Small hotels may lack the deep pockets of the big hotel chains, but that doesn’t mean you can’t enjoy the benefits of revenue management.
The key to success is recognizing the dance of the RMS (revenue management system) partnered with a skilled revenue manager (RM.)
AI-enabled RMS tools vs “rules-based” RMS
The variables of revenue management include changing conditions such as lead time, market segments, weather, the hotel brand reputation, cancellations and much more. Each of these contributes to your optimum room pricing in different ways.
There are dozens of variables to affect hotel demand, and the combination of the RMS and a skilled revenue manager can result in the maximum revenue boost.
For example, the two types of RMS tools include simple “rules-based” algorithms that can manage one or two variables vs sophisticated AI-enabled tools paired with a revenue manager.
The rules-based algorithm can automatically adjust prices based on one or two variables. This type of software can be used by anyone who has no experience in revenue management as they trivialize the discipline. No real need of revenue managers in this case. But of course, results are more limited.
For instance, if market demand is at x, then price should be y. Or they react in relation to competitive set pricing. But there’s no opportunity to adjust based on multiple variables, nor is there any interpretation or correlation among different variables.
AI-driven RMS takes into account all these multiple variables. Still, most importantly, they incorporate AI (artificial intelligence) and machine learning to learn how humans (revenue managers) react to these variables to replicate their behavior (increase or decrease prices and by what amount, or not change them at all).
Machine learning is part of AI (more on this below.) Whether chatbots, robots, or AI-enabled RMS, the machine learns from the humans that operate it. They don’t replace humans; they augment them.
The revenue managers are basically the “engineers” and teachers, and without them most of modern RMSs would not even exist.
The role of the RMS
The AI-driven RMS and the revenue manager work together. The software simplifies data collection, rate elaboration and online distribution (provided a well-integrated open API connects the systems.)
When the revenue manager feeds the RMS the data ongoing, the so-called “auto-pilot” integrates the machine’s work and the human. For example, with regular updates, the machine learning “learns,” and if the revenue manager is off-duty, sick or overseeing other properties, its prices are automatically adjusted on all the platforms according to the revenue manager’s data it’s learned on.
That’s why AI-based RMSs must be continuously accompanied and monitored by skilled and professional human revenue managers in their learning process.
Just as search engines evolved thanks to the software engineers who understand and build complex algorithms, the predictions of your RMS develop based on the revenue manager’s understanding of the data they input.
As you can see, the RMS does the pricing automation, but it’s the revenue manager who interprets the data and makes strategic adjustments, teaching the machine on a regular basis.
Revenue Managers bring strategic analysis
The human revenue manager interprets the RMS data and adjusts for the best results.
Hoteliers who’ve never practiced revenue management can try a rules-based RMS and see some results, especially if market conditions are favorable (no pandemics, wars, natural disasters etc.). But with a skilled revenue manager, the results will often increase 3x as much. That’s because an AI-enabled RMS incorporates Machine Learning (ML) and the revenue manager inputs different variables to predict different results.
The RMS is a tool that parses vast quantities of data in seconds and “learns” based on the revenue manager’s inputs. The human then makes predictions and adjustments accordingly.
Some GMs and hotel owners choose to use an RMS on their own without a revenue manager and they don’t get the best results. You can think of it as hiring a skilled surgeon vs. attempting your own surgery with some specific tools.
For instance, some small hotels have large double rooms. Could you add an extra bed and call such a room a family room or suite and charge more?
A revenue manager might think “yes” and input such data into the RMS. Yet, a hotel owner might input “double room” into the RMS without accounting for multiple people or beds. This affects the pricing. A Revenue Manager knows different data inputs give different nuanced results.
Software like an RMS works well automating repetitive tasks. It analyzes vast amounts of data in seconds and changes the output based on the variables you decide to put in. There’s always a human decision behind the final automated output. In that way, it’s similar to a spreadsheet. It’s a decision-making tool but can’t make the actual decisions. The person makes data-driven decisions or recommendations thanks to the software’s information.
When AI (artificial intelligence) and machine learning (ML) partner with that skilled revenue manager, the machine “learns” more efficient pricing recommendations over time thanks to the Revenue Manager.
From segmentation to inputs, the Revenue Manager can suggest a commercial strategy. The revenue manager is the engineer and teacher of the software tool because they’ve studied the discipline and understand how the different variables can change demand and pricing.
Most GMs and owners don’t have the time nor the experience to properly analyze the data and make strategic decisions. That’s the role of the revenue manager.
An RMS alone is not the answer
Some GMs and hotel owners want to practice revenue management. Yet, hiring a revenue manager seems difficult and expensive. They think, “let’s try the software and see.”
Of course, it doesn’t take long to realize you’re not clear on the data to enter in the RMS.
The Revenue Manager understands how the variables impact pricing.
Some hoteliers see an increase of around 10% with just the software. That’s because they’ve never practiced revenue management and this slight change makes an impact. But with a Revenue Manager guiding the strategic decisions, those hoteliers usually enjoy an increase of around 30%.
That’s because the revenue manager is constantly working with the RMS to discover the optimum rates.
For example, you can change your room types to increase revenue, but two other variables include your brand reputation and channel distribution. Change these and your hotel can enjoy a strong increase in revenue.
Channel distribution, brand reputation and Revenue Management
Channels and reputation score also impact revenue, and the RMS can showcase possibilities.
Trying distribution channels can help increase revenue too. A human revenue manager can identify channels that attract different types of clients. These could be more profitable than the +10% gain you enjoyed with the software alone and could lead to a 30% boost, leading to a different level of demand and therefore new pricing recommendations from the learning RMS.
In some cases, hoteliers lose revenue by promising a block of rooms to a tour operator at a low rate. Or, they’ve closed the OTAs when it makes no sense to do so. Such scenarios render the RMS useless.
Furthermore, a revenue manager might consider it worthwhile to invest in amenities or additional services like parking, pool, spa, or an enhanced breakfast. Even updated and inspiring pictures and descriptions can change the demand and therefore pricing, and none of that can happen on autopilot.
Besides channel distribution, there’s brand management, such as the hotel’s reputation score.
Consider your hotel’s score on Booking.com, TripAdvisor, etc. You can boost your reputation score and therefore increase visibility, demand, and revenue.
Imagine your hotel has an 8.7 on Booking.com and 4.5 on Tripadvisor. You implement an RMS and see 15% incremental revenue. But what if you had a 9 on Booking.com and a 5 on Tripadvisor? You can boost your ADR and potentially reach a 35% increase.
A revenue manager can drive adjustments to boost the reputation scores and feed the new data to the system.
For example, this ebook shows how the improvements in the breakfast service, overseen by a skilled revenue manager, lead to an improved brand reputation and visibility on the OTAs. More visibility leads to more demand and therefore a repricing approach from the RMS.
Autopilot and human intervention
Pre-pandemic, hoteliers practicing the combination of an RMS and Revenue Manager saw success. The travel industry was stable, and each hotel had certain expectations. An RMS works well during such conditions. But recent events have shown just how vital revenue management is during times of change.
This article from Costar shows how a revenue manager can use the RMS to adapt in times of crisis. It makes sense to turn off the autopilot feature on an RMS during uncertain times and override the rate recommendations. Some revenue managers discovered creative ways to drive business and profitability beyond pricing.
Such event as the pandemic or the recent Russian invasion of Ukraine shows hoteliers need to remain adaptable. Revenue managers need to have the option of manually overriding the settings and teaching the machine new information so that they can become better at pricing.
As you can see, revenue management is about a combined effort of sophisticated software led by a skilled revenue manager. The software alone isn’t able to give insights about key activities like investments or costs cutting or staff scheduling, that’s too sophisticated for a machine.
There are those who say RMS will eventually automate revenue management and won’t need the human element. Such analysts tend to use the words revenue management and pricing as the synonyms but they’re missing the bigger picture.
Pricing is only a small part of the broader picture of revenue management. Pricing isn’t the only way to boost revenue. It’s the final part of the process. It’s a consequence, not the cause. And while pricing can and should be fully automated thanks to machine learning (where teachers are revenue managers by the way), the entire process of revenue management simply cannot.
By now you’re seeing the way a revenue manager works with the RMS. You can see that the human revenue manager brings strategic thinking while the machine automates the data collection and pricing elaboration and distribution efficiently and accurately. Together these can make revenue soar, and should be seen as a modern and positive version of the Greek myth of Centaurus, half human and half AI.
Does it make sense to outsource Revenue Management or hire in-house?
Based on a sample of more than 2.000 properties worldwide studied by the Revenue Team by Franco Grasso, it was found that combining an RMS with an experienced revenue manager multiplies incremental revenue by at least three times compared to the software alone.
The software handles data efficiently, and the revenue manager creates, executes, and oversees the strategy.
For example, some hotels have used an RMS (software only) and seen 100.000$ yearly incremental revenue. Those hotels that combine the RMS with an experienced Revenue Manager can see at least 300.000$. The additional revenue more than absorbs the cost of the Revenue Manager (between 50K-100K/year.)
There’s a reason why the big chains use both. If you review the job descriptions advertised by tech-oriented brands like CitizenM or Radisson Hotel Group, you’ll see they always hire human revenue managers to manage the RMS and other automation. They invest large amounts of money on revenue management (humans plus software) as the cost incidence on total revenue is really low, statistically speaking between 1 and 5%.
Additionally, the most popular AI-enabled RMS tools all have testimonials from revenue managers or revenue directors on their websites. Those are the roles that understand how to configure, teach and use these tools as they have studied the discipline of revenue management.
And it’s no coincidence the same AI-driven RMSs target revenue managers (humans) with sponsored campaigns on LinkedIn and other networks.
Yet, for many small hotels, it makes more sense to partner with an outside revenue management expert. Such hotels can enjoy all the benefits of a skilled revenue manager and AI-enabled RMS but without upfront costs.
Some outsourcing companies offer performance-based variable costs. They provide skilled RMs and an AI-based RMS for a small percentage of the incremental revenue vs the budget or the hotel’s best historical performance, which makes it affordable. It’s a risk-free way to pay revenue management only if it brings a real return.
Conclusion
By now, you see that a revenue manager powers the RMS thanks to their technical and analytical skills. Whether you choose to outsource revenue management or hire an in-house expert, one thing is sure: RMSs will never replace revenue managers for the simple reason that revenue managers are the primary software engineers and developers of all modern AI-driven RMSs.
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