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Wednesday, 13 August 2014

Business Intelligence Provides Answers to Hotel Revenue Management Questions

Hotel Revenue Management should be a data-driven discipline, an art backed by the science of business intelligence. Practicing revenue management as such, supported by concrete facts, will help both revenue management and business intelligence to become less mysterious, better understood and more highly regarded within the hotel industry.

The discipline of hotel Revenue Management and the practice of Business Intelligence (BI) are complimentary in many ways. Revenue management is largely about understanding hotel and market business trends and optimizing hotel performance based on a projected set of conditions. It is a “question-based” discipline, putting forth such musings as how much a hotel can charge on a given day, how many roomnights of a certain market segment will produce over a given period, or how many group rooms a hotel should accept several months in advance. Business intelligence is about engineering an environment of answers – ensuring that business processes support a high quality of resultant data, and providing the technological means for those data to be analyzed efficiently and effectively. Business intelligence is factual – it is an “answer-based” practice. Business intelligence helps to answer the questions that revenue management asks.
A less fortunate commonality of these two areas is that they are still largely misunderstood – and therefore underappreciated – in the hospitality industry. This can lead some to view them as mysterious or even unnecessary practices despite the fact that each (and especially a tandem of both) can improve hotel revenue performance significantly. To dispel some of this mystery, and to illustrate clearly how hotel business intelligence can support the practice of effective revenue management, let’s take a look at a few of many possible use cases of BI in this area:
Understanding the lead time of retail business
An important concept in hotel pricing is understanding when reservations are booked relative to their arrival date – the “lead time” of bookings. This is a very simple calculation for any single booking, yet doesn’t lend much insight until lead times are aggregated across a given sector of business – say a certain rate program, market segment or geographic source market. For a hotel that offers a publicly available “Advance Purchase” rate for instance, knowing definitively the lead time of full-rated retail business (such as “Best Available Rates”) allows the Revenue Manager to create a more effective price structure for the hotel. For example, if the lead time on the majority of full-rated retail business is known to be within 30 days, then an advance purchase rate that is available until 14 days prior to arrival warrants scrutiny. “Buy-down” is likely occurring in such a scenario, and the lead time restriction on the advance purchase rate should consequently be moved outside of the retail booking window (to 30 days or more). This is a simplified example, but the point remains: Business intelligence, properly architected, would provide for such an insight by supplying the analytical horsepower to aggregate lead times across thousands (or hundreds of thousands) of bookings.

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