Big data has been a starting player on the strategic revenue roster for years. In fact, years before big data exploded into something like a phenomenon, hotels were increasingly incorporating industry data into their revenue technologies and strategies. The opportunities afforded through the effective use of big data have grown to such an extent that today's flourishing hotels must increasingly leverage larger amounts of available data to seize their most lucrative revenue opportunities.

But while big data might be heralded as the core of intelligent decision-making, using it effectively can prove to be daunting task for many hotels - especially for those looking to use industry data to develop new revenue management strategies, such as an increasingly essential need for hotels to strengthen their channel performance capabilities. Channel performance refers to the management of channels or sources of business to achieve optimal revenue and maximum profitability for a hotel. However, when it comes to using industry data and revenue technology for sustainable channel execution, management and results, hotels need to not only prioritize the right types of data, but use it effectively to track and manage their costs and thoroughly understand their guests.

Prioritizing the Right Data

As hotels explore different types of data for their revenue management strategy, they need to take into account the degree of uncertainty the data brings since a high degree of uncertainty can create a risk for substandard revenue decisions and strategies. This is why identifying the right types of smart data is a critical first step for every hotel.

At the highest level, analytical revenue management technology is traditionally comprised from a combination of four data set types, with each set contributing to very distinctive outputs. To better understand the importance of each data set type - and how each one helps drive revenue results for hotels - the following is a brief breakdown of the four types of data sets: descriptive data, diagnostic data, predictive data and prescriptive data.

Hotels have been using descriptive data in their basic hotel technology to achieve their business goals for many decades. Descriptive analytics utilize data similar to standard hotel reporting, and large volumes of this data are used to give hotels a view of what happened in the past. These descriptive analytics allow hoteliers to make informed decisions about the future based on data that details what has already happened. Descriptive data falls into a category classified as "hindsight," which is data that provides hotels with the ability to interpret their historical performance.

Hotel technology also uses diagnostic data to measure hotel performance relative to its market by aggregating historical data to understand why an outcome occurred. Diagnostic analytics look at historical data from multiple sources, aggregating it to make assumptions for the market as a whole. These types of analytics can also be used by hotels to help benchmark their property performance against their competition, as commonly illustrated through the likes of STR reporting. Similar to descriptive data, diagnostic data also falls within the hindsight category since it uses performance data that helps hotels understand the reasons behind their past performance.

The majority of today's revenue management solutions utilize predictive data, which provides hotels with well-informed occupancy and revenue forecasts. These forecasts are typically used to help hotels establish their ideal pricing and inventory control strategies. As hotels gain insights into future outcomes, they can accurately manage price and inventory to achieve an ideal mix of business. However, as predictive analytics are derived from forecast models based on historical records, there can be challenges forecasting time frames with brand new market conditions or events. Predictive data falls into the "foresight" category, as its data forecasts future outcomes to identify revenue opportunities for a hotel.

In more recent years, prescriptive data has stepped into an integral analytical role for today's hotels. Prescriptive analytics not only leverage historical data points, but future data that allow hotels to go beyond conventional hotel forecasting practices. Through the use of advanced market intelligence, prescriptive analytics are able to understand optimal outcomes and the strategic decisions needed to achieve them.

Today's advanced revenue management solutions provide these analytics - and they give hotels deeper levels of insight into market positioning, channel profitability and guest personas. When this data is combined with powerful revenue strategy controls, it delivers highly accurate pricing and inventory control decisions by room type and multiple lengths of stays. It also allows for more insightful marketing campaigns and promotions that can be monitored in real-time to maximize direct business and profitability.

Prescriptive analytics move hotels beyond foresight into the "insight" area of analytical capabilities. Market intelligence data provides hotels with deeper views of their data to maximize the most amount of revenue possible, while the automation of today's technology continuously optimizes decisions and outputs, leaving hotels with little room for human errors and missed rate opportunities.

As big data continues its reign as one of hospitality's most prevalent revenue management themes, it has become paramount hotels focus less on the 'big' narrative, and more on the application of 'smart' data. With large amounts of disparate data available today, hotels need to prioritize the data that provides them with meaningful insight and action. Leveraging forward-looking demand intelligence with historical and internal data sources will help create an analytical engine that provides a hotel with insightful strategies that deliver optimal revenue results and maximum profitability.

Organizing and Understanding Data

Organizing and understanding data is essential to effectively using it to help drive the decision-making process. This is also true when it comes to channel performance, in particular as it relates to channel costs.

To accurately track costs, it is critical that effective best practices and standards are structured around the proper use of business coding and data collection. This ensures the available data is both accurate and suitable for digging into new channel performance opportunities. As an example, source and channel fields within reservation systems are extremely vital to helping hotels understand where their business is coming from, so they may, in turn, track the costs of each reservation. Practices that ensure these field types are well-defined and reliably assigned to reservations allow hotel properties to successfully monitor and evaluate their channel metrics.

The other element that is critical in this process is identifying the true cost of each reservation. This may be a relatively simple exercise for an online travel agency reservation that carries a fixed charge, but the depth and breadth of charges related to a reservation are often more complex than that. Considerations must be made for all costs involved in a reservation - this may include such items as variable commissions, labor costs, sales spend, loyalty program charges and a plethora of others. Ensuring that these elements are understood, organized and tracked correctly is essential.

Once data is organized and understood, hotels can look to advanced revenue technology and services to close the revenue gaps on better channel performance. The availability of insightful channel reports and dashboards that revenue technology provides give hoteliers swathes of opportunities to analyze acquisition costs and channel revenues at deeper and more customized levels. As more hotels start tracking their channel costs more closely, they'll have the ability to monitor their production into the future, which allows them to make any necessary adjustments to their strategy before it is too late. This will result in more visibility into profit-focused key performance metrics, such as net RevPAR and net ADR, which support hotels in delivering a channel performance strategy that maximizes their bottom line.

Optimizing Performance Through Market Intelligence

When applying big data in practice, hotel revenue management strategies have historically focused more on its mathematical side by looking at traditional data sources (such as economic factors and historical results) to anticipate market demand. Hotels rely heavily on these data sources and their performance-based numbers to identify the basics of a profitable business strategy. However, exponentially more hotels are seeing untapped wells of opportunity to drill deeper into their data to extract and analyze behavior-based facets and thoroughly understand their guests.

Strategic revenue management is beginning to rely heavily on prescriptive analytics to build upon traditional forecasting practices and look beyond numbers to better understand how and why a particular outcome occurred. Across the industry, this shift in strategy is largely considered to be a fusion of both revenue management and marketing strategies. Combining these two functions allow hotels to identify the factors attracting and driving potential guests to book directly with the hotel, as well as helps determine the ideal price to bring in the most revenue at the lowest costs. The use of this market intelligence data supports hotels as they plan intelligently and make more profitable decisions for their organization.

Hotels typically have access to technology that tracks basic shopper activity on their website; however, it has been mostly limited to high-level booking data and lost business data for only their brand website. While this type of market intelligence data allows hotels to view searched date ranges, page activity, and room types or packages shoppers expressed interested in, online shoppers have still remained largely mysterious. This is where the availability of travel intent data - and its strategic implementation - has become critical for developing a hotel's revenue strategy.

Travel intent data uses search and booking data from third-party booking sites and OTAs to help quantify the demand a hotel can expect for future dates. This advanced and predictive demand intelligence provides hotels with human-focused insights that allow them to market strategically with specific ad placements and personalized offers that pull in more direct bookings. Taking the insights from travel intent data, and layering it into the traditional data sources used in forecasting, provides hotels with one of the most lucrative opportunities to predict which guests are most likely to book and deploy a tailored marketing strategy that targets them.

Big data won't be sitting on the revenue management sidelines any time soon, and hotels can continue to expect new and evolving technology that will help them make the best - and most profitable - use of it. When looking to uncover even more revenue opportunities, today's hoteliers are turning to the implementation of strong channel performance initiatives that further strengthen and execute their most powerful and profitable revenue strategy.

Reprinted from the Hotel Business Review with permission from www.HotelExecutive.com

About IDeaS

IDeaS, a SAS company, is the world's leading revenue management software and services provider. Combining industry knowledge with innovative data analytics technology, IDeaS creates sophisticated yet simple ways to empower revenue leaders with precise, automated recommendations they can trust. With 35 years of expertise serving hospitality, including hotel, event, and parking clients, IDeaS delivers revenue science to more than 30,000 properties in 164 countries around the world. Results delivered. Revenue transformed. Discover greater profitability at IDeaS.com.