Sure, big data can be a marketing powerhouse, helping reach the right guest at the right time. Is there anything more important, really?
Yes! Reaching the right guest at the right time with the right rate. However, that rate should not only be the product of what the guest will pay (because this can lead to a hotel full of low-paying guests) but also of what the maximum opportunity for revenue from that guest may be. This number is a dance of customer-centric data, as well as data external to the property data. The former includes historic behavior and future predicted guest behavior, and the latter encompasses competitive and other market data (events, weather, and so on).
Hotels can’t rely on the PMS and ancillary systems, that are often not integrated, to offer the appropriate data for making savvy revenue management decisions. As a result of these antiquated systems, rate decisions frequently end up being a product of a gut idea about what the guest will pay instead of an exact figure resulting from specific data points that have been scientifically generated.
And here is where I officially begin to beat a dead horse. Data must integrate, both technically and visually, in order to be of use in rate determinations.
For the purposes of revenue management, hotels have at their disposal PMS data and CRM data. Then there is reputation data, which comes from a number of sources (Google, TripAdvisor, Yelp, property website, and so forth). For tech-wise properties, some of that reputation data is derived from social media, but social media also offers many other clues to guest behavior that can be integrated into revenue decisions. Web analytics, mobile analytics, and geo-location data should be considered. Competitive data is a no brainer, but hotels must be clear about their competitors in order for the data to be relevant. Many hotels are still casting their net in the wrong direction, either aiming at properties they wish were true competitors or simply casting it too wide. Just as the goal is to turn revenue projections on a dime, competitive data will also be shifting with lightening speed as more hotels adopt more nuanced strategies. Technology must keep up.
That said more data isn’t necessarily better data. Each property must decide which data is most relevant in making proper strategic forecasts. And the data should serve the property by “improving price-elasticity estimations, recommending better competitive pricing decisions, changing the objective (profitability vs. revenue) used by optimization algorithms, and adding the user-centric information that guests actually use in selecting hotels,” according to Paul van Meerendonk of IDeaS Revenue Solutions (Hotel Executive).
In addition to integration and careful consideration data relevance, the organization must align. Some call this a revenue management culture, but no matter what you call it, the ability to communicate underscores its success. Sales and marketing must communicate with revenue management, revenue management must communicate with reservations. Reservations must communicate with sales and marketing. Reservations and marketing have customer-centric data, while the revenue team should have their finger on the pulse of the market. The exchange of information must go in both directions, across platforms, and ideally be mobile-friendly, in order to be successful. The system should allow diverse teams to access and manipulate the same data as well as correspond about initiatives that impact or require rates.
For some properties, the aspiration of big data when applied to revenue management is the creation of completely personalized pricing based on a wealth of available data about guests and an intricate web of information that, in a nutshell, locates their pricing thresholds (DataScienceCentral.com). Personalized, flexible, open, dynamic pricing. Whether or not you to subscribe to any of these burgeoning revenue management strategies, the fact of their existence points toward a shift in the way revenue management has been handled pretty much since its inception, which is basically setting a rate and adjusting up or down based on limited factors such as demand and occupancy. Fortunately, the leap to smarter pricing will make hotels more competitive, especially when it comes to driving direct bookings and diverting revenue from OTAs, and it depends on big data.