In my post in September on Why revenue management analytics are becoming outdated, I made note of several important limitations in the traditional revenue management approach. Today, I’m going to focus on just one of these issues: price elasticity.
Why Price Elasticity is So Important
As I noted back in my 2-part entry on Revenue Management vs. Price Optimization, a number of important trends have taken hold since revenue management science was developed by the airlines:
- In the airline industry, low cost airlines introduced simplified fare structures with fewer fares and significantly reduced fencing. Today, these simplified fare structures dominate most air travel markets – effectively invalidating the assumption of demand independence made in revenue management models.
- Revenue management has been introduced into markets where strict fences on rates or fares never existed. Many hospitality and travel providers have rate or fare structures that do not contain strict fences – and so the assumption of demand independence is again problematic in applying traditional revenue management science.
- Rates have become increasingly dynamic. Revenue managers are now frequently managing the price at which rates are sold on a day to day basis.
These trends have taken hospitality and travel far afield from the expectations of the original revenue management scientists who modeled the airline revenue management problem. As a result, price sensitivity needs to be a key component of revenue management decisions – and the inclusion or exclusion has ripple effects across the operations of the system.
Demand Modeling Using Price Sensitivity
So, as our team here began working on the development of SAS Revenue Management and Price Optimization Analytics, we knew that it was critical that we model demand as a function of price. Of course, our model could not be limited to just price: we also knew that hospitality and travel demand continues to vary based on a number of other factors, including:
- Time of year
- Day of week
- Holiday and special event periods
- Remaining time prior to arrival
- Competitive effects (more on that next week)
- …and so on
Here at SAS, we have significant experience in modeling price elasticity, as a result of our work on price optimization solutions for the retail industry. There, too, it was necessary to construct demand models that captured price effects as well as these other effects. This experience was extremely beneficial in our development – especially in dealing with automating such calculations on a large scale, and dealing with a variety of different products and market conditions for different products in different geographies.
However, calculating price sensitivity of demand is particularly challenging in the context of revenue management because price is commonly being managed relative to demand – this is, after all, what revenue managers get paid to do (see chart below). So, when demand for a property is high, the prices tend to be high. When demand is high we raise prices, but since demand is high, customers are willing to pay those higher prices to have access to the inventory.
Using Price Elasticity to Optimize Rates
The primary use for price sensitivity of demand in revenue management is price optimization of rates. Using traditional revenue management methodology, typically referred to as yielding or inventory optimization, rates are made available or not based on the level of forecasted demand. In contrast, price optimization considers willingness to pay when setting prices to maximize revenue. Price optimization provides a better answer for market segments whose rates for a given stay date are managed using variable pricing (we call this “price-able”).
But, in the hospitality industry, not all market segments are price-able. Hotels make many agreements where the rate is fixed, and the only lever the revenue manager has is to allow or restrict access (we call this “yield-able”). In addition, hotels often link qualified rates (such as corporate agreements) to the best available rate (BAR) offered to transient guests (e.g., 10% off of the best available rate). The existence of these different types of rates, and the differences in how they can be sold and managed, makes for important conditions that need to be considered during optimization. For example, it is not sufficient to consider the effect of price elasticity on transient BAR alone when other rates are linked to that rate – the elasticity of the segments associated with those linked rates must be considered, as well. Modeling the elasticity of these different segments independently allows SAS Revenue Management and Price Optimization Analytics to determine whether a property is better off reducing the best available rate to stimulate additional transient demand, or whether such a reduction will lead to dilution of ADR in these other segments that the property revenue as a whole suffers.
Optimizing Prices and Availability
Because many rates are still managed by availability, it is not sufficient to optimize pricing alone – even when we consider complex rate relationships. The lever of availability is there, and optimizing those levers remains a part of hospitality revenue management. In developing SAS Revenue Management and Price Optimization Analytics we have used a hybrid optimization approach that allows us to recognize that some rates are price-able, some are price-linked, and some are yield-able, and some are a mix (e.g., price-linked and yieldable).
Due to the interconnectedness of both rates and inventory, decisions regarding availability for yieldable segments can dramatically influence the best available rate decision – and vice versa. Our hybrid approach allows optimization to optimize price and availability in an integrated manner – recognizing that price changes impact demand, and that this will impact the optimal availability decisions. Similarly, the model recognizes that, when there is a sufficient supply of high-ADR, yieldable demand it may be beneficial to raise the transient rates for the remaining rooms.