Among the many things preoccupying hoteliers let us look at the adequate interpretation of sales predictions and getting the most out of comparing results between years. The goal of these operations is to improve the reactive response when faced with a given situation or, on the long-term, to define realistic objectives. While statistical analysis tools of prior dates (ADR, REVPAR, REVPAC, segmentation, etc.) are generally well grasped, the same cannot be said of future projections.

 

We are commonly asked this type of practical question: “We are presently in October 2013. My occupancy predictions for July 2014 is of X %. Am I ahead or behind in relation to last year?

 

It’s easy to know, with marketing reports, what the final occupancy was for last July (2013). Although this information is useful it only provides a part of the information necessary for the user to make a proactive decision. In fact, we are unable to follow the daily evolution of those numbers until the month’s end and its final occupancy. The more pertinent question is: In October of last year (2012), what was my prediction for the following month of July (2013)?

 

Providing this type of information may however present a technical challenge since an “image” of previous predictions must be preserved without being erased by actual numbers over time. To avoid this, we have integrated into Hotello an automatic journal system for the night audit. This functionality has been refined so as not to interfere with the output and the size of the date bases.

 

Results obtained thanks to this new functionality are then integrated in the Yield Management module. These are represented numerically and graphically at the bottom of the chart.
Here is a concrete example:

 

Date of audit: 07/11/2013

 

Looking at predictions for 07/12/2013

 

Let’s examine the significance of these results for the occupancy rates. The same reasoning can then be applied to the ADR and the REVPAR. An effective analysis considers these three factors (rate, ADR, REVPAR) in parallel.

 

The number in black tells us that as of today, November 7, 2013, our prediction for Saturday December 7, 2013 is 98.95% occupancy.

 

The number in blue tells us that last year, November 7, 2012, our prediction for Saturday December 8, 2012, (we must compare dates of the same day of the week) had been 30.53% occupancy.
The number in red tells us that our final occupancy rate for Saturday December 8, 2012, was 100%.
INTERPRETATION OF THE DATA

 

If we compare our prediction for the current year vs. our prediction of last year, our establishment is ahead 68.42% (98.95% -30.53%).

 

• Occupancy 08/12/2013 = 98.95%
• Occupancy 07/12/2013 = 30.53%

 

If we compare our prediction for the current year vs. final occupancy last year, we see that our establishment is only 1.05% short (100% – 98.95%).

 

• Occupancy 08/12/2013 = 98.95%
• Final occupancy 07/12/2013 = 100%

 

If we compare our prediction of last year vs. final occupancy last year, we see that despite having only 30.53% of confirmed reservations one month prior, the final occupancy for Saturday December 7, 2012, was 100%. That means 69.47% of reservations were taken over the previous month.

 

• Occupancy 07/12/2013 = 30.53%
• Final occupancy 07/12/2013 = 100%

 

It is nevertheless important to note that cancellations (as with new reservations and modifications) affect occupancy. Mass cancellations may result in a much greater number of vacancies on a day that had previously had strong predictions.

 

Decision aids

 

Short-term reaction

 

If the numbers show our establishment is lagging we can launch a last minute promotion. Conversely, if we’ve already attained our objectives we can raise rates, require more restrictive reservation rules such as longer minimum stays and increase profits.

 

Long-term reaction

 

By observing the evolution of our predictions for the coming year, we can refine our promotional campaigns. By comparing our predictions for the coming year to the final occupancy rate, we can adjust objectives and follow their course. This information is invaluable when deciding whether to increase or reduce spending in a particular publicity scheme.

 

An analysis of predictions and final occupancy in relation to the date of audit allows us to predict the average period needed to reach our final occupancy. For example, we can evaluate the number of days for the predictions (blue) to reach 80% of final occupancy. This will aid us in making other extrapolations.

 

The Yield Management module is therefore an indispensable complement to Hotello which allows you to increase your productivity. Thanks to several data filters, this module can also become an essential ally when negotiating with Web distribution partners (Expedia for example) to whom you allot only a portion of your inventory. In fact, thanks to filtered results, you can better evaluate the number of rooms you should put online versus how many you should sell through your own channels at a higher rate.

 

In any case, the final decisions are taken by the manager. This fast, user friendly and powerful tool offers you indispensible information, allowing for enlightened decisions and a proactive approach. You can now anticipate market fluctuations rather than being subject to them.

 

Chief Operating Officer,Product Manager

Analyst