In this article, we will see how hotel revenue managers leverage hospitality business intelligence from hotel booking pace report to forecast demand more accurately, and optimize revenue with informed decisions.
What is Business Intelligence?
Business Intelligence or BI is the term used to describe sharing of the right information across the enterprise, with the right people, at the right time. BI enables information workers or business users to make decisions and act up on this information in a timely manner. BI measures the success of your business performance. It provides in-depth insights into your past, present and future business activities, so you can answer the questions:
- What happened?
- What is happening?
- What is going to happen?
With the answers obtained, you can decide what you want to happen.
Often times BI is misunderstood to be the set of tools and technology that go into developing the BI solution. However, technology is just one piece needed to complete the BI jigsaw puzzle. BI must be seen as a solution. What makes BI possible as a solution is not technology alone, but your own business domain expertise. Tools and technology are vehicles of delivering successful BI solutions. Technology experts working together with business domain experts make BI successful.
BI for hotels, resorts and casinos
The application of BI within the hospitality industry is not a new concept. Large, successful chains became successful by embedding BI into their business processes and decision making. They are reaping BI’s benefits ranging from higher customer satisfaction, more repeat stays and higher RevPAC (revenue per available customer) to higher profits and record-high RevPAR (revenue per available room).
Why smaller hospitality chains lagged behind in BI adoption
A few ahead-of-the-curve independent chains attempted to deploy BI in the last decade. Some succeeded, some failed, while others failed miserably. Reasons contributing to a failure were numerous, such as lack of vision, lack of technical knowhow, or simply, insufficient business user involvement in the project. Those who eventually saw the benefits, also experienced budget overruns, challenges with user adoption, and sluggish return on investment. Other independent chains stayed away from BI because not only had the technology price-tag been prohibitive, but the talent to deploy and use the technology had also been scarce. Today, although the technology talent is still relatively scarce, the situation is improving with BI technology becoming affordable and mainstream. Nowadays, database vendors package an OLAP (On-line Analytical Processing) server along with their database engine. Moreover, the technology is becoming easier to deploy, and end-user tools’ usability is improving significantly. As a result, BI today is no longer a far-fetched dream for independent hotels and chains, that it was five or 10 years ago.
BI in Revenue Management
In this article, we will see how revenue managers act upon the intelligence and make informed decisions to optimize revenue
On any given day, your goal as a hotel revenue manager is to optimize RevPAR, or in other words, make the most out of every available room in the hotel. For this you typically rely on the business on the books information obtained from the hotel’s reservation systems. With this business on the books information you forecast the hotel’s daily occupancy levels, for instance, by analyzing the peaks and valleys of the business on the books trend line. In addition, you factor in other known or approximate facts such as, actual sales same period last year, average booking cancellation/no show rate, average booking lead time, and so on. Typically, you would do this by daily exporting data out of the reservation system by running queries and tabulating this data in a spreadsheet application. This being a manual process, as you can imagine, not only is such an exercise time-consuming, but it can be prone to errors and limited in functionality. It can take a lot of guesswork and crossing fingers, when making crucial decisions! Such analysis also lacks the single version of the truth, since different revenue managers may use the data differently with varying results and recommendations. Also, when a revenue manager leaves or moves, the knowledge leaves with him/her.
Figure 1: Chart produced from a transactional system, such as a property management system, showing the next 15 days’ business on the books (OTB) as of today and the actual sales for the same period last year (Act LY).
Deeper Insights with BI
A BI solution can automate this process for the revenue managers giving even deeper insights into their business on the books than a property management system (PMS) or a central reservation system (CRS) could. Let’s see how.
The data within a reservation system, such as a PMS or a CRS, is constantly changing. It is the nature and purpose of these systems to process transactions and provide an up-to-the-minute view of any given reservation. BI can collect the snapshot of this information at a regular interval, say, once a day, and feed this data to a hospitality data warehouse. With this information collected, a revenue manager can now have access to not only the business on the books as of today, but also the business on the books as of yesterday, seven days ago, 30 days ago, and so on. This helps her understand the reservation pickup rate or demand for hotel rooms for a given date over time.
Figure 2: Pickup (PU) analysis for the next 15 days, showing business on the books (OTB) today, along with pickup since yesterday, seven days ago, and 30 days ago.
Similarly, in addition to the actual sales for the same period last year, a revenue manager can now analyze the business on the books for the same period last year, as of same day as today last year. This is commonly known as the booking pace analysis. With the booking pace analysis, a revenue manager can clearly identify “how many rooms did I have on the books on the same day last year, and how many rooms did I actually sell, for the same period last year?” In other words, “How many rooms did I pick up between today and a future day, during the same period last year?”
Figure 3: Pace analysis for the next 15 days, showing business on the books (OTB) today, along with difference between business on the books and actual sales for the same period last year. The combined totals provide a demand forecast for this time period.
Improved Hospitality IQ: Profitable Decisions
With the added knowledge of the pickup from the recent past as well as the pickup for the comparable period last year, revenue managers can forecast demand more accurately. Moreover, you can immediately identify any major dips or surges in the demand, investigate them further, and fix the problem or seize the opportunity early on. The automated loading and processing of the data from the reservation systems into the data warehouse provides much-needed consistency, efficiency and accuracy. A well thought out BI solution that is implemented right can free up valuable time of employees and managers who can focus on using the information to make better decisions and take timely actions.
Hotel Revenue Managers Forecast Demand with Improved Hospitality IQ (HospitalityNet, March 2008)