As was discussed in our Revenue Management Foundations Video, complete the calculations found on the attached worksheet and then take a photo of your complete work and upload it here.
Examples of revenue management
The hotel industry is a sector which routinely makes use of revenue management strategies, artificial intelligence and the analysis of big data. We will now take a look at two examples where revenue management can be used to maximize income.
Local concert increases demand for hotel rooms
As there is always a fixed level of accommodation in any one area, local events such as music concerts can lead to a significant spike in demand. Using historic data, taken from previous events, and real-time data, hotel companies are able to accurately predict the correct pricing point for their capacity. For example, let’s assume there is a hotel with 50 rooms and a daily room rack rate of $300. As a consequence of increased demand for the local event, it may be possible to charge $350 per room.
The figures for this particular scenario are as follows:
50 x $350 = $17,500
If we compare this to the normal room rack rate, the traditional yield would be:
50 x $300 = $15,000
So, as a consequence of predicted demand for local hotel accommodation due to the concert, this creates an above average yield:
($17,500/$15,000) x 100 = 116.66%
This is a perfect example of taking into account local events, predicted demand and using real-time data together with historic data to calculate the value tipping point.
Increasing revenues during quiet times
No hotel will ever be fully booked all of the time, but with there are ways and means of adjusting pricing in real-time to maximize revenue. Let us assume that on a relatively quiet day a hotel would normally attract 20 customers leaving 30 rooms free:-
20 x $300 = $6,000
This equates to a yield of:
($6,000/$15,000) x 100 = 40%
However, what if historic data suggested that a reduction in the room rack rate to $250 would attract 35 customers as opposed to 20? The income calculation is very different:
35 x $250 = $8,750
This equates to a yield of:
($8,750/$15,000) x 100 = 58.33%
This is a perfect example of using real-time data and artificial intelligence to find a new price point in the quiet times. The overall yield is increased from 40% to 58.33% simply by reducing the room rate from $300 down to $250.
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