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 questions like how much a hotel can charge on a given day, 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.
An unfortunate 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 an “Advance Purchase” rate for instance, knowing 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 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 structured, would provide for such an insight by supplying the analytical horsepower to aggregate lead times across thousands (or hundreds of thousands) of bookings.
Determining Realized ADR Premiums on Room Type Classes
Most hotels have differentiated room type inventory across two or more different classes of room types for which they charge different rates (Standard rooms, Deluxe rooms, etc.). The price difference between these room type classes is clearly established and the hotel’s rates are loaded accordingly across all distribution channels. Yet most hotels aren’t aware of the magnitude of difference between these listed premiums and what actually materializes in their results – the realized ADR premiums across their different room type classes. The ability to accurately measure and monitor realized ADR premiums is paramount to effective Revenue Management, and the further ability to view these premiums for different sub-sets of business (by market segment, booking channel or loyalty membership status) is critical to understanding a hotel’s inability to realize established premiums. Such issues can be complex, involving a spectrum of elements (inventory management issues, competitive pricing pressures, operational concerns, etc.). Key is the ability to conduct multidimensional analysis quickly and easily to test theories and identify trends, and this is a basic requirement of any Business Intelligence capability.
Considering Account-Level Displacement
Above and beyond the total production of any negotiated account, it’s important to understand the opportunity cost of having accepted that account’s business on each particular stay date across a given period. The Revenue Management question at play here is: “Did accepting room nights from this account cause us to turn away any higher-rated business over these dates?” This is a question that applies only to high-capacity dates (sold-out or almost sold-out), which immediately points us in the direction of Hotel Business Intelligence. A core capability of any BI environment is to isolate such granular sub-sets of data. In this case, we can formulate a query such as “show me production from company X, but only on nights that meet this criteria of being sold-out or almost sold-out”. A simple comparison of the account’s production on these nights relative to their overall production may be revealing. An account with low overall production and a large percentage of that production falling over sold-out nights may be using your hotel as a backup choice in the marketplace. The overall value of the account can now be calculated, factoring in any displacement of full-rated business over high-capacity nights. Again in this instance, Hotel Business Intelligence provides a compelling answer to a Revenue Management question.
Hotel Revenue Management should be a data-driven discipline, an art backed by the science of Business Intelligence. To the degree that BI supports the Revenue Management process, many perplexing questions can be answered quickly and accurately. Practicing Revenue Management as such, supported by concrete facts, surely will help both Revenue Management and Business Intelligence to become less mysterious, better understood and more highly regarded within the hotel industry.