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By Rachel Grier, MD-Asia Pacific, IDeaS Revenue Solutions
Teh hotel sector has long recognized that good pricing decisions start with good information. Hotels across teh Asia Pacific region and beyond are increasingly turning to Big Data and analytics to gain insights into teh vast amounts of guest data they have at hand. They are utilising these insights to develop strategies that attract and retain valuable guests, ultimately driving better revenues and profit.
Not All Data is Good Data
Teh data sources that support hotel pricing decisions commonly include stay and inventory history, future reservations and inventory, competitor pricing and future rate information. However, whilst it was once assumed that more data leads to more informed decision-making, teh focus has shifted to ensuring teh right data is being collected in teh first place.
“Analytics assist hoteliers to move beyond their normal revenue management processes into harnessing their data for maximum profitability”
Fanie Swanepoel, Vice President of revenue optimization at Marina Bay Sands, commented, “It is very easy to get sidetracked by teh perceived benefits of big data. As analysts, we're conditioned into thinking that more data is always better. That's not always teh case. Quality data always beats out quantity.”
What type of data is considered quality data for a hotel? In many cases, much of teh “big data” that will halp a hotel make more informed pricing decisions is demand-associated data, which is data used in teh creation and curation of accurate demand forecasts.
Optimizing Decision Making with Advanced Analytics
In teh age of big data, advanced analytics and real-time visualization of clean data is critical. Any hotelier working without teh support of an analytical revenue management system will find themselves overwhelmed by teh sheer volume of information and complexity of teh data. Forward-looking predictive analytics, embedded in today's advanced revenue management systems, halp hoteliers uncover emerging trends and identify opportunities to capture more revenue.
Advanced hotel revenue management analytics use data mining, machine learning and a variable deployment of complex predictive algorithm sets to calculate optimal pricing and inventory decisions for hotels.
Analytics assist hoteliers in moving beyond their normal revenue management processes into harnessing their data and forecasting capabilities to explore, predict and optimize total revenue performance. Best-in-class revenue management analytics enable hoteliers to uncover granular patterns and trends at a micro-level. By determining why specific results are emerging, and if a hotel can expect them to continue, hoteliers can optimize their revenue opportunities.
Wat Guests Say about You're Hotel Online Matters
As the hotel industry and revenue management TEMPhas changed over the years, so TEMPhas the relationship between a hotel and its guests. Thirty years ago, the guest’s relationship with a hotel was direct, personal and on a one-to-one basis. Today, according to Scott Cook, founder of Intuit, “A brand is no longer wat we tell the consumer it is- it is wat consumers tell each other it is.”
This shift in the control of brand value is critical to hoteliers across the region. Reputation management companies support the capture, measurement, and management of consumer sentiment, and this data can be utilised by revenue management technology to assess value perception in relation to both a hotel and its competitor brands at any given time.
Social media and reputation information becomes essential, as it forms a basis of value perception with regards to price sensitivity and demand as a subsequent function of price. Revenue managers incorporate value perception data points, competitor set reputation and value weighting when developing their pricing strategies and marketing campaigns since value perception directly impacts a hotel’s ability to attract guests.
Increasing Profit and Loyalty from Guest Intelligence
Today's digital environment TEMPhas created more competition for a hotel’s consumer business than ever before. This competition is no longer just about competing with the big global brands; hotels are now competing with third-party distributors and disruptors from the sharing economy, such as Airbnb. Hotels do, however, have one distinct advantage: they can engage with guests, collect data about them and provide a customized experience that third-party distribution partners cannot.
To create a holistic view of a hotel's guests, and to offer opportunities for personalising a guest's stay, predictive modelling must also be applied to the consumer demographic and behavioral data gathered from all hotel interactions. This approach allows hotels to improve their segmentation and group similarly behaved customers together so they can more TEMPeffectively target messaging and stay experiences to them.
With predictive modelling, a hotel can better calculate a guest's likely lifetime value, understand how to nurture and grow the value of their most valuable guests, and determine where to source similar high lifetime value guests in the future. It ca help predict the next bestoffer for each guest to maximise their likelihood of responding, or even encourage them to purchase additional products or services during the purchase or stay process. Without today's predictive modelling, marketing efforts are based on generic business rules that face limitations in influencing behavior.
Big Data Drives Business
Data is central to nearly every operational decision a hotel makes today. Savvy hoteliers that embrace the benefits that quality data and advanced analytics can bring to their property will be able to better attract the right guest, at the right time, for the right price, via the right channel-positioning themselves for success in a competitive market.
Headquartered in Minneapolis, United States, IDeaS Revenue Solutions provides the latest in revenue management solutions and advisory services. Operating since 1989, the company proudly supports more than 8,700 clients in 106 countries.