and b respectively. The balancing point of interest is found using Littlewood's rule which states that demand for $R\_2$ should be accepted as long as

$R$₂ $\backslash ge\; R$₁$*\; Prob(\; D$₁$>x\; )$

where
$R\_2$ is the value of the lower valued segment
$R\_1$ is the value of the higher valued segment
$D\_1$ is the demand for the higher valued segment and
$x$ is the capacity left

This equation is re-arranged to compute protection levels as follows:

$y$₁$=\; Prob$⁻¹$(\; R$₂$/R$₁$)$

In words, the seller wants to protect $y$₁ units of inventory for the higher valued segment where $y$₁ is equal to the inverse probability of demand of the revenue ratio of the lower valued segment to the higher valued segment. This equation defines the EMSRa algorithm which handles the two segment case. EMSRb is smarter and handles multiple segments by comparing the revenue of the lower segment to a demand Weighted mean of the revenues of the higher segments. Neither of these heuristics produces the exact right answer and increasingly implementations make use of Monte Carlo simulation to find optimal protection levels.

Since the mid-1990s, increasingly sophisticated mathematical models have been developed such as the dynamic programming formulation pioneered by Talluri and Van RyzinTalluri, K. T. and G. J. Van Ryzin (2001). "Revenue management under a general discrete choice model of consumer behavior." Management science which has led to more accurate estimates of bid prices. Bid prices represent the minimum price a seller should accept for a single piece of inventory and are popular Control system for Hotels and Car Rental firms. Models derived from developments in financial engineering are intriguing but have been unstable and difficult to place the parameters in practice. Yield management tends to focus on environments that are less rational than the financial markets.

Enterprises that use yield management periodically review transactions for goods or services already supplied and for goods or services to be supplied in the future. They may also review information (including statistics) about events (known future events such as holidays, or unexpected past events such as terrorist attacks), competitive information (including prices), seasonal patterns, and other pertinent factors that affect sales. The models attempt to forecast total demand for all products/services they provide, by market segment and price point. Since total demand normally exceeds what the particular firm can produce in that period, the models attempt to optimize the firm's outputs to maximize revenue.

The optimization attempts to answer the question: "Given our operating constraints, what is the best mix of products and/or services for us to produce and sell in the period, and at what prices, to generate the highest expected revenue?"

Optimization can help the firm adjust prices and to allocate capacity among market segments to maximize expected revenues. This can be done at different levels of detail:

• by goods (such as a seat on a flight or a seat at an opera production)
• by group of goods (such as the entire opera house or all the seats on a flight)
• by market (such as sales from Seattle and Minneapolis for a flight going Seattle-Minneapolis-Boston)
• overall (on all the routes an airline flies, or all the seats during an opera production season)

Yield management is particularly suitable when selling perishable products, i.e. goods that become unsellable at a point in time (for example air tickets just after a flight takes off). Industries that use yield management include airlines, hotels, stadiums and other venues with a fixed number of seats, and advertising. With an advance forecast of demand and pricing flexibility, buyers will self-sort based on their price sensitivity (using more power in off-peak hours or going to the theater mid-week), their demand sensitivity (must have the higher cost early morning flight or must go to the Saturday night opera) or their time of purchase (usually paying a premium for booking late).

In this way, yield management's overall aim is to provide an optimal mix of goods at a variety of price points at different points in time or for different baskets of features. The system will try to maintain a distribution of purchases over time that is balanced as well as high.

Good yield management maximizes (or at least significantly increases) revenue production for the same number of units, by taking advantage of the forecast of high demand/low demand periods, effectively shifting demand from high demand periods to low demand periods and by charging a premium for late bookings. While yield management systems tend to generate higher revenues, the revenue streams tends to arrive later in the booking horizon as more capacity is held for late sale at premium prices.

Firms faced with lack of pricing power sometimes turn to yield management as a last resort. After a year or two using yield management, many of them are surprised to discover they have actually lowered prices for the majority of their opera seats or hotel rooms or other products. That is, they offer far higher discounts more frequently for off-peak times, while raising prices only marginally for peak times, resulting in higher revenue overall.

By doing this, they have actually increased quantity demanded by selectively introducing many more price points, as they learn about and react to the diversity of interests and purchase drivers of their customers.

Some consumers may object that it is impossible for them to boycott yield management when buying some goods, such as airline tickets.

Yield management also includes many noncontroversial and more prevalent practices, such as varying prices over time to reflect demand. This level of yield management makes up the majority of yield management in the airline industry. For example, airlines may price a ticket on the Sunday after Thanksgiving at a higher fare than the Sunday a week later. Alternatively, they may make tickets more expensive when bought at the last minute than when bought six months in advance. The goal of this level of yield management is essentially trying to force demand to equal or exceed supply.

When yield management was introduced in the early 1990s, primarily in the airline industry, many suggested that despite the obvious immediate increase in revenues, it might harm customer satisfaction and loyalty, interfere with relationship marketing, and drive customers from firms that used yield management to firms that do not. Frequent flier programs were developed as a response to regain customer loyalty and reward frequent and High-yield debt passengers. Today, yield management is nearly universal in many industries, including airlines.

Despite optimizing revenue in theory, introduction of yield management does not always achieve that in practice because of corporate image problems. In 2002, Deutsche Bahn, the Germany national railway company, experimented with yield management for frequent BahnCard passengers.

The fixed pricing model that had existed for decades was replaced with a more demand-responsive pricing model, but that reform proved highly unpopular with passengers and led to widespread protests and a decline in passenger numbers.

• Geo (marketing)
• Variable pricing
• Price discrimination
• Algorithmic pricing
• Last minute advertising
• Institute for Operations Research and the Management Sciences
• Behavioral Operations Research
• Revenue shortfall
• Revenue management