Mr. Mahmoud

Revenue Management

Which Data Enhances the Optimization Process and Increases Profitability?

By Ahmed Mahmoud, Founder,

The more data you have the better but only when the RMS analytics improve price-demand estimates, provide controls for your particular business mix and pricing strategy, and enhance the optimization process. A good example of this is the use of rate shopping data for competitive pricing.

A key evolution-in-the-making in revenue management technology in an age of Big Data is the optimization of profitability rather than revenue generation. Profitability optimization can be undertaken by obtaining ancillary revenue and cost data to generate profit contributions by various customer segments. Ancillary revenues cover the spectrum of food and beverage revenue streams as well as golf, spa, events, even from the rooms such as early check in fees, late check out, no show charges, late cancellation and more. These ancillary revenue streams some of which have no cost involved, thus contribute directly to the hotel's profitability.

Cost or margin data is required across each customer segment when the RMS maximizes total profitability, since certain customer segments, while contributing extra revenue, can also incur additional variable costs. This data can be shared with other departments within a hotel like marketing and operations to help identify those segments and activities which help maximize profits.

Revenue managers are facing a data overload: competitor analysis, channel production, distribution costs, booking information, customer data and much more. How can they know where to look, what to get out of their data and how to apply it?

Progress is a double-edged sword for revenue managers. With increased information comes more opportunities and the possibility of better accuracy. However, datasets become more complex, workloads increase and knowing where to look and what to measure can be harder. In simple words revenue management data can include but is not limited to the below:

  1. Arrival and departure patterns ( by Co., by time, by segment, by payment methods …etc)

  2. Guest in house

  3. Market segments

  4. Pricing including Comp set

  5. Inventory

  6. Membership levels

  7. Occupancy levels

  8. ADR

  9. RevPar

  10. Cost effective in each channel and customers segments.

  11. Daily / weekly pick up by segments

Each and every item mentioned above and more can be broken down to a more and more micro data level to help for better analysis, and the most important thing is learning how to read the data to be able to generate more revenue and more profitability for your hotel.

If we take one simple example for the data collection usage, we can use the forecasting which is required by the revenue management. It requires various data elements such as demand, inventory availability, market share, and total market. Its performance and accuracy depend critically on the quality of the data collection, which use time-series models, booking curves, cancellation curves, etc., and project future demand, such as reservations. Price-based forecasts seek to forecast demand as a function of marketing variables, such as price or promotion. These involve building specialized forecasts such as market response models or cross price elasticity of demand estimates to predict customer behavior at certain price points. By combining this data, a RMS can then quantify the benefits and develop price optimization strategies to maximize revenue.

Develop Revenue Management Technology to Contain Non-Traditional Data

We already know that we have big data, but where do we go from here? You might even be thinking we have been using an automated revenue management system for years, and it is working fine, but does this RMS contain enough data to influence the pricing decision? YES it does. Since the game has changed over the last few years, there are more and more varied data influences on today's pricing decisions than before when RMS was available. Price transparency, guest reviews and OTA ratings, shortening booking windows and the proliferation of third-party distribution channels mean that more data, different data and a new mathematical approach is required to drive revenue, profits and share through optimal pricing.

Advances in data management, processing power and forecasting and optimization science now allow RMS to analyze more data faster - with multiple forecasting methods and true optimization algorithms - resulting in more accurate pricing recommendations that reflect current market conditions.

With increases in processing speed, RMS can not only employ more accurate forecasting and optimization algorithms, but they can also perform more complex calculations, like price sensitivity of demand at a market segment or room type level. RMS can account for more data sources, like the influence of competitive rates or reputation scores on demand patterns or price sensitivity.

A strategy coming more into wide-scale use is to set pricing with the help of sophisticated software and RMS developed by hotel brands, management companies, and outside consultants. Today's top hotel companies invest heavily in software that aids revenue-management decisions. Examples include HiltonGro (Hilton), One Yield (Marriott), ROS (Starwood) and IDeaS and Duetto, iRates, and Pricematch and many others which are independent systems used by many hotel franchises and independent hotels. The playing field has been leveled somewhat between branded and independent hotels related to access to these high-tech systems. Moreover many OTS's have started to introduce their own Price analysis tools, i.e. and Expedia.

Where Should You Start?

After you finish reading, it may come to your mind, where to start? Where to invest? Is it big data collection, or a highly sophisticated RMS or both of them together at the same time.

Nowadays and with easy access to data and progressively lower costs to store and process this data, it is easy to believe and act upon the idea that RMS' should incorporate the newest available data immediately. But, like any investment decision, it is a decision that should be considered carefully, ensuring that the hotels' pricing strategy and decisions are improved because of these additions. In conclusion, some RMS providers have yet to prove how new data types drive better revenue.

Remember this, you have big data, you need RMS, you have RMS, you need the big and useful data??!!

In 2006 Ahmed Mahmoud founded, a premier provider of enterprise revenue optimization solutions and services. was created to make an immediate impact on hotel revenue and profitability through forecasting, optimization, and pricing solutions. With more than 18 years of international hospitality industry experience, specializing in revenue management implementation and set up, Mr. Mahmoud started his career in 1992 and has since held various management positions with top hotel chains including Accor Hotels, Hyatt International and Starwood hotels. In 2009 Mr. Mahmoud successfully completed a simple yet sophisticated revenue management operation manual outlining best practice methods designed to rapidly increase a hotel’s RevPar and GOP. Mr. Mahmoud can be contacted at Extended Bio... retains the copyright to the articles published in the Hotel Business Review. Articles cannot be republished without prior written consent by

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