Step Aside Revenue, the Dawn of Enterprise Optimization Is Here
By Bernard Ellis, Vice President of Industry Strategy, Infor Hospitality
Classical and even more current revenue optimization practices and technologies have focused too narrowly on maximizing room revenue, and more recently, to minimizing the distribution and marketing costs associated with that revenue. Expanding the same practice to other revenue streams has been a natural next step for some revenue managers and systems, but the higher that revenues go, the more profit margin that seems to leak out of the balance sheet. A new practice called Hospitality Enterprise Optimization, using the proven analytical abilities of revenue managers and the systems at their disposal, will go a long way to finding that lost profit.
So we've been hearing for a while now that, in the United States anyway, the hotel industry has fully recovered from the downturn, and we're now exceeding pre-recession levels. The pipeline of supply is historically low versus demand, the elusive group segment has come back to the table in spades, and revenue management systems have been running at full throttle to be sure their human operators aren't leaving money on the table. One way to know that revenue managers and their systems are on the job is to just ask any business traveler you know if they felt like they paid too much for their last hotel stay, and the one before that, and the one before that, and you're bound to get a "yes" to most of them. They didn't like the price, but lacking attractive alternatives, they paid it anyway. So, now that the revenue managers have chipped away at the guests' value perception of their properties, at least hotel owners should still be happy, right? Meeting with them should be nothing but an exercise in gratitude and delight. But, this isn't how those meetings are going. Instead, in this high fixed cost/low variable cost business they invested in, they are wondering what happened to that "almost pure profit" business that they were supposed to now be enjoying, in return for toughing it out through those lean years.
They say extreme times call for extreme measures - pick your battles, focus, and everything else will follow. At the depth of the downturn it was all about cutting costs, and at such low business levels, it was pretty easy to discover where the line was where you couldn't cut any further, where you might as well shut off the lights and lock the doors. And in boom times like these, it's all about maximizing rate. RevPAR levels are at historic highs, and most operators are similarly discovering how high they can price before either demand falls off a cliff, or the guests that do pay it are so disappointed with the value, that the hotel pays an even higher price with their social media reputation. So, if hotels are getting historically high rates, why are they not incurring historically high profits, while delivering flawless service? Where did that profit go?
There are a variety of possible causes. One is that many operators may have simply forgotten how to be so busy, or in the case of millennials, may have never been this busy ever before in their careers. Another one is that, in choosing to focus on the rate battle, all of their revenue optimization expertise, attention, and measurement is being applied too narrowly to that effort.
Well, just as pricing decisions during the downturn were relatively straightforward-basically open up all your discounts and get what you can--one could argue that they are almost as straightforward now. If you know your high water mark on price, and you know what days demand will way exceed your capacity, it doesn't take a lot of analysis to conclude that only your highest rates should be available. Yes, you can keep testing if your highest rate is really high enough, or on the softer demand days, keep testing if your band of lower price points is set correctly, or consider if you should have pre-set price points at all, since you may be leaving $5 or $10 on the table here or there. But those actions aren't going to move the needle much.
You can examine your channel mix, and see if perhaps you are getting your business at an unnecessarily high cost of distribution, a practice now commonly referred to as revenue strategy. If you're in the rare situation of really being able to tell your guests what channel they have to book through if they want to stay with you, or not have them think you are sold out when you are simply not available on the first booking channel of their choice (or quite often, their employer's choice!), then that will move the needle more. But what if you go to that next meeting with your owner or asset manager, and it's still not enough? What now? Well, if revenue optimization was achieved by applying observation, science, and technology to the problem, then it only stands to reason that doing so throughout the enterprise should yield similar results: The time has come when Revenue Optimization must give way to a broader practice of Enterprise Optimization.
At this point many readers may be anticipating that Hospitality Enterprise Optimization is simply a new term for Total Revenue Management, wherein not just room revenue is optimized, but also ancillary revenue sources, such as food & beverage and spa. Or they might think it's a new term for Total Profit Optimization, which looks at the combined contribution margins of all revenue streams. And both would be right-Enterprise Optimization definitely requires both of those practices to be carried out soundly. But, it's also so much more. Hospitality Enterprise Optimization doesn't just limit itself to the revenue side of the picture, and maybe the costs most directly associated with bringing in that revenue. Instead, as its name would suggest, it has as its universe the entire hospitality enterprise: any input, output, or business practice which can be made to yield a more optimal result by applying observation, science, and technology, is fair game for Enterprise Optimization.
But revenue is where it all begins of course, and revenue management professionals, as well as the technologies they use, have been steadily improving their ability to ensure that no revenue opportunities are overlooked , and that maximum wallet share is obtained from each guest who walks through the door. This has helped to build bridges between revenue management and sales, since the latter usually receive more commissions on the higher priced products and larger wallet shares brought about by ever more sophisticated demand management. Great things have been achieved, but couldn't one also argue that, since the goals of the business and its bonus-collecting sales and marketing employees were already so in alignment, maybe this was actually not the most fruitful place to start? So many parts of a hospitality enterprise feature business practices and customs where individual goals are not aligned with those of the broader organization, and have a much higher probability of dragging down profit margins. Let's look at some examples of how Enterprise Optimization can find more profit in unexpected places:
- A valet parking manager who must get approval from the controller or GM for a $75 purchase order, ironically has relatively free reign to schedule hundreds of hours of labor. And when peak demand is not met, the ire of angry guests who are getting to their functions late, missing their flights, or otherwise being severely inconvenienced, quickly spreads throughout the lobby, the elevators, and on social media. Result: though he is well aware it will maximize payroll costs, the manager will still tend to staff for the peak workload which could potentially occur.
EO: This is probably one of the more common scenarios which would benefit from Enterprise Optimization. An unbelievably high number of labor schedules are created based on very rudimentary business driver assumptions, either based on early forecasts of occupancy and expected arrivals and departures, or simply because that's how you staff a Wednesday. Instead, through the prism of EO, updated rolling forecasts from revenue management would be distributed to all department heads, and more importantly they would be told how to interpret them, and what the ramifications are. The schedule in this example may have been made as much as a month in advance, and in the meantime, demand from two particular business segments did not materialize as expected, and the RMS revised the forecast downward, so it should be determined if labor schedules should follow suit. While systems can be built with more sophisticated business drivers to calculate how staffing levels should change based on changes to the RM forecast, human insight still has a role. In this case, it would probably be a human that would realize that the two under booked segments tended to be from drive-in feeder markets, and tended to arrive in clusters creating high peaks of activity for the valet department to handle. With this twofold reduction in both the number of cars, and the amount of high-volume peaks in activity, the right answer was to reduce this department's schedule by 75%.
- A bar manager who also works shifts, however, might take the opposite view. By scheduling fewer bartenders than truly required, though service won't be as attentive, he will still keep up with it adequately, and make more in tips by sharing the pool of customers with fewer colleagues. Result: the bar manager does have a great night for tips, but service is mediocre, and beverage sales decrease because people were not offered refills quickly enough.
EO: This is also a common way for profit to evaporate before it makes it to the bottom line. In this case, instead of having the bar manager create the schedule based on gut or self-interest, an enterprise optimization perspective would have uncovered that the expected mix of business in house that night typically correlates with very high sales of signature cocktails, with an average of 2.2 per person being sold. While these are high margin items, they are more laborious and time consuming to prepare, than the wine and beer favored by other very similar segments. As a result, the bartender couldn't work at a fast enough pace, and while not necessarily frustrated by the service, most people who wanted a third or even second cocktail gave up on the prospect, cutting the beverage sales for the night by fully half. This disastrous result could have been avoided by just giving the bar manager a clearer picture of what kind of guests were expected, and more importantly, what to expect from them.
- Engineering is staffed leanly, and any extraordinary repairs that become necessary typically require keeping staff for overtime. Engineers are expected to perform preventive maintenance in between guest work orders, to prevent unexpected repairs from undermining the guest experience and incurring unnecessary costs. However, engineers find it difficult to focus on preventive maintenance with the constant interruptions of guest incidents, as well as not having access to occupied rooms. It proceeds slowly. When there is a severe outage, the engineer who is called in to fix it not only gets overtime pay, but also lots of gratitude from management on duty, and the affected guests.
EO: As in the prior examples, better insight into what kind of guests will be in house, and what volume of service requests and incidents typically are logged by those segments, will make it clearer when it will simply be impossible to pursue preventive maintenance on the fly, and when it should then be scheduled as a dedicated process, to avoid costly overtime repairs and guest dissatisfaction. Though guests are grateful for speedy resolution, it's of course more optimal to prevent the incident in the first place. Technology and the right analyst can also find profit in other ways: are there particular pieces of equipment that are performing much less efficiently than others, and therefore due to fail? Are there certain airport shuttle drivers who seem to use more gasoline than others? Is energy use proportionate to business levels in house? Are review scores higher when incidents of equipment failure are lower? So, take a look at your pile of completed work orders every morning, and start to envision the dollars of lost profit stacked between them. Use technology to harness the Internet of Things for lower cost of ownership, as well as an undisrupted guest experience.
- The final example: A hotel chef's signature dish is a burgundy-braised short rib of beef, which is especially popular with older male business travelers visiting local firms. The food cost of the short rib varies greatly from week to week, and sometimes supplies are limited at practically any price, forcing the chef to pull the dish altogether, much to the disappointment of regular guests. The hotel's main meat supplier offered the Chef an opportunity to make an advance purchase of a large shipment of short rib, on which he jumped. But unfortunately the usual crowd who loves the dish was diverted en masse to a government conference, and instead the hotel was forced to accept a price-sensitive childrens' sports group.
EO: It should be noted that none of the short rib was discarded, but instead found its way into lower priced short rib sliders and quesadillas that were offered as specials for the sports group. This is one of the insidious ways that food cost can turn out to be so much higher than forecast, even when no waste was observed. Upon further analysis, revenue managers may find lots of opportunities like this where higher food cost is highly correlated to certain market segments, even when everything else is seemingly constant. In a case like this, purchasing food in smaller lots on more of a just-in-time basis, even at higher prices, could drive significant gains in profitability that mike make RevPAR gains seem minor in comparison. And if an ingredient's availability and price are unpredictable, then relegate it to just being an occasional special of the day when conditions warrant, and otherwise, buy filet for the businessmen, and ground chuck for the sports group, and, you'll eat better as a result.
An especially unique characteristic of hospitality is that so many of its products are consumed immediately after they are produced: not just a restaurant meal being picked up off the hot line, but a hotel room that has just been cleaned and inspected. Asking Enterprise Optimization to drive better performance from this hectic confluence of demand management, service delivery, human capital management, and financial management may seem like too tall of an order, but it's right down the alley of the analytical minds of revenue managers, and what else do they have to do these days, anyway?
Bernard Ellis, Vice President of Industry Strategy for Infor Hospitality is responsible for defining the global go-to-market strategy for the entire Infor solution suite for the hospitality, travel, and leisure industry vertical. In addition to general product positioning, brand messaging, and industry relations, Mr. Ellis directly oversees product management of Infor’s hospitality-specific PMS, RMS, and POS industry applications, and pursues their tight integration with Infor’s world-class solutions.. Mr. Ellis also guides these other solution groups on the “last mile” functionality required to achieve specialized hospitality editions that outperform best-of-breed industry solutions, yet are still cost-effective to implement. With his launch of Infor CloudSuite™ Hospitality in 2014, Mr. Ellis marked over 15 years of evangelizing SaaS solutions. Mr. Ellis can be contacted at 202-232-3839 or firstname.lastname@example.org Extended Bio...
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