S1:E3 Market Segmentation

Daniel Foreman, alongside Nick Larsen and Kim Welch from Schulte Hospitality, explores the art and science of market segmentation. Discover the importance of identifying and targeting distinct guest segments to refine your booking strategies. This episode guides you through balancing your market mix, tailoring offerings, and driving higher revenue through strategic segmentation, empowering you to make decisions that enhance your market positioning.

Demand drivers are a major factor, but also when guests book, and how we price along the way determines our ability to stick the landing and get that perfect sellout. But this isn’t the full story - It should come as no surprise that market segmentation is a major variable that can be analyzed and modified to help us consistently reach 100% occupancy more often.

So how do you establish these market segments in the first place?

Market segmentation can really be a fine art as much as a science because segmenting our guests is very subjective.

Market segmentation using rate plans in revenue management

If you want to know how revenue managers and major management companies do it, I’ll try to establish some very general rules but keep in mind there are no hard and fast rules here!

That said, typically in the world of hotel revenue management we segment guests by the rate plans they book. A rate plan contains lots of little kernels of information - hints at the channel, discount percentage, whether the rate is associated with a package, and sometimes a length of stay element.

I’ll spare you the tedium of how exactly these rate plans come to be in the CRS and PMS, but here are a few hypotheticals: a guest might book an advance purchase OTA rate plan at a 15% discount. The rate plan might be something like ADVOTA15.

Or a guest might book a stay 3, and get a free night package with the rate plan, PKG3F.

A guest may be here on business, on a locally negotiated rate with ABC corporation, L-ABC10.

While this doesn’t tell us whether the guest likes a firm or soft mattress, or what kind of music they listen to - segmenting guests by rate plan allows us to see those trends and patterns in booking/cancellation data, identify how far in advance certain guests tend to book, understand how long they stay on average, and very importantly know what their ADR, or average daily rate is.

We can then combine these rate plans with other rate plans that are similar enough to create a ‘segment’, and we can then analyze our room night production in this way.

For example, the PKG3F1 rate plan might be lumped together with 10 other package rate plans to create a PACKAGE segment that we then track. From there we can make statements such as:

“Our package segment has a 4.3 night average length of stay year to date”

To expand on this a bit more, here’s a look at the accounts module within Lighthouse BI to give you an idea of what metrics revenue managers are using to evaluate different market segments.

Here, we’re looking at year-to-date production filtered by just market segment. Keep in mind each segment contains dozens of individual rate plans.

Keep in mind that your segments may look entirely different than this, and if you were to run the same report on your portfolio, the results may be quite different!

Market segment analysis in revenue management

Doing even some basic market segment analysis we can see that market segments behave in very different ways, for example at this hotel, guests that fall into the “Consortia” segment have a relatively high ADR, and one of the longest average lengths of stay of any segment (The “Consortia” segment includes 44 individual rate plans, by the way).

By contrast, "employee and redemptions" have the lowest average rate and shortest length of stay.

When optimizing our mix over a sellout date, we would want to incentivize Consortia bookings, and as far as we can, dissuade the more unfavorable segments such as employee and redemption.

But what if we were aiming to sell out for a Saturday night? Doing deeper analysis, Saturday nights are actually the least popular day for Consortia business, meaning that we would be hard-pressed to rely on this segment to fill our hotel on a Saturday night.

When analyzing "employee and redemption", however, Saturday night is by far the most popular night, with many 1 and 2-night guests staying on this day of the week. Unsurprisingly, if we really wanted to fill our hotel with these guests on a high-demand Saturday night, we could very easily do so, but with very suboptimal revenue results.

Yield strategy

This is representative of the constant push and pull, and balancing act that we as revenue managers must do when building our market mix for a sellout.

Almost always, a revenue manager’s imagined “ideal” market mix simply doesn’t exist in the real world; we have to maximize revenues with the demand segments we’re given by constantly testing different price points, restrictions, promotions and sell strategies to attract those guests that will give us the best chance to sell out in the most profitable way possible.

This is a natural seguë to the concept of yielding. Yielding is the actual incentivizing and dissuading certain segments from booking.

An important note: when we dissuade certain segments from booking, we don’t necessarily want to turn away the guests themselves but instead want them to book in a certain way.

If we can incentivize a guest to book a retail rate instead of a deeply discounted rate with a high channel cost, then our yielding strategy has succeeded.

As a general rule of thumb, in scenarios where we know demand will be extremely high, we would want our business mix to draw from those segments that are most desirable (most beneficial stay pattern, longest LOS, and highest ADR to name some of the attributes), and yield out segments that are least desirable.

Take a look at the graphic for a visualization of how a hotel might yield different segments in different demand scenarios:

Note that yielding isn’t an all-or-nothing exercise - a rate, or a segment isn’t just open or closed, we can use all sorts of controls to influence guest booking behavior and make our sellout more likely and more lucrative - we can add minimum length of stay requirements, maximum length of stay requirements, close-to-arrive, or use full pattern length of stay restrictions to influence our market mix.

Remember our example sellouts that we looked at in part 1? (Our football game, and then an ordinary midweek night). This was a great (albeit extreme) example of the effects of yielding on segmentation.

Demand for the football game was so high that we could afford to completely yield out entire segments that we felt didn’t deliver the ADR premium we desired. This led to room night production in only 6/13 segments.

Meanwhile, the more routine weekday sellout saw production from 12/13 segments, reinforcing that we were indeed less restrictive.

OK, so we know about market segments, how to do some basic analysis with them, and how we can “yield” them to influence which market segments book - but how do we turn this into more consistent sellouts?

Remember what Brandon said in Episode 2 when he mentioned capturing guest booking intent at the right time by segment?

This is a perfect example of why we segment guests as it allows us to fully customize entire strategies just based on certain types of guests.

But turning back to the data, this is where having more robust analysis tools can be your best friend.

Lead time, stay pattern and ALOS are 3 metrics that you can start analyzing right away to make a perfect sellout more likely.

I asked Nick what he looks for when performing market segment analysis:

Nick even takes it a step further and incorporates ancillary spending, which is indicative that they are doing some really deep analysis on the market segmentation side that extends even beyond what we’ll have time to cover in this blog.

If you have a potential sellout coming up, analyze what kind of sellout it’s likely to be - a corporate Wednesday? Adele's concert on Saturday?

Review past, similar sellouts and the market mix you experienced - it’s OK if it wasn’t optimal, but patterns should emerge.

With enough observations, you will hone your abilities, and, in time, you should get a feel (and some hard data) about when certain segments book, their stay pattern, and average length of stay.

In my interview which you can hear in the podcast I specifically asked Kim how they used market segmentation analysis in their sellout strategy at Schulte, she had a really good response that ties in demand and forecasting into this larger conversation around market mix:

Here’s an example recent sellout shown in Revenue Insight.

Key Takeaways

• Try forecasting demand by segment - if you’re already forecasting great!
  But can you take it a step further? For your next potential sellout, try to separate which segments you think will be contributing to demand. Take note of how your forecast changes and what effect this has on your forecast accuracy.

• Look at a recent sellout where you buttered the bread, and analyze your market mix (this will be a breeze if you have Revenue Insight).
  Next, look at the lead time for each of these segments. Where did they book along the booking curve? For example, with a hotel I looked at when writing this blog, I noticed that 0% of their Retail rooms were booked more than 50 days out; 50% of that production was booked within the final week leading up to arrival!

• Do a similar analysis to gauge ALOS for some of your top segments for a recent sellout. Were there any segments that may have been detrimental to surrounding shoulder dates? Any extended stay guests you wish you had taken more of?

When writing this blog, and scripting the podcast, the tool I relied on most heavily for this episode was Lighthouse Business Intelligence.

If you’d like to learn more about this or any of our other solutions, please reach out here!