There have been so many advances in the field of revenue management over the past decade that hoteliers today would be foolish not to use all readily available data to help price hotel rooms for maximum profitability.
Today, hoteliers have access to historical data, business on the books, forecasts, web shopping data and even flight arrival information to give a clear picture of upcoming demand.
But here’s the part we might all be overlooking: If hotels are making such critical decisions on how to price their rooms using a complex algorithm based on a combination of intricate data sets, what happens when that initial information is wrong?
Hoteliers have become accustomed to seeing a small variance in data, especially when it’s transferred among different technology systems. One of my former employers would assume a 10% variance between data pulled from the PMS and data pulled from the RMS. The data would never match, and we spent hours of manual work adjusting it on the backend. The sad thing is I’ve heard numerous stories from others in the industry of even greater discrepancies.
At Duetto, we like to ensure our partner hotels are working with the most accurate information sets. That’s why I’m so excited about the recent success of our integrations team. Now, through even deeper integrations with the industry’s largest PMS providers, we are able to obtain 100% accurate folio-level data rather than reservation-level data.
[bctt tweet="Through deeper integrations, hotels can obtain 100% accurate folio-level data." username="OptimizeDemand"]
Why is Folio-Level Data such a big deal?
Typically, data extracted from a PMS is at the reservation level, meaning any application looking to use that data for the hotel’s benefit would obtain it from the point when the guest made a reservation. At this point, the data is typically the guest’s basic information, the source of the business, what rate he or she received, and how long he or she will be staying with you.
Many times, the total price paid is averaged across the number of stay nights, and any reported ancillary spend is averaged as well. But what if you’re doing year-over-year analysis for one specific day and you’re working with averaged data instead of actuals? This is another common source of data variance.
To solve the variance issues, Duetto has sought folio-level data, which is from post-checkout and comes directly from the guest’s final bill. This means we get all the granular line-by-line charges that may have happened post reservation, from minibar charges, parking, room service or any spa, golf or F&B spend that was charged back to the room. We also see any credits that may have occurred post check-in.
With folio data, hotels have an accurate view of a guest’s total revenue spend. Working with accurate data ensures all the decisions being made with these numbers — forecasts, revenue projections, pricing recommendations, negotiated rates, loyalty promotions and more — are built on a true foundation.
Beyond that, hotels can build custom revenue categories (such as golf or spa), give them a name and assign a transaction code. This means that when you’re looking to analyze or re-segment historical data, you’re working with data that represents the entire guest journey, not just the rate of reservation.
At Duetto, we understand our analytics-driven platform requires intense data validation. It’s not something we take lightly and we won’t ever just build an integration and consider the work done.
Our fanatical approach to deep integrations and data validation is just one step we‘re taking to ensure hotels and casinos have the most relevant and accurate data. No matter the number of hotels you have or how large they are, you cannot afford to make critical revenue or business decisions with less than accurate data.
After all, we really aim to be a property’s single source of truth.