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Getting Comp Sets Right for the Digital Market

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July 12, 2019
Revenue Optimization
Cindy Green

For an industry where RevPAR index influences everything from stock prices to hotel performance tests to bonuses for management, you’d think we’d spend a lot of time defining the underlying competitive set we compare our hotels against. There’s no lack of concern about it. The topic certainly gets a great deal of air time in boardrooms and industry conferences, but this hasn’t yet translated to changes in the approach.

An Historical Perspective
The original competitive set model created in the early 1990s was suitable for the data and technology of the era. “When we established the concept of comp sets, it was for the purpose of understanding the percentage of guest stays captured by a hotel,” said Mark Lomanno, former president of STR and a 20-year veteran of the team that created it. 
Lomanno said having one group of hotels to compare to a subject hotel originally seemed like a simple and reasonable practice, but the use of comp sets has extended well beyond its intended purpose. Given the limitations of top line revenue as a reflection of performance and the impact of mergers and acquisitions creating fewer competitive parent companies, using the original model to assess performance is no longer appropriate. 
“We have created a generation of C students,” Lomanno said. “The original benefit of evaluating a hotel’s performance has been challenged by the many ways a hotel can game the system.” He shared some examples of how hotels can artificially inflate top line revenue without regard for acquisition cost, thereby undermining profitability, or simply slipping in some lower performing hotels so the subject hotel looks good by comparison.
“For a hotel to get a 100 RevPAR Index, they are essentially average,” Lomanno said. “What owner or manager aspires for average performance?” 
He said he’s seen many hotels, “load up on low margin OTA business just to hit a high revenue mark, only to give it up by paying huge margins in commissions with little or no benefit to GOP.” When that happens, he added, “the performance bonus is still paid.” 
In today’s digital age, it’s now possible to apply robust data sets, statistics and predictive modeling to examine this issue. Models from the analog era of 1990 won’t sustain a healthy hotel ecosystem in the digital age. 
Looking Forward
Lomanno said now that the data is available and technology has advanced, it’s time to move to nextgeneration tools that focus on profitability. “In 1990, we didn’t have access to guest booking data in a market, nor did cost of acquisition matter much; but its critical today. Hotels have to target the right competitors and manage the cost to acquire their revenue to operate in a market with high-cost third parties.” 
He added, “the current models won’t survive one more major M&A event – the legacy RevPAR Index model will be rendered either illegal or irrelevant.”
In today’s digital age, it’s now possible to apply robust data sets, statistics and predictive modeling to examine this issue. Models from the analog era of 1990 won’t sustain a healthy hotel ecosystem in the digital age. 
Updating them sounds good in theory, but what would this approach look like? And what about the federal guidelines that prevent comparing rates between fewer than four hotels against a subject hotel?
The Next-Generation Comp Set
While there may be many approaches to this critical industry challenge, Kalibri Labs has spent the last two years testing alternatives to establishing comp sets for 2020 and beyond. In a departure from the legacy arithmetic average of a group of hotels, Kalibri Labs has created a statistical algorithm to establish a hotel’s Optimal Business Mix using comp sets by rate category and weekpart, along with other pertinent market factors. 
Rather than thinking about a subject hotel competing fully with other hotels, in reality, each hotel competes for a pool of consumers that are spread among many hotels. With daily data from individual bookings, you can determine the degree to which a subject hotel’s business matches up with each competitor by rate category for both weekends and weekdays. Now that the algorithm is fully deployed, the data indicates that hotels in most U.S. markets typically match up to some degree in at least one rate category with about 20 to 30 hotels.
Legacy benchmarking aggregated groups of five to six hotels for comparison, but new data techniques create comp sets for a hotel by market segment. They’re evaluating one rate category at a time and incorporating the cost of customer acquisition. The competitor data by rate category can also be rolled up, but weighting by market segment is much more accurate than assuming a single group of five to six hotels matches up well enough to provide the most effective performance target. The Optimal Business Mix algorithm designed by Kalibri Labs generally follows these steps:
1. Determine which hotels have business that matches the subject hotel: 
The algorithm looks at daily bookings for over 100 hotels in a radius around a hotel. In order to qualify as a competitor, a hotel must have similar rate ranges, arrival/departure patterns, lead times and length of stay. It also has to be close enough geographically to compete for consumers in the same market segment.
2. Take into account cost of acquisition for each type of business:
Add the cost of acquisition for every transaction to make comparisons between hotels based on revenue net of booking costs. This lets you strip down opportunities to the revenue that will flow through to the GOP line.
3. Evaluate the daily volume and rates by market segment and channel for each qualified competitive hotel: 
Examine the business for each competitor for every day to determine which business would be a good target for the subject hotel to pursue. The algorithm generates an optimal mix for each day then rolls it up monthly and annually to set targets.
Beyond Financial Performance
Besides opening the lens to a wider group of competitors by rate category and week part, hoteliers know many other factors influence performance. 
The Optimal Business Mix algorithm considers every piece of a competitor’s business that may be a match then dials it up or down based on brand strength, consumer feedback, meeting space and other meaningful factors. This lets the subject hotel set a realistic target.
The legacy approach to comp sets isn’t likely to serve the hotel community well in the digital market. It’s time to move on. However well intended, the somewhat arbitrary selection of nearby hotels with the focus on achieving performance that’s at or slightly above average is inaccurate and misleading. To remain healthy, the industry can and must do better. Adding the inevitability of more mergers and acquisitions will render many comp sets ineffective as they contain too many same-company hotels. Benchmarking techniques must be viable regardless of the volume of M&A activity.
Target The Best A Hotel Can Achieve, Realistically
Using multiple comp sets to derive a target that represents the best a hotel can achieve should also address hotels’ tendency to gravitate to the average performance of their market as defined by the RevPAR index model. Given the granularity of data available in each market, the Optimal Business Mix offers a detailed, realistic roadmap driven by actual market performance and customized with daily booking data for the subject hotel compared to every potential competitor.
Having a high degree of confidence in the revenue potential for the top opportunities will help hotels and regional teams narrow their focus and surgically target top priorities. They can right-size sales payroll, digital or other large customer acquisition spending because they can spend in proportion to the value of the optimal targets, thereby saving both staff time and cash outlay. 
Benchmarking can be a productive way to evaluate or predict hotel performance. Allowing hotels to set targets against the best they can achieve may prove more beneficial in the digital age than comparing business against a small group of hotels that may only be a partial match. In order to activate these effective tactics, the hotel industry needs to establish comp sets by rate category and weekpart and to incorporate the cost of acquisition into the model.
There’s no reason for a hotel to guess about the best path to improving profits when data and technology can now provide a clear roadmap. At the risk of raising another generation of C students that may not fare so well in a world of digital A players, the hotel industry would be well served to take a fresh look at its approach to comp sets.

©2019 Hospitality Upgrade 
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