In our previous blog When Do You Sell Out?, we wore the shoes of a (fictional) couple looking to book a last-minute, short anniversary stay at three different hotels.
NB: This is an article from Duetto
Because the couple was booking late, only one of three desirable properties was open – and they ended up with the room they wanted (and more). Comparing the couple’s nightly rate to the competition who sold out earlier, the “winning” hotel cashed in big by adopting a pricing strategy that their competitors didn’t – all the while holding occupancy lower than the others to better optimize resources.
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Sound like a crazy daydream? It’s not, and we shared the mathematical and logical reasoning behind it. Yet, hotels still stumble when it comes to breaking from the pack – for reasons that often boil down to siloed priorities, dated technology, forecast-driven actions, you name it.
This scenario might sound impossible – much less doing that every day, at scale.
With the pandemic becoming endemic, and the future of demand behaving in ways even analytics are struggling to analyze, you’re going to have to take a long look at how your decisions get made to define a strategy that you can flex and evolve to move forward profitably.
Your aim is to be the choice of customers like the ones mentioned in the previous blog, at the price you want and the price they are willing to pay.
So how do your decisions get made?
As hoteliers, we’re still looking for answers to how we can capitalize on endemic-powered demand – the demand of now, and the future. But in many cases, each function or department – scaled/limited by resources, tech, and other factors – is making decisions in terms of what will help their business unit succeed. Certainly top-down or centralized policies and strategies help gain some consensus and control; however, those practices can also restrict or waylay functions designed to support those objectives.
An entire area dedicated to the art and science of making decisions – appropriately called Decision Management – can help us here. Decision Management helps dictate the optimal mix of analytics, business rules/strategies, automation, channel, timing, and other factors required to consistently make the best decisions for specific use cases – like pricing.
Why should hotels care about RMS and decision management?
We know why hotels are focused on pricing – at the end of the day, how you set your pricing will impact every facet of your business, from staffing and distribution to occupancy and, ultimately, profits. Offering a room at a price is a decision you make – and the act of managing your pricing is, per our definition, decision management.
In our previous blog, three hotels of similar desirable features (in the eyes of the anniversary couple) originally offered occupancy for a particular date, but two sold out too early for that couple to choose either one. Ultimately, the remaining hotel (that they chose) made the decision to hold prices higher than usual (and thus retain occupancy at a later date), with the confidence that last-minute bookings would more than fill the potential revenue lost from an unbooked room.
Not only that but Hotel “P3” also decided to create a “last-minute” promotion that added an extra night of guaranteed, higher cost bookings when the other hotels would need to scramble just to fill those rooms. Their “decision management” outperformed the competition!
But what was it that they did differently? Let’s examine each hotel from the previous blog by how they activated the various levers of decision management:
Let’s start with P2, who sold out a full three months before the couple wanted to stay there. Their pricing is almost completely automated (with occasional overrides when rates seem out of whack or there’s a sudden need to shift based on comp sets). They used a standard BAR-based pricing algorithm which looks neat on a pricing table, but it isn’t accounting for increased, predicted demand going forward after they sell out – so they sell at a lower price than they could get.
P1 took a similar approach but was able to increase their ADR (compared to P2) by staying open longer. How did they hold off? When they got close to 100% occupancy, they set a rule (strategy) to completely turn off OTA bookings, eliminating that alternative for customers. This allowed for 100% direct bookings and zero last-minute discounts, but also reduced visibility for prospective future guests – not a great thing when lower demand periods demand agile channel management.
P3, the couple’s choice, did some things differently – which is particularly clear if we examine this from a Decision Management perspective:
- Their Open Pricing Algorithm allowed them to flex channel discounts (not off a static BAR), so they could profitably retain indirect channel sales until just before selling out. When they were down to their very last high-value rooms, they retained the “marketing” OTA channel (“contact hotel”) to ensure maximum visibility for last-minute potential guests.
- They applied rules to extend the power of the algorithm’s Open Pricing DNA. In this case, for example, rules provide the basis to set strategies for different levels of occupancy, so that pricing changed when they hit certain thresholds. Similar to other forms of decision management, RMS rules are if/then based and allow hotels to flexibly combine business strategies with analytics to increase/improve outcomes (“human + machine”).
- They deployed automation based on the factors above, with their RMS continually re-optimizing to ensure automated rates aligned with their customer-centric pricing strategies and the influx of fresh, normalized data coming from their internal and external systems. Periodically, either due to predicted demand spikes or “testing” new strategies (such as adding new segments or rate codes), they would temporarily switch to manual mode until they were satisfied with the outcomes. Via robust reporting and BI, they could compare before/after results to determine if their analytics and strategies were indeed behaving optimally in the current environment.
For P3, pricing decision management allows them to continually attract higher paying customers and optimize room bookings, while consistently staying “open” well beyond their competition. This scenario may seem like a high wire act to some (“aren’t we risking leaving a bunch of open rooms?), but remember – the analytics, the rules/strategies, and automation are thinking and moving faster than what you’ve probably experienced, and the continual influx of data into the decisions help you calibrate to adjust to various demand and business scenarios.
RMS’ can be a key decision management lever in your overarching business objectives. Your room pricing is impacted by, and impacts, other decisions, such as what packages you offer (P3 was creative with their “extra night”); building out your loyalty program; how you adjust your staff to predicted demand/occupancy goals (P3 is operating at 90% maximum occupancy to help ensure staff morale and avoid last-minute shortages), and so on.
As you align your cloud RMS with other modern tools in your tech stack, the opportunity to deploy and optimize connected decisions across the business will have a dramatic impact on attuning your brand to what the market – YOUR desired market – wants. Then you can differentiate well beyond price, making your ability to separate from the pack even more profound.
