Case Study: Using Rate Evolution and Booking Window Analysis to tell a story

In this case study we will be looking at what I think are two neglected metrics: Rate Evolution and Booking Window Analysis to show you why you should:

1.      Price your hotel realistically more than a month out, even with strong base business.

2.      Be more cognizant of pickup in the medium-term window.

3.      Maintain a steady upward trend in your pricing strategy leading up to arrival.

Rate evolution and booking window analysis are two under-utilized tools that every revenue manager can use to gauge the effectiveness of his/her overall strategy- they are versatile because they can be contrasted with your hotel’s basic metrics (such as occupancy and ADR) to create a narrative which better explains the hotel’s performance for a given time period. Rate evolution and booking window analysis adds temporality to what can otherwise seem like cut-and-dry numbers.

Over the next few paragraphs I will show you two nights at a property which had very different rate evolutions and booking window patterns, and subsequently very different overall performance.

Night 1

This particular night was a strong Friday night in March

First let’s start with two numbers that are crucial, but certainly aren’t particularly exciting: for this night the Hotel sold 86 rooms at an ADR of $203

With just these two data points we’re feeling around in the dark, so let’s add in the element of time to see how things developed in the run-up to arrival, which will give us a better sense of narrative.

By adding some booking window analysis for this night, notice how this starts to color the story a bit more vividly:

From the chart above we now know that the hotel only picked up 1 room night in the 4 to 7-day to arrival window and 10 room nights in the 4 to 14-day window. Also, the hotel picked up a staggering 26 room nights in the final day of arrival, which accounted for over 30% of its total rooms sold. Already there is a compelling story here, so let’s see what else the data can tell us.

Sticking with the theme of time and narrative, let’s add in what I think is one of the most crucial elements when analyzing effectiveness of strategy: rate evolution. Notice that this new element, when combined with pickup by booking window, ADR, and Occupancy begins to make the story even more clear.

The most striking take-away of the rate evolution chart above is that the hotel dropped rate a total of 90 dollars in the final 3 days leading up to arrival and ended up $29 below their sell rate 30 days from arrival.

After adding these additional pieces of information, it may not surprise you to learn about the hotel’s final market mix for this night: This night was one of the top 5% nights for Opaque room nights sold, with 33/86 rooms sold to rooms that booked via an opaque channel.

It’s likely that by now you have a strong opinion of whether this was a ‘bad’ or ‘good’ night. If you’re in the ‘bad’ camp, I tend to agree with you, but to reinforce this opinion let’s contrast how this night developed with another night a few weeks later.

Night 2

For the second night let’s start with the basics again: on this night, the hotel Sold 86 Roomsat an ADR of $273.

Although the ADR is objectively higher than the first night we looked at, that still isn’t enough for us to declare this night a resounding success; just like before, let’s see what the time element of the data tells us when we break down the booking window:

Notice that this is very much different from the first night; there are fewer rooms booking 31+ days out, more rooms picking up in the 15-30 window, and relatively balanced pickup from 14 to 2 days out- so much so that the hotel lost a room on the final day before arrival and still sold the same amount of rooms as the first night.

Next, take a look at the BAR rate in the run-up to arrival which is completely different from example 1:

Adding in Rate Evolution analysis for this night, notice that aside from a moderate rate drop 28 days from arrival, the hotel trended almost exclusively upward with its sell-rate! In fact, the hotel was sold out heading into the actual day of arrival, which explains the lack of a data point in the rate shop for the final day (the hotel would go on to lose a few rooms that night and barely miss the sell-out). The final sell rate of the hotel was $129.50 higher than the sell-rate 30 days out.

It may not surprise you when we look at the market mix for this night that we find something interesting, and completely different from the first night:

This night was one of the top 5% for most Retail Rate room nights with 14 rooms sold to just our Best-Available rate plan alone (just 1 rate code!). Also, only 3 opaque room nights were sold.

So what was it about the second night that yielded such strong results, and what can we do to emulate the more desirable performance?

In order to answer this question, I decided to broaden the data set and look at all nights in 2018 that were top 5% producers for either Opaque room nights or Retail room nights, and started to notice a trend:

Surprisingly, nights that were high opaque producers tended to be nights that had stronger occupancy on the books 31+ days out, which likely led to overconfident pricing in that booking window. Over-pricing probably damaged pickup in the crucial middle-term booking window (30 Days to 8 Days). Notice that nights with high-opaque room night production start at a higher sell-rate than high-retail rates 30 days out, but consistently drop rate all the way to arrival, whereas high retail nights can more safely push rate until about 2 days from arrival. There is obviously something very crucial that happens in the medium-term window (specifically the 8-14 day window) that determines the hotel’s future pricing strategy and ultimately market mix. A night with lackluster pickup in this window seems almost doomed to lower rate and rely on less ideal booking channels as a main source of occupancy, rather than a supplemental tool to help execute the perfect sell-out. A hotel’s BAR rate always needs to be realistic, even in the long term (31+ to arrival) so as not to deter retail rated pickup, which later turns out to be a crucial part of market mix.

Knowing this, we can put together three takeaways to help improve your market mix, and push rate rather than drop rate:

1.      Do not price yourself unrealistically high 31+ out from arrival, even if you have above-average occupancy on the books. Rather, yield lower rated business and focus on pushing length of stay.

2.      Be more observant of pickup in the medium-term window (30 to 8 days to arrival). For example, 8-14 days out, pickup should be examined at the reservation-level: what rate plans are booking? what is the length of stay? Are guests still booking the BAR rate? All of these questions should be asked and answered on a weekly revenue call.

3.      Avoid dropping rate on the final day as much as possible. There are all sorts of negatives to having a rate strategy that involves constantly dropping-rate day-of. It can be demoralizing for a revenue manager, send mixed signals to prospective guests, and negatively alter market mix.

If you’re interested in how you can implement these strategies at your property, or want to unlock these kinds of insights, visit our website at www.KriyaRevGEN.com!

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