Last week I had the opportunity to attend the INFORMS Annual Meeting in San Francisco. For those of you not familiar with this organization or conference, the Institute for Operations Research and the Management Sciences (INFORMS) is the largest society in the world for professionals in the field of operations research (O.R.), management science, and analytics. Several thousand papers are presented at a typical INFORMS Annual Meeting, which is held over three to four days, and includes many separate tracks covering distinct areas of focus. The plurality of presentations at an INFORMS Annual Meeting seem to come from academic sources, but there are also papers presented by vendors and industry. This mix of paper sources makes the conference an excellent place to see where research is being focused (by academics, industry, and vendors), while at the same time gauging where the current “state of the art” is in a given area. Unfortunately, because the conference is so large, it is virtually impossible to see more than a very small slice of it, which makes getting general impressions across disciplines difficult. In my entry today I’m going to focus very specifically on Revenue Management and Pricing, as this was my focus at the event.
This year’s conference included 83 separate tracks, ranging from Aviation Applications to Service Science. It seemed that every available meeting room was taken up by the conference, and most attendees agreed that this year’s event was larger (in terms of numbers of sessions) and better attended than others in recent years. There were two (sometimes three) tracks dedicated to Revenue Management and Pricing, but several very relevant presentations on revenue management were held in other tracks, so I found myself shifting tracks quite a bit to catch those topics that interested me most.
My own presentation was held in the Practice track, and all three of the presenters in this section focused on revenue management. I presented “Revenue Management in the Big Data Era” where I focused on the industry and technology changes that are driving new challenges to revenue management, and how Big Data appears to offer the opportunity to face these challenges more successfully in the future – when that data is paired with new analytic approaches. In my presentation I referenced several studies that are showing the potential value of big data to revenue management, including Kelly McGuire and Breffni Noone’s studies on Hotel Pricing in a Social World.
The two other presenters in my practice section were Warren Lieberman from Veritec Solutions (Warren also chaired our session, which was titled “Succeeding with Revenue Management”), and Cory Canamo from Disney Cruise Line. Warren’s presentation “Models and Methods” covered a number of topics, including comparing the benefits of centralized vs. decentralized revenue management organizations, and the availability data and importance of using a range of data to drive pricing decisions – which dovetailed nicely off of my own piece. Warren then described the benefits that Veritec’s customers are seeing from a multiple signal approach. Cory’s presentation provided an overview of Disney Cruise Line, and some of the unique challenges that Disney Cruise Line faces as a niche, family-oriented cruise line. Cory also discussed some of the industry innovations that Disney Cruise Line has introduced (such as the Magical Porthole), and how customer reactions to these innovations has challenged the revenue management function at Disney Cruise Line.
This year saw a large number of papers at the INFORMS Annual Meeting, with 27 papers involving SAS authors, presenters, or topics. A number of these presentations related to revenue management and pricing, including Matt Maxwell’s two presentations “Customer Choice Model Optimization with Overlapping Consideration Sets”, and “Optimization Challenges with a Customer Choice Model”. Matt’s presentations highlighted:
- Limitations in traditional revenue management forecasting approaches - in particular the assumption of independence between demand streams
- How customer choice model approaches overcome these limitations, and
- Challenges that must be overcome to optimize revenues using customer choice models
Jason Chen’s (also from SAS) presentation “A Simulation Study of BAR by Day Heuristics” discussed the complexity in optimizing BAR (best available rate) by Day (where room prices are the same for a given date of stay for all occupants, regardless of length of stay). As Jason explained, BAR by day is attractive due to its simplicity, but it presents challenges when trying to optimize revenues while accounting for length of stay effects. Jason presented the outcomes of simulation studies used to gauge several different approaches to optimizing BAR by day pricing.
One last presentation that I thought would be interesting to regular readers here at the Analytic Hospitality Executive: “Upgrades and Upsells: Hertz vs. Hilton Models” by Guillermo Gallego from Columbia University. Upgrades and upsells is an area that has received quite a bit of attention, not only in hotels, but also in airlines (due to the increased number of seat inventory types caused by the addition of extended-legroom coach seating). Dr. Gallego’s presentation discussed the origin of upgrading / upselling (mismatching demand and inventory types) and compared several different business strategies to maximize revenue. Dr. Gallego also discussed the issue of “strategic behavior” (i.e. customers choosing not to pay for paid upsells when free upgrades are frequent), and the impact on optimization and outcomes. One of the things that struck me about this particular presentation was the value that was gained from improving inventory utilization (occupancy) when using just a simple free upgrading policy – value that I think is being lost or forgotten as hotels have begun to follow paid upgrade policies, because the value gained from the payment is simply easier to see.
Overall I had two general impressions regarding revenue management and pricing from this conference:
- Choice modeling continues to be a very hot topic amongst researchers, with at least 5 separate sessions (3-4 presentations per session) in the revenue management tracks dedicated to Choice modeling in revenue management – and a handful of other sessions outside of the official RM tracks covered this topic.
- I was also intrigued and encouraged at the large number of presentations at the conference regarding the application of game theory in revenue management. One of the general criticisms of traditional revenue management approaches has been the focus on short-term revenue / profitability, and the inability to capture the complexities of strategic decisions that need to be made in a competitive environment, where competitors respond to actions. Based on what I saw at INFORMS, researchers are clearly beginning to take a hard look at these very issues. You might want to study up on “Nash Equilibriums” before attending your next revenue management conference.
The most anticipated session that didn’t actually occur was the planned keynote by Google project leader Anthony Levandowski. Anthony was to present on Google’s work on the driverless car, but unfortunately this session was cancelled at the last minute. An audible “No!” was heard throughout the conference center when the email went out to all attendees, less than an hour prior to the event, indicating that the session was cancelled – leaving all of us with the question “If they can invent a driverless car, how come they can’t run a speaker-less keynote session?” (Full credit for this quip goes to my colleague Ed Hughes).