# Rate Optimization: Enhancing your hotel’s pricing strategy

Ravi Mehrotra

IDeaS

source: here

IDeaS is probably the biggest and the most known company providing software and services for revenue management. I used their tools previously in my work but all I saw was front-end user application. Therefore, when I found the article regarding rate optimization written by Ravi Mehrotra IDeaS , the CEO and founder, I’ve decided that I need to read it.

In order to maximize company revenue and profits, revenue managers have to firstly understand the demand characteristics of their products, understand the price sensitivity of demand and design appropriate rate spectrum. To ensure that they capture the maximum revenue at all times, they use rate optimization algorithms to choose rates based on historical price sensitivity of demand.

The price sensitivity of demand is a measure as the change in demand to change in price. The demand is elastic when the small change in price is accompanied with large change in demand. The demand is inelastic in opposite situation when even large change in price is accompanied with small change in demand. It is so important to understand the price elasticity of demand, because such a knowledge will enable us to predict the change in demand when we will change the rates and as result choose the rates that will maximize revenue. Price Elasticity of Demand is usually calculated as percentage change in demand to percentage change in price:

The other interesting indicator is price elasticity of revenue, which measures the effect of price changes on revenue. Of course revenue is a function of the demand and price, therefore price elasticity of demand might be represented as:

and the relationship between Price Elasticity of Demand and Revenue can be written as:

Therefore, price elasticity of Revenue has much higher practical implication, because it explains the change in the most important figure in Revenue Management – Revenue.

In order to further explain this concept the author created following example. There are 4 scenarios in hotel with 100 rooms:

• Price – \$0 -> 100 rooms sold -> \$0 revenue
• Price – \$40 -> 60 rooms sold -> \$2,400 revenue
• Price – \$60 -> 40 rooms sold -> \$2,400 revenue
• Price – \$100 -> 0 rooms sold -> \$0 revenue

It looks like, the maximum revenue (\$2,500) would be at price \$50. It can be also calculated using previously showed equation where at the price of \$50, the price Elasticity of Demand is -1 and the elasticity of revenue is 0. The value 0 indicates that the revenue will decrease with any change in price, therefore \$50 is optimal. To better understand this example author prepared following graphs.

It is important to mention here that in this example author used linear function, but in real world the relationship between price and demand is non-linear and also there are a lot of other factors that affect the demand. The changes in price might be illustrated as moving on the demand curve. The external factors, on the other hand, can affect to move the entire demand curve. The determinants of demand might include: “consumers’ preferences, income, economic and market conditions and price of substitute goods.”

So in order to maximize the revenue, following steps should be taken:

1. Gather historical data (including occupancy, capacity, rate info for various products) – from at least 12 months
2. Analysis of demand and occupancy trends and patterns
3. Evaluate price sensitivity of demand and define rate spectrums
4. Structure appropriate rate levels relative to market segment.

In the end, I would like to quote Ravi Mehrotra who summarizes the benefits of understanding the demand.

By understanding the price sensitivity of your business, your published rate plans are now more intelligent because they are based on analytics rather than gut feel or rules of thumb. Rate optimization will keep intelligent hotels form being dragged into constant price wars with their less analytically-savvy competitors.

My comment:

The author stated that the equation for price elasticity will always have negative value. As far as I can agree with this statement for all budget and even full-service properties, I wouldn’t say that this work for extremely luxurious boutique hotels. If we consider their products as a Veblen  good, their demand curve would look on the chart below. This means that for certain rate range, the price elasticity of demand will be positive as the demand will be growing together with increasing price.

Bibliography:

Mehrotra, R., Rate OOptimization: Enhancing Your hotel’s pricing strategy. IDeaS. A SAS Company.  http://www.ideas.com/uploads/File/White-Papers/WP-Rate-Optimization-Enhancing-Your-Hotels-Pricing-Strategy.pdf

Author: Mateusz Konopelski

Additionally, I found this interview my Ravi, which besides promoting IDeaS, he is saying how important is to use mathematical equations to describe real-world problems.

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## One thought on “Rate Optimization: Enhancing your hotel’s pricing strategy”

• 18 March 2017 at 05:09

I have 12+ Vacation property listings and counting and I’m only interested in paying/hiring someone/a company to handle all my pricing for me including but not limited to human oversight and adjustment on the prices before they are uploaded About 3 times/week.