Part 1 of 4 of an article series entitled 'Revenue Management Systems 2' -

The Revenue Manager (RM) and the Chief Financial Officer (CFO): both must decipher and act on the most influential data in order to determine the future of their respective businesses. 

Much like the CFO, the RM must gather forecasting data not only to achieve the highest revenue possible (measured here by RevPAR), but also to ensure the hotel's future profitability and cash flow.  This data contributes not only to day-to-day operations, but also to strategic, long-term projects such as capital improvements and expansion.

The CFO uses historical market prices to forecast future decisions associated with debt-financing, overall financial performance, and—probably the most closely watched of all— the firm's own stock price.  By studying market trends and how they have affected the firm in the past, the CFO can create a model that influences future decisions.

Likewise, the RM uses his own property's historical pricing data as a baseline for a rate model, but unfortunately, this is where most RM's end their rate-pricing analysis.

Historical Pricing is Not Enough

While historical pricing is important, other variables must be taken into account.  The RM must possess the most current and relevant data to strengthen the historical pricing forecasting model.  From there, he must eliminate variables out of his control—create a baseline model—in order to make decisions based on market variables other than historical data.

For example, a CFO's plans for asset management and debt financing consist of weighing variables that affect the future rate of return.  Before he can figure out where to park the company's funds, the CFO must ask questions such as: What is the expected rate of inflation?  What is the market's current rate of return? What is the relatively risk-free rate of time-equivalent treasury bills/bonds?  Each of these questions represents a decision that the CFO can act upon; however, first he must normalize all of the other of data—data out of his control, such as historical market variables, interest rates and inflation rates.

The same type of thinking can be applied to RM decision-making.  Let's examine by using a simple example of a hypothetical 200-room hotel:

• A corporate event planner has called to request 100 rooms next Friday at a rate of $70 per room.
• For the last eight Fridays, the hotel has had 80% occupancy, with an ADR of $100.  Consequently, the forecasted RevPAR for next Friday is $80.
• The event coordinator's offer would yield a RevPAR of $35, which added to the remaining vacancies for next Friday—it's assumed that the remainder of the rooms (100) would sell out at the trailing ADR rate of $100—would yield a Total RevPAR of $85. ($35 + $50)

This is a traditional baseline historical pricing model.  But what now?  Given that the hotel would make a higher RevPar ($85 instead of $80) if it books the event coordinator's group, the decision is a easy one, isn't it?

No, it's not. 

This is only a baseline model—from this point going forward, every decision made relies on the accuracy and relevancy of current and forecasted data, in addition to the RM's property knowledge. Property knowledge deals mostly with internal management and can take up the bulk of an RM's time.  The RM must analyze internal data that solves problems such as the added expense associated with the extra 20% in occupancy. 

Will the extra bump in RevPAR cover these expenses?  Will the extra revenue generated from additional hotel activities make up for the added expense?  Is there a negative pricing impact of selling out a block of 100 rooms to increase RevPAR only five dollars?  Could the RM have reacted to the market differently to get a higher ADR from this block of rooms—or even the remaining 100 rooms on the market? This is where historical pricing simply isn't enough.

Just as the CFO analyzes ever-changing market variables to reach a decision on where to invest company's funds, the RM must now react upon such variables in order to make a well-informed decision about next Friday's booking. 

Let's look at a few of the variables the RM must analyze:

• Sales Channels: The relationship of the hotel's pricing to its online page position; rate discipline; rate consistency throughout channels, etc.
• Inventory: Up-to-the-minute inventory data—Does it match with the historical levels associated with the trailing data?  Is inventory trending higher or lower?
• Competition: At any given moment, where does the competition's rate stand compared to the RM's current rate?  Is the competition reacting quickly to its online sales channels and actively managing its page position?

Of course, this not an exhaustive list; however, a single variation on any one of these can influence the RM's decision.  Of course, it's humanly impossible to monitor all of these variables in addition to managing the internal tasks associated with revenue management. 

For those who want to generate the most comprehensive forecasting and decision-making models, software solutions make the most sense.  Just as CFOs use software to normalize market trading variables to create a baseline from which he can move forward acting on variables under his control, so too does the revenue manager.  Furthermore, software packages offer the RM the added benefit of real-time monitoring, leaving more time to deal with internal revenue management tasks. 

So, is next Friday's group booking worth it?  Only the RM can make this decision; however, the more active variables he can act upon, the more certain (and profitable) his decision will be. 

In short, both the Revenue Manager and the Chief Financial Officer use historical data to determine the financial future of the company.  After compiling historical data to create a baseline, the success of both the CFO's and the RM's decisions depend on the most up-to-date and relevant current and forecasted data. 

Look for Part 2 of this series where we further compare the Revenue Manager to the CFO and explore the use of market-based solutions in the hotel revenue management industry.

About REVPAR GURU

REVPAR GURU provides hotels around the world with an alternative revenue management system, designed to deliver maximum bookings and profits. As REVPAR GURU's custom-designed Yield Dynamic Price Engine is the only real-time revenue management system available on the market, it meets the rapidly changing needs of hotels in a very demanding business environment.  REVPAR GURU's  system offers dynamic rate optimization, real-time pricing, integrated internet and extranet yield channel management and GDS sales distribution, to increase a hotel's RevPAR intelligently and effectively, while maintaining rate integrity and automated rate parity. Since 2004, REVPAR GURU's system has been used by hotels worldwide to increase occupancy and RevPAR. 

Headquartered in Miami, Florida, additional information can be found at www.revparguru.com or by calling +1.786.478.3500.