The market is moving fast, but your competition is moving faster. The ones that leverage revenue science and the power of automation to handle the massive amount of data revenue leaders need to stay competitive are at a distinct advantage. But, staying ahead of the competition requires you to change your daily behaviors in order to focus on higher level strategic work.
Technology is also moving fast—no, make that really fast. Today we are able to analyze higher volumes of data, output clearer insights and provide more opportunities than ever before. We have seen how the omnipresent Internet-of-Things ecosystem is finding new ways to connect personal devices with just about anything, not so subtly encouraging those of us with a pulse to stay on the grid. Permanently.
Hotels, in particular, have wished for similar advancements in property technologies for enhanced guest services, enriched productivity and overall profitability. However, even with all the advancements to date, many hoteliers still rely on limited processes and technology—to the detriment of their productivity and bottom-line revenues.
While we might be slowly making our way up the proverbial hill of innovation in any variety of ways, the contrast in revenue management capabilities from 10 years ago to today is compelling.
The Next Generation
A new era of machine and software collaboration is already here. With each move forward, machines are learning faster than we can ever hope to. It has become clear the human brain can no longer keep up with the influx of data being generated by today’s technology.
We are also witnessing a rapid increase in newcomers to the hospitality and travel markets. Startups are disrupting the playing field and making the booking and customer experience more personalized, leading the way toward greater customer engagement and satisfaction.
In the same way we have seen rapid technological advancements occur in restaurants, retail and healthcare, we are seeing a direct crossover into hospitality and travel. From the cloud to biometrics and robotics and from big data to AI, innovations are hitting the market at breakneck speed.
While it may seem overwhelming to try to keep up with the constant flow of innovation, now is the time to lean in and tap into the additional power you can gain by fully leveraging automation and technology, not only to stay ahead of the competition, but also to satisfy your customers. The reality is the convergence of technology and automation into our daily lives is no longer unusual. It has in fact become the norm, particularly as it relates to the customer experience.
Automating Revenue Management
Hotels have managed inventory through manual controls for decades. However, compared to 10 years ago, many revenue technology providers haven’t improved these fixed controls at all and with their day-to-day responsibilities, hoteliers often have limited time for strategy—especially if they’re manually managing rates.
Technology requiring manual maintenance and implementation of rate recommendations forces users to be less nimble to market shifts, limits time and resources, and hinders productivity. Advanced technologies today, however, use analytics to deploy an automated inventory strategy that not only improves profits, but productivity as well.
Machine-learning innovations, coupled with high mobility, prescriptive analytics and transparent business intelligence have put hoteliers in a prime position to break down old barriers to new profits. Automated pricing and inventory decisions, for instance, continually optimize and automatically deploy to integrated selling systems. This gives time back in the day to focus where it’s needed—on strategic opportunities.
Obviously, the ultimate goal of a revenue strategy is to drive profitability. The challenge lies in pricing different rooms, through different channels, across different days, to different guests.
With unique demands for room types, revenue technology needs to support different buying behaviors by analytically-determining ideal prices, inventory controls and overbooking strategies for different room types. If rates are managed independently from inventory controls, profits end up sacrificed in the process.
More Is Not Necessarily Better
In order to gain the best results, hoteliers need the best data. This means more than anything, data quality matters. As the industry continues to leverage evolving data sources, hotels need to be thoughtful about the data used within their technology and business strategies. While we live in a world where “more” equates itself with “better,” that’s not always the case when it comes to data.
Thanks to big data, machines now have a lot more sources from which to learn. The question is: are they learning or are they causing hotels to work harder to decipher all that added data?
Revenue technology should analytically incorporate big data and forward-looking market intelligence when the right types of data are statistically significant—and can drive better revenue performance.
Let’s think about a well-established data source that can inform a revenue strategy. Sliced and diced in so many ways, you can look at data by arrival date, channel, source, segment and more. Several revenue strategy tools bring data sources into their system, but few actually leverage the data as part of their optimization process to ensure strategy is also optimized against price and inventory in the market. Looking beyond that, what do we want a machine to learn from this data?
A high-performance revenue management solution that analytically determines decisions, like pricing and inventory controls, should be able to generate a price that adapts to fluctuations in the market and anticipates them in advance.
It should understand the impacts of a particular price in the market and if you raise or lower that price by $10, what the change in actualized revenue will be as a result. If demand can influence price, and price can influence demand, it should stand to reason a machine-learning tool will also understand that relationship and better optimize pricing to secure the optimal mix of business from the demand.
But when demand is only forecasted based on the price you set, you never truly understand what the optimal outcome is or what impact that may have on another rate derived from the price you set (e.g., an advance purchase rate). In this case, your machine is not learning, it’s just a more expensive rate distribution tool.
How Do You Want Your Data Delivered?
As hotels consider a solution to help with pricing and business optimization, they must decide if they want a sophisticated machine-learning tool that analytically determines decisions, or a tool that requires manual rules be set.
Revenue managers work hard. They have enough reports to review and strategies to validate. Is your revenue strategy tool working as hard as you, or are you having to work for it? Machine learning is not going anywhere, and systems will only continue to become more refined with more powerful data. Don’t let your strategy be defined by rules you have to continually set.
Similar to the apprehension felt when automated decisions were introduced all those years ago, many hoteliers today might feel just as skeptical about the benefits of machine learning and automation, especially since hotel business relies heavily on human interactions.
However, machine learning should be considered as another opportunity to utilize the advanced power of analytics in order to better analyze all available data—something manual or spreadsheet-based environments simply cannot provide.
If there’s one thing the evolution of revenue management technology has shown us, it’s that analytics matter. Today’s hotels and tomorrow’s success stories have to consider how important it is to build a solid analytical foundation for optimal forecasting and establishing the right rates. With phrases like “big data” and “analytics” becoming important themes in the industry, it’s imperative hoteliers understand what those capabilities can bring to their hotels.
Today’s revenue technology offers intuitive, user-friendly designs, innovative interfaces, customized reports and dashboards, and lots of useful functionality. However, digging deep into their analytics is where the market becomes truly divisive.
Identifying the right analytics often means hotels have to ask their technology providers some tough questions. For example, many revenue management systems use one or two forecasting methods for transient and group business, taking a relatively general approach to analyzing data. Other solutions employ advanced analytics with hundreds of forecasting models to solve specific hotel challenges and produce extraordinary results.
These powerful analytics have the power to assess guest-price sensitivity while accounting for season, day of week, days to arrival, length of stay and more—in addition to managing rate availability, optimizing stay patterns and strategic overbooking to drive the most revenue overall.
Another interesting advancement emerging in the industry today is in integrated distribution strategies and their intertwined relationships with sales, marketing, loyalty programs and revenue management.
To be sure, the combination of systems, the right data, the right strategies, user interaction, machine learning and analytics will ultimately drive the best results for hoteliers going forward. Leverage the power of true machine learning to build an automated profit strategy that sets you apart from the competition.
Dr. Ravi Mehrotra is the president, founder & chief scientist of IDeaS Revenue Solutions. Through the establishment of IDeaS in 1989, Dr. Mehrotra pioneered the “Opportunity Cost” approach that later became the industry standard for dealing with the complexities of the network or length of stay effects in revenue management. Today, Dr. Mehrotra remains an active and hands-on chief scientist at IDeaS. He continues to research increasingly sophisticated methods for dynamic pricing that optimize expected profits over longer time horizons and is a widely-recognized leader in the field of predictive analytics, forecasting and dynamic price optimization.