The revenue management field is ever-evolving. Paul Murray, VP, hospitality practice, Revenue Analytics, an enterprise SaaS company specializing in revenue management and pricing solutions, talks trends and the relationship between revenue managers and tech.
A lot of talk these days is about an eventual downturn. How should hoteliers think about revenue management in this environment? As much credit as we give ourselves, revenue management has historically responded poorly to downturns in the economy. In the past, as we saw demand taper off, price wars have ensued, and prices moved south at a rapid pace. The challenge is exacerbated by the industry’s slow response to an eventual economic recovery, which has seen average rates improve much more slowly. The good news is that this behavior pattern changed slightly during the 2008 downturn, which is coincidentally when first adopters were deploying price optimization analytics that leveraged price sensitivity measurements to combat this type of emotional response to competitors dropping rates. The learning from this for the next economic dip is to focus on your own analytics and behave rationally. Fundamentally then, it is important to act now to make certain your demand forecast is strong and incorporates human insights, and ensure that you tune price optimization and market response models so that you are confident that a change in price will confidently lead to improved revenues.
You have more than 20 years of experience in the hotel industry. How have you seen the role of revenue management technology evolve? The role of RM technology has changed in many ways since 2000. This is partly in response to technological capabilities, such as cloud computing, which allows for processing more data in a faster manner to deliver more accurate results in near-real-time. This speed and data has enabled advancements in analytics that have led to improvements in forecasting and optimization capabilities in today’s RMSs. Another technology change is the reliance on the internet for distribution, which changes the way we sell inventory, price our hotels, and consider our own online distribution channels. Additional factors affecting RM tech changes include new business needs as revenue management becomes more imbedded in hotel strategy and more sophisticated in the science predicating demand, pricing rooms and managing inventory. This results in systems that are more complex in screen design and reporting capabilities, that have more integrations throughout the enterprise IT ecosystem, and consume more data to solve more intricate problems. I do believe this brings us to a tipping point where we have over-indexed our systems on complexity and decision support—making systems difficult to learn and manage. So, the next frontier is to move past decision support (providing data to users so they can make a decision) to improved automation in pricing and forecasting so that we free up revenue managers from managing task to delivering strategy.
Some major challenges for hotels include supply growth and disruptors like Google and Airbnb. How do you see these challenges impacting hotels’ revenue strategies? Over time, distribution partnerships have gained the upper hand when it comes to owning the guest relationship. Google’s entrance into the distribution space threatens to steal share from both hotels and OTAs. Further, Airbnb as well as other short-term rental accommodations offer alternate options for leisure guests and increasingly for corporate travelers. The challenge for hotel chains is to respond to these in ways that strengthen the direct relationship with guests. One way is through loyalty programs. In fact, a recent trend of member discounts through hotel direct channels has some evidence that there is an impact to guest booking behavior in ways that benefit hotels. Another strategy that we are seeing picking up momentum among hoteliers is personalization. The intent is to leverage hotel website and reservations system technology to give the guests a better buying experience that can’t be replicated through distribution channels like OTAs and Google. This improved buying experience could take many forms, such as customized offerings based on buyer past behaviors and future booking characteristics—such as leisure or business travel. It could also include valuable attributes that are meaningful to the guest, such as parking, late checkout, breakfast, spa packages, or other methods to merchandise offerings that are meaningful to the guest.
We’ve seen many mergers and consolidations, which by their nature include technology integration. From a revenue management perspective, have you seen any major issues with this? As hotel chains consolidate, we see two different approaches to revenue management. The first is consolidation of resources and strategies that seemingly leads to efficiencies in scale. The challenge is when the consolidation includes vastly different scales, such as midscale and luxury hotels, the consolidation of strategies and technology will eventually underserve one or both of the newly partnered hotels. On the other hand, some chains react in the opposite manner, by allowing different technologies and strategies to coexist but without connecting the dots in ways that allow the chain to monitor and manage performance across the disparate group of hotels. The Goldilocks solution is to develop strategies that speak to the level of complexity that is meaningful to each chain scale but is also based on a technology that is integrated across the organization and supports performance monitoring in a simple and direct fashion. Today, this is a hard balance to strike for many reasons but should be a target for hoteliers as they look to manage more hotels across a broader spectrum of hotel types in order to deliver long-term revenue gains.
From your perspective, what’s the relationship between revenue managers and RM technology? Hotel revenue managers have been pioneers in many ways, especially when it comes to data, analytics and technology. They were at the forefront of applying airline yield tactics to hotels, they were early adopters of big data, they pushed the envelope in forecasting and optimization analytics, and have helped deliver high returns from their endless contributions to their hotels and hotel companies. In all, the revenue manager has led technology development and picked up where technology dropped off in the past. The end result is a more scientific approach to revenue optimization. As revenue management is applied to more and more revenue streams across hotels in the near future, I believe we will reach a constraint in human resources. We can’t ask the same resources to do more and more forecasting, pricing and inventory controls across more and more venues in the hotel. And, it is challenging to expect that we can find and train more and more team members to take on the new activities. So, what is the solution? The way to account for the expanding needs across limited resources is to turn toward automation. Today, we have the technology and the confidence in our data science to (a) manage real-time optimizations throughout the day and (b) the analytics to solve for pricing and inventory optimization. Leaders in revenue management will begin to trust these combined powers to move to automation versus the existing and intensive process of reviewing and recreating every scenario before eventually adopting (or overwriting) and deploying that same decision. So, the future hope is for a more seamless interaction between the revenue manager and technology so that they can apply their insights to the rest of the hotel.
In a 2019 survey, you found that 83% of hotel chains were planning to invest in revenue management. In what type of platforms is investment happening? Revenue Analytics has just completed a 2020 industry survey, and we are evaluating some of the early results, which provide insight to this question. Interestingly, we see hotel companies self-reporting that investments will be focused in many strategies, but with the greatest focus on Pricing, Automation and Data Science—all of which dovetail nicely into our prior discussions on merging revenue manager decision-making into RM technology. Surprisingly, key segments of focus for the coming year will remain transient business segments, which matches our results from our 2019 study. Group segments, meetings and marketing follow behind in secondary areas of focus for hotel revenue management. This may tell us that we haven’t quite solved transient problems in RM and speaks to our potential readiness to move on to other segments. Last, our preliminary results show us that RMS technology will be the primary area of focus in RM, with Business Intelligence falling in at second in our responses.
Different chain-scale segments have different needs. For each of the segments, what are the most important things to think about when looking to invest in revenue management tech? It is very intuitive that economy, upscale, and luxury hotels have different operating models and resources to devote to RM. Each has its own operating complexity, demand characteristics, and associated pricing and inventory decisions. On one hand, economy hotels are small properties with multitasking team members. They host individual travelers typically on a short booking window and need to react quickly to pricing changes. Alternatively, luxury hotels focus on service levels and loyalty, they have a long lead time with complex distribution channels and have a desire to maintain rate premiums that support their brand identity. So, the key is looking for technology solutions that solve the needs of the groups of hotels you are serving. For example, in my previous example, an economy hotel may need a very reactive and active pricing solution to remain relevant as guests’ book in the extremely short-term window. On the other hand, a luxury hotel does not want a pricing tool that introduces too much variability in pricing, as this could undermine the brand position and service level offerings that the hotel is delivering. In many instances, these same hotel types fall under a single chain and therefore must be served with different solutions to maximize the performance of both sets of properties. HB