We’ve seen automation become prevalent across many other industries, from marketing automation tools to self-driving cars. Automation relies on a framework of artificial intelligence and/or machine learning. In the simplest sense, this means a computer, or computer software, is able to compute, perform functions and deliver recommendations hundreds of times faster than any human. And over time, the computer or software is able to identify patterns and perform processes of elimination so that it “learns” from its users.
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Automation is not new to the hotel industry. In fact, it has been a driver of revenue software for decades. Recognizing that travelers are searching and booking around the clock and that market changes can come suddenly, recent versions of systems have been built to react accordingly with adjusted forecasts and rate recommendations. Even factors like changing competitor rate strategies can create immediate demand surges or declines and revenue managers have learned to identify those triggers.
Reacting in Real Time
But until recently, revenue managers have been forced to react to these changes with pricing updates in real time—even if it’s the weekend or the middle of the night. While they might be “alerted” to spikes in demand, they were still usually required to react with a new strategy or, at the very least, accept new rate recommendations across all segments and channels. It’s a difficult task.
Recent advancements in software automation are now allowing revenue teams to fully automate the more tactical decisions and trust they can step away to focus on bigger decisions.
“As technology evolves, datasets become more rich, and machine learning becomes more relevant, revenue management will evolve from pulling pricing levers to creating demand through the deployment of smarter outbound sales and marketing initiatives,” says Mike Medsker, co-founder of Focal Revenue Solutions.
However, in order to trust technology enough to enable “autopilot,” users must be able to understand how it works. Similarly, revenue managers must trust their systems and feel empowered to understand what the software is doing and why it's recommending what it is. This means revenue software providers are opening up more of the functionality and analytics that power things like algorithms and rate recommendations, so users can more clearly understand where those recommendations and suggestions are coming from. As a result, today’s revenue teams have more behind the scenes access to booking curve analysis and forecast accuracy results.
At Melia Hotels International, Global Revenue Development leader Txetxu Gurruchaga says he uses an automated revenue system to help his property-level DORMs focus their attention where needed. Recently, a Melia revenue manager in the U.K. was overseeing two hotels and opening a third. According to Gurruchaga, she was able to put the two open properties on “autopilot” while she focused on getting the new property up and running.
“Once she was confident enough with the strategy and recommendations, we turned autopilot on for those properties so she could concentrate on the opening. And it was very helpful,” Gurruchaga says. “We currently have six hotels piloting 100% autopilot and now our idea is to keep moving different hotels to autopilot for pricing.”
Another example of automation at work comes from Andrew Ritson, Group Revenue and Distribution Manager at Then Hospitality in the UK. He says automation has helped his team make critical decisions, such as removing wholesale rates, as well as helping with segmentation. But there was resistance to taking the ‘leap of faith’ and trusting the technology.
“Automation means time is spent focusing on replacing lower-rated segments with higher-rated segments. It allows the team to question how to fill the gaps we have. There was resistance from the team, but they soon learned that time could be spent looking at the bigger revenue picture,” Ritson says.
Automation, coupled with improvements around user notifications, helps users forget about the systematic changes that are happening every day and instead focus on the more critical decisions. For revenue managers, this means using expertise from the ground, from being in the market and on property, to help build strategies that a machine cannot. This allows revenue teams to focus more time on identifying and understanding any data that may look out of character. In Duetto’s recent research, one respondent summed it up well: “I want to focus on the $500 decisions, not the $5 decisions.”