Our industry's big data story continues to bubble over with an increasing amount of emerging data sources – including social media, reputation management engines, web traffic sources, weather and airline data. However, when you throw every piece of data together, it can be inundating and hard to determine which ones positively impact your bottom line.
Here is a closer look at three data sources that can affect your hotel revenue management forecasting and details on if/how they can impact your revenue:
1. Dirty Data
Leading scientists refer to 'regrets and denials' data as "dirty data" and incorporating this data into your revenue management system (RMS) can actually end up over-unconstraining demand, leading to over-protecting inventory and reducing occupancy. So why is using this data so ineffective? Systems that incorporate denial data into their analytics from direct websites are only capturing partial regrets and denials data for unqualified transient demand. Using only unqualified transient demand ignores the demand for different market segments and additional channel behaviors.
This becomes extremely harmful because successful unconstraining must include demand from each - and all of - wholesale, group, corporate negotiated and unqualified transient demand.
Blend this in with the potential for questionable data quality due to coding and multiple same person inquiries, and a high look-to-book ratio next to generally flat booking volumes; there are just too many unknowns that make this data unreliable to use in a forecasting model.
2. Weather & Airline Data
It has yet to be proven whether weather and airline data can be used reliably as an RMS input to improve forecast performance. This data may improve the short term demand fit, but only if the data's immediate impact can be assigned to a particular market segment or property. For example, it is conceivable that a hotel based on a remote island in the Pacific Ocean—whose business is tightly tied to airlines bringing guests to the island—will benefit from the additional information that an airline data feed can provide. On the other hand, a large snowstorm in New York can hurt some hotels and benefit others. In other words, while weather and airline data may be impactful to travel patterns on a large scale, their relationship to business or leisure bookings in a particular location is loosely coupled.
As much of this type of data itself is also forecasted, this introduces an additional source of error to the demand forecast. Do you trust your weatherman to accurately predict your hotel forecast?
3. Online Reputation Data
User generated content is reshaping hotel revenue management strategies, with the importance of reputation data growing within the hospitality industry. Access to this data has become easier for hotels and many RMS providers display reputation and rates in relation to their competitors. While displaying this data helps provide decision support, it is even better when this data is incorporated into the demand modeling and optimization processes.
This is a great example of how to use your customer-centric data for forecasting demand as a function of price, when demand is also a function of your online reputation performance.
For other helpful hotel revenue management advice, download the new "Revenue Management Ingredients" eBook here: www.ideas.com/RMebook
IDeaS, a SAS company, is the world's leading provider of revenue management software and services. With more than 30 years of expertise, IDeaS delivers revenue science to more than 30,000 properties in 154 countries. Combining industry knowledge with innovative data-analytics technology, IDeaS creates sophisticated yet simple ways to empower revenue leaders with precise, automated decisions they can trust. Results delivered. Revenue transformed. Discover greater profitability at ideas.com.