S1:E2 Rate Evolution

Dive into the complexities of rate evolution with Daniel Foreman and Brandon Burtis from Pacifica Hotels. This episode demystifies dynamic pricing strategies, showcasing how adjusting room rates in real-time can drive significant revenue growth. With practical scenarios, you’ll learn how to set and modify rates to stay competitive and maximize profits, turning rate management into a powerful tool for success.

In this episode, I was joined by Brandon Burtis, VP of Revenue Management at Pacifica Hotels, the largest owner/operator of boutique hotels on the West Coast!

If we’re going to land the figurative plane smoothly, let’s start by learning a key term we’ll need on our journey. That term is Rate Evolution, which is simply how room rates change over time.

Rate evolution can be analyzed for a single hotel, for an entire market, or the entire industry. For example, check out our latest report on the effect the Olympics are having in the Paris market, specifically this chart:

This is a great example of market-wide rate evolution which shows how rates “evolve” over time – the X axis is “days to arrival”.

Like in Part 1, I’m going to start by showing you a few conceptual rate evolution curves. As soon as you see them you should be able to understand what’s going on with each (but don’t worry, if not I’ll add some details).

So, let’s play out a little scenario…

Say you’re a revenue manager at a successful hotel. You are in a dynamic market with an active event calendar; it’s a routine day for you and you’re working away diligently when all of a sudden you get an alert.

"ping'"

A big event was just announced. It’s a major demand driver and will be happening roughly a year from now!

Use your imagination here, maybe it’s the Superbowl, the Big Tech Corporate Conference ™, or maybe a big bird-watching convention (who am I to judge). The point is, this will be a really major event!

Let’s say that we get a bit over-aggressive and crank up our rate through the stratosphere to $1000 (meanwhile, our compset may only be somewhere around $500).

The Greed Curve

What happens when we price too high?

This is a phenomenon I’ve discussed in a recent article about common mistakes in revenue management.

Oftentimes, we as revenue managers will either throw out a “dummy rate” to guard inventory that is too high, or we’ll simply price in a completely unrealistic way in the hope that we’ll magically have strong pickup.

From experience, here’s what happens when you take this approach. Take a look at the rate evolution curve below and notice what’s happening here:

Notice that our hotel stays at an unrealistic price point for a very long time. Meanwhile, the compset has a more reasonable sell rate.

As time goes on a lack of pickup forces us to drop our rate last minute to try and drum-up occupancy, which is almost always a losing strategy.

Also, notice that the compset gets a chance to push rate in the final days leading up to arrival, which is a benefit of having a logical pricing strategy (they were layering in good base occupancy while we were turning guests away!)

I think we can all agree that what I call the “greed curve” above is a sub-optimal strategy.

In my podcast interview with Brandon, he made a great point about the “greed curve” - usually when revenue managers make this particular mistake, it’s due to blind optimism and a misunderstanding of how much demand there is:

I like Brandon’s point here because even the best of us can make these optimistic mistakes from time to time, and often not accurately gauging demand is a big culprit.

Remember the Paris Olympics chart I showed you at the start of the blog? Well, as it stands right now, the Paris market is very much stuck in the “Greed” curve, likely for the reason Brandon mentioned!

The Underpriced Curve

Let’s play back this scenario yet again, but now let’s take a completely different approach…

"ping"

Now, the big event gets announced but this time rather than overprice, we leave our rate too low.

This is the other end of the spectrum, which we can call the “Underpriced” curve.

Often, this rate evolution pattern can happen when a hotel misses an event announcement or underestimates strong demand indicators.

I even added a few “x’s” in the curve to simulate the very likely sell-outs that would occur if this were to be a real example!

Speaking from experience, this curve is every revenue manager’s worst nightmare!

Notice after selling out too early, the compset can continue pushing rate and enjoy absorbing all of the demand that the example hotel wasn’t able to take at much higher rates.

In the podcast episode I got a chance to ask Brandon if he had ever experienced the “Underpriced” Curve, here’s what he had to say:

Another great point from Brandon, because he’s certainly right: the few times where I made this mistake myself was when an event was announced that I just wasn’t ready for.

For example, a few years ago, a new music festival in Austin had its first year and the hotel I was working with sold out in about 24 hours, over a weekend! It still gives me sleepless nights.

So what does a good rate evolution curve look like, you ask?

The About-Right Booking Curve

Well, let’s play our scenario back - we’re a revenue manager working away at our dynamic hotel without a care in the world, and then…

"ping"

We get an alert about the big event next year.

This time, however, we've played our cards right.

By using a mixture of rate shopping tools, demand analysis tools, maybe a BI solution, or even a benchmarking tool, we push rate as we get closer to arrival in a logical way.

If you want to see a real-life example of what this curve looks like, let’s turn our attention to a real-life hotel using Rate Insight.

This is a hotel in a very dynamic market that had a very strong rate evolution curve that caught my eye right away.

^{A real-life example showing the about-right pricing curve}

Our example hotel is shown in dark blue, and the rest of the compset is shown as well.

While there is no “perfect” rate evolution curve, this is more of a rate evolution shape that revenue managers should be striving for.

The hotel’s final sell rate was much higher than it was at 30 days out, with a strong upward trend in price over time.

^{A real-life example showing the booking and pricing curve}

To illustrate the booking curve and pricing curve working together; Here’s the corresponding booking curve for this same night, again shown in Rate Insight’s ‘pace’ chart.

This was very much a stable booking curve with the only major jump happening right at the 30-day mark - what I find interesting is that our Revenue Manager still stuck to their guns and didn’t overreact when they experienced this jump in occupancy!

Turning our attention to the compset - I will give them some credit - this market seems to have been pretty disciplined in the run-up to arrival. Only 1 hotel is dropping rate in the final 2 days leading up to arrival. Our light lavender competitor sells out about 8 days in advance, but as we’re learning in this series, that’s not necessarily a bad thing.

Back to our hotel, pricing this way must’ve taken some guts to do, but it paid off. The hotel continued to build steady base occupancy, and the Revenue Manager really started to maximize on rate in the final 9 days leading up to arrival.

In those final 9 days, the hotel goes from 75% to a perfect sell-out on the day of arrival.

A round of applause for our diamond-hands revenue manager ladies and gentlemen!

Key Takeaways

In addition to simply pricing, in my interview with Brandon, he touched on lots of other great topics around maximizing revenue, executing the perfect sellout, and how to analyze the data to arrive at the right price. At Pacifica, Brandon puts huge emphasis on:

• Analyzing booking windows by channel and segment, which in turn allows to be able to capture those guests at the highest possible ADR, when they are at the peak point in the booking pattern.

• Layering in other segments and promotional strategies with a compelling price, such as non-refundable advance purchase to help balance possible cancellations in other segments.

• Offering other ancillary opportunities beyond just the base rate all along the guest journey to maximize revenue (packages, amenities, etc.)

If you haven’t already, be sure to listen to the episode!

So let’s round things out for this episode, and keep things simple as I think there is a very obvious exercise we can all do to become better revenue managers - try this:

Log in to Rate Insight, and run a rate evolution chart for 5 or 10 of your recent technical sellouts (nights above 95% occupancy).

Analyze the shape of your rate evolution curve - are you a Greed Curve revenue manager? An “Under-pricer”, or are you already pricing about where you should? Is the shape of your rate evolution curve explaining why you missed the sellout on some nights?

When writing this blog, and scripting the podcast, the two tools I relied on most heavily for this episode were Business Intelligence and Rate Insight.

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