This is the second installment in a series exploring the principles, process and unparalleled power of REVENUE SCIENCE
Welcome back to Revenue Science 101. If you missed the first day of class, you may catch up here. Today, we will dig deeper into some of the foundational concepts of the discipline as they relate to demand-based forecasting.
Whether you sell hotel rooms or parking spaces, confidence in your forecast, and ultimately your pricing decisions, begins with a solid understanding of available demand in your market. This starts by applying a scientifically sound approach to assessing your unconstrained demand—demand that is not constrained by the capacity or restrictions of your business and could be sold if your property had an unlimited number of assets available.
For example, picture a 100-room hotel or 100-space parking facility with demand for approximately 100 rooms/spaces on a particular date. Okay, straightforward enough. Charge a fair rate, and you should be fine. Now think of the same property, only there is demand for around 1,000 units. How you optimize your business between these two circumstances may require very different actions.
Ultimately, in both situations, you may reach full occupancy. But the true demand is very different. In the latter case, when the demand is 1,000, you can afford to yield more aggressively, charge higher rates, and still fill up your property.
Find True Demand
“The temptation to form premature theories upon insufficient data is the bane of our profession.” – Sherlock Holmes
Any historical data you reference after the date in question has passed will be influenced by many factors, such as capacity and pricing decisions. It’s important to understand what the demand would have been, had it not been influenced by these constraining factors. This allows us to know the true demand so we can really optimize profits by understanding the demand in the market—instead of looking at constrained demand.
I am sometimes asked why IDeaS doesn’t use lost business data to determine unconstrained demand. Perhaps a few decades ago, when demand sources were simple with few channels of distribution, you could have effectively kept track of regrets and denials. But it’s difficult to track this type of data cleanly and accurately in today’s complex online environment, especially when you don’t know why a person isn’t willing to pay a particular rate. A regret no longer occurs simply because of price but because of a combination of many factors like location, reputation, and online content.
Using lost business data is flawed, adds a lot of bias due to the small sampling of data a business can control, and ultimately may not produce an accurate estimate of demand. IDeaS, therefore, uses scientific methods to understand true demand, and this has consistently proven to have the highest reliability due to the integrity of the data utilized.
Account for Uncertainty
“As far as the laws of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality.” – Albert Einstein
Demand has a lot of uncertainty. You can think of it as a signal, which is a forecastable part to which noise has been added. The job of forecasting is to isolate the signal and use it to produce a forecast. But what remains, after you have taken the signal out, is random noise. Therefore, you must understand that for whatever forecast you produce, you will be uncertain about it and must account for the random noise.
So, how can IDeaS’ revenue science solutions give you greater confidence in your forecast?
- By avoiding inaccurate or “noisy” data that dilutes the reliability of a demand forecast
- By developing machine-learning, decision-based systems that adapt quickly to changes in your business or the market to continuously improve forecast performance
- By harnessing the power of hundreds of SAS analytics models, finely tuned for specific business scenarios
- By predicting demand by incorporating historic and future data, competitor pricing, and forward-looking market demand intelligence
- By considering demand and how it varies due to pace, season, day of week, year-over-year trends, shift, length of stay and asset type
- By understanding the unique relationships between properties, market segments, and their booking patterns while accounting for uncertainty or volatility in the market
- And by integrating pricing and market demand intelligence data directly into demand forecasts and strategic decisions to optimize revenue performance
Until next time, class dismissed.
Dr. Ravi Mehrotra is the president, founder & chief scientist of IDeaS Revenue Solutions. Through the establishment of IDeaS in 1989, Dr. Mehrotra pioneered the “Opportunity Cost” approach that later became the industry standard for dealing with the complexities of the network or length of stay effects in revenue management.
Ravi’s research and founding involvement in IDeaS is a natural progression of his scientific background. As an assistant professor at North Carolina State University, he invented new models for parallel computing; designed and analyzed both asynchronous and randomized algorithms for distributed processing; and reviewed many proposals for key government scientific agencies. At Texas Instruments, Ravi played an integral role in the development of a real-time scheduler for a complex manufacturing company. As a scientist in the Decision and Technology Lab of Andersen Consulting, Ravi was instrumental in the development and implementation of a fleet planning, scheduling and load consolidation system for a major household goods transportation company. Additionally, Ravi co-authored and holds more than one dozen patents.
Today, Dr. Mehrotra remains an active and hands-on chief scientist at IDeaS. He continues to research increasingly sophisticated methods for dynamic pricing that optimize expected profits over longer time horizons, and is a widely-recognized leader in the field of predictive analytics, forecasting and dynamic price optimization.