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Are you on the right curve? A useable metric

Whether we know it or not, we all use booking curves to some degree.  Some of us pluck figures out of the sky and use a ‘guest-i-mate’ but the hotels that benefit the most are the ones that have a good understanding of their business and will measure using detailed booking curves.

So lets take an easy example and look at one segment.

Lets imagine we have a 310 bedroom hotel in a city centre and we want to measure how our local corporates are impacting our business.  We start by tracking:

Days before arrival -1 0 7 14 21 28
01-May 145 150 85 60 30 20
08-May 172 175 120 80 30 10
15-May 195 200 145 55 30 10
22-May 160 170 100 85 20 10
29-May 117 120 80 55 30 5
Average BOB 158 163 106 67 28 11
% BOB 97% 100% 65% 41% 17% 7%
  • So in this example we look retrospectively at May
  • We look at Mondays being 01/08/15/22 & 29 May
  • Then we look at how many rooms we had booked 28/21/14/7 days in advance as well as 0 days (being the day of arrival). Of course in a real situation you would of course look much further out than 28 days but for this example, lets keep it simple.
  • You will see that I have also added -1 day being the day after arrival.  This is crucial to measure as it will allow you to see how many no-shows or cancellations you had on the day of arrival.

In this example we record how many rooms we have sold from our corporate segment rate code on given days and we measure this for all Mondays in May. So in the above example 7 days out we had 85/120/145/100 and 80 rooms sold for all Mondays in May.

  • Then we average our rooms so 7 days out we had an average of 106 rooms sold.
  • To show the impact as a percentage, we need to take our 106 rooms and work out what percentage this is compared to the number of rooms we usually have on day zero (or the day of arrival)
  • We do not in this case, take the 106 rooms and work out the percentage against the 310 rooms we have in our building as we are trying to work out purely how this one segment behaves – not how the segment behaves compared to our total room stock
  • You will also note that in this example we can see that on average 3% of the rooms that were due to arrive from that one segment actually cancel or no-show on the day.  This is because there is a difference of 158 rooms that actually materialised compared to 163 that were booked.

If you want to see this visually, use Excel (and each persons software programme might be different so I won’t attempt to run through step-by-step on how to add a line graph).  But you can easily insert a graph that will show you visually by actual number of rooms or percentage.

I find that this is a really usually metric as if you track properly, you can then see how Mondays in May compare to Mondays in April as an example.  Or perhaps how Mondays behave compared to Thursdays.

You can of course use this to track against your total inventory or ‘whole house’ but the metric I find most useful is how one segment is actually performing against itself. So in the above example, we know how many rooms we should expect 7, 14, 28 days out and if we are ahead or behind on our trends for that day.

We can also use this to understand how many rooms ‘wash’ from that segment and this could be used very successfully in an over-booking strategy.

If you do want to measure the impact of this segment against your total number of rooms, then just add a new row and work out the percentage against 310 rooms.  You can of course see that the values change and that the -1 day value changes as well (which is why I feel measuring against itself is a much more useful metric)

Days before arrival -1 0 7 14 21 28
01-May 145 150 85 60 30 20
08-May 172 175 120 80 30 10
15-May 195 200 145 55 30 10
22-May 160 170 100 85 20 10
29-May 117 120 80 55 30 5
Average BOB / Segment 158 163 106 67 28 11
% BOB / Segment 97% 100% 65% 41% 17% 7%
% BOB/ Whole House 51% 53% 34% 22% 9% 4%

We all have a multitude of data at our disposal and for many of us, the sheer amount of information and the prospect of tracking it can be daunting.  If you can, invest in good reporting software or better still a good revenue system to do this for you.

The more information you have on your customers behaviour the better informed you are on how to manage that business.

I hope you find the above useful and remember for all things revenue just ask@rightrevenue.wpengine.com