Big Data in revenue management is one of the most important techniques for maximizing hotels’ profitability. RM is simply defined as the process of analyzing past and present data to predict the future and to ensure that property offers the right product to the ideal customer at the right time at the best price. The concept of revenue management has evolved dramatically over the past few years and still going forward for further transformation to serve the business unique needs. While RMS algorithms became increasingly relying on data and analytics to improve decisions and to determine the perfect pricing strategies, revenue management technology will continue evolving using behavioral data collected from customers to become more strategic, centralized and to achieve a better outcome for the property.
Hotels performance metrics moves from revenue per available room (REVPAR) to gross operation profit per available room (GOPPAR) and GOP per available square foot.
ROLE OF BIG DATA IN TOTAL REVENUE MANAGEMENT MODEL
According to Sheryl E. Kimes in her survey with 400 international RM professionals indicated that “Total Hotel RM” will begin gaining traction over the next five years. Hotels already recognize that they can significantly increase profits by applying RM practices to all revenue streams, including non-rooms areas such as function space, restaurants, spas, golf, and parking. Thus, hotels performance metrics are moving from revenue per available room (RevPAR) to gross operating profit per available room (GOPPAR) and GOP per available square foot. TRM fundamentally depends on aligning the data from departments such as sales, operations, and particularly marketing. Thus, for effective total revenue management outcome, hotels must take the concept of big data to the heart when it comes to leveraging customers’ data. With such in mind, it’s no wonder so many hotels’ chains have been quick to hop on the big data trend, but the shift is much more difficult when putting into practice.
PERSONALIZED PRICING STRATEGY
In the era of Big Data, the days of fixed prices seem to be coming to an end. Revenue tools are now more sophisticated to serve the current and future consumers who are more informed than ever, and any pricing strategy that seeks to fool or deceive them will not last.
Dynamic pricing is to set the prices according to variables that are not related to the customer such as the time of day, the day of the week, the season, the demand, the available supply, and the competitor’s prices. In dynamic pricing, almost everyone sees the same prices despite not all guests are equal, and their values can differ significantly. So that a “one size fit” approach proves to be ineffective.
Hoteliers should learn from startups like Netflix, Amazon, Airbnb and hundreds of others which prove personalization truly pay off both emotionally and financially.
Alternatively, personalized pricing is to offer rates based on full analysis of customers’ personas and customize those rates based on their characteristics, spending habits, preferences, and lifetime value. It could be also described as the customer-choice pricing model. Booker would have more options to select the individual components of the room and the amenities they want and leave off the ones they don’t, either spending extra or getting a cheaper rate in the process.
Hoteliers should learn from startups like Netflix, Amazon, Airbnb and hundreds of others which prove personalization truly pay off both emotionally and financially. Hotel websites such as Orbitz and Auto-dealers like Tesla utilize personalized pricing to their advantage when conducting sales with a customer. Even Uber has dipped into personalized pricing by offering “premium pricing” to predict which users are willing to pay more to go to a certain location.
Hotels used to personalize experiences and services, and travelers are increasingly expecting perks and promotions based on their preferences. The same strategy expected to be applied to revenue management systems that will use customers’ data to introduce pricing strategies based on customers’ personas, history, spending habits and preferences. Big data will result in RMS transition from reactive to proactive, leveraging analytics to develop more customer-centric offers and an integrated profit optimization strategy that involves all parts of a hotel’s revenue-generating process.
Both personalized and dynamic pricing use data to give a competitive edge, while personalized pricing methods are still in their early adoption stages, it’s expected to be the norm very soon. Hotels can easily give customers more options to select the individual components of the room they want and avoid paying for things they don’t need while gaining more options that truly matter to them. The personalized-pricing business model can also function as a major brand differentiation, allowing hotels to stand out in an overcrowded market, provide a better experience and monetize all the available assets.
Source:
Kimes, Sheryl E. “The Future of Hotel Revenue Management.” Cornell University, Cornell University School of Hotel Administration, 13 Jan. 2017, https://scholarship.sha.cornell.edu/cgi/viewcontent.cgi?article=1239&context=chrpubs