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Price Ladders: How Much Revenue Are They Losing You?

Remember when "New Coke" came out?

(If not, you’re still young, and I’m not jealous of your metabolism at all :)

New Coke sounded like a good idea at the time. But this too-sweet Pepsi knock-off has gone down in history as one of Coca-Cola’s biggest money-torching blunders.

Well, get excited, because revenue management has its very own New Coke: price ladders!

What’s a price ladder?

It’s when you wait for occupancy to reach a pre-determined level (like 50%), and then increase price a pre-determined amount (like $5).

(If you have to ask what a price ladder is, it obviously hasn’t gotten its razor-sharp hooks into you yet – very good! Now run for the hills while your STR rank still looks good. Better yet, schedule a demo of our innovative new RMS: N2Pricing).

Why is it a terrible, horrible, no good, very bad idea that the hoteliers should avoid like their careers depend on it?

Price laddering is a surefire way to lose revenue (emphasize: not a great buy for revenue management leaders)!

Here’s how price laddering sabotages you…

Price Laddering in Action

The key feature of price laddering is:

  1. Waiting for occupancy to materialize to increase price

In the price laddering example below, Hotel Beta waits until it reaches 50% occupancy, then increases price from $45 to $50.

Then, Hotel Beta waits until occupancy reaches the next threshold (60%) before increasing price a little bit again – from $50 to $55.

And so on. Until Hotel Beta ends up with 90% occupancy, charging $70 BAR on the last five rooms to book.

And this price laddering strategy nets Hotel Beta: $4,900 in revenue.price-laddering-01Price laddering is a simple construct that’s easy to understand. And, supply and demand principles are clearly at work (as rooms become more scarce, their price increases), which is great.

So what’s not to love?

Predictive Pricing at Work

For the answer to that question, let’s turn to another example, in which Hotel Beta’s biggest competitor – Hotel Alpha – is using a ‘predictive pricing’ approach instead of price laddering.

The key feature of predictive pricing is:

  1. Predicting occupancy and raising price immediately

price-laddering-02In this example, Hotel Alpha accurately forecasts in advance that they will reach 90% occupancy. Based on that prediction, they start charging $70 BAR (the max BAR Hotel Beta reached) on the very first room rather than wait until there were only 5 rooms left.

The result?

Hotel Alpha ended up achieving 90% occupancy, just as Hotel Beta did. No difference there.

But Hotel Alpha made $6,650 revenue, which is $1,750 (36%) more than Hotel Beta, which only took in $4,900 through their price laddering scheme.

Hotel Alpha made more because they didn’t wait to see what occupancy materialized before they set the right price. Using prediction, they applied the correct price from the beginning, which netted them more revenue on the first 80% of all bookings.

And this is only from the power of a forecast. Just think what could be achieved with a true RMS – like N2Pricing – which also factors in critical data from the competitive landscape and customer price sensitivity!

The Real Cost of Price Laddering Tools

The revenue numbers in these examples -- like $6,650, $4,900, $1,750 – in are teeny tiny. But the number to pay attention to is 36%.

Hotel Beta made 36% less revenue than their competitor–Hotel Alpha—by relying on a price laddering scheme.

That kind of loss is sure to leave a bad taste in the mouths of owners at Hotel Beta – like New Coke did.

The worst part is it’s completely unnecessary.

The good news is it’s completely fixable.

Schedule an N2Pricing demo today to see how much revenue you’re losing and how much you stand to gain.

 

Tess McGoldrick

Tess McGoldrick is a Director at Revenue Analytics. In this role, she leads cross-functional project teams to develop high-impact solutions, leveraging Artificial Intelligence (AI), to drive organic revenue growth for her clients.


Tess McGoldrick is a Director at Revenue Analytics. In this role, she leads cross-functional project teams to develop high-impact solutions, leveraging Artificial Intelligence (AI), to drive organic revenue growth for her clients.

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