Ms. Mockerman

Revenue Management

Analytics, the 'Holy Grail' of Revenue Management

By Lily Mockerman, Founder, TCRM

Analytics are what some might call the 'holy grail' of revenue management. We build technology to try to capture them, seek new ways to apply them, and wonder how to use more of them. Dashboards and reporting suites are touted by vendors of all sorts as key to the value of their products. Meetings are driven with reports spread across a table or screen as various players on the team work to make sense of the numbers and what they mean for their business. Analytics help give us the information needed to relay suggestions to marketing, advertising and public relations teams to help identify areas that are currently experiencing success and others that may need improvement. They can be used to help predict consumer behavior and provide effective ways to use product availability and price to maximize a company's revenue growth. Using analytics as a foundation allows hoteliers to enhance their ability to shed light on how the guest will behave before, during, and after the travel planning process.

As is often discussed in leadership circles, the best predictor of future behavior is past behavior. The data analytics provides can be harnessed to predict future actions. Analytics are also imperative to forecasting both the subject's and the market place's pricing and product availability.

Big Data is the new hot button for analytics, with increasing conversation around how to harness and use it at the hotel level. Per an article from Forbes, Big Data can be seen as a collection of data from traditional and digital resources from inside and outside a company that can provide a source for continuous discovery and analysis.

Each individual guest will check into a hotel with their own set of expectations and preferences, and it has become the job of Revenue Managers to help identify areas of success and failure, so that hoteliers can achieve those expectations to deliver a returning customer. To do so, we must use in-depth data and resources to help us differentiate between customer preferences. However, despite our refrain of better use of analytics, we still misuse or ignore the basic capabilities we already have.

In the use of analytics, we see three primary issues:

  1. Collection Addiction - When a company has a high level of focus on collection of data from multiple points but doesn't place enough emphasis on effective application, leaving them with a disjointed data warehouse of unusable metrics. Companies can gather an influx of data that may help tell a story, but if they don't have the ability to effectively apply the information, then it will be considered useless. This issue often presents itself when working with big data.

  2. Biased Fallacies - When a company or team member misuses data to further their own agenda by creating false proofs using information in partial or mis-applied ways. This can be compared to the familiar confirmation bias - a common systematic error of inductive reasoning.

  3. Distractibility or Reactivity - When team members are so focused on the next fire to put out, or the pressure of a political corporate environment, that the analytics are "massaged" or altogether ignored in favor of an emotional reaction to various situations. This directly conflicts with the science of analytics and can often present disastrous results for a company.

To effectively utilize analytics, companies must follow the below best practices for analytics:

Evaluate usability and accuracy of collected data points. While you might try to prove a point by reviewing the fact that all women named Sally that stay at your resort are willing to pay over $500 for a room, logic would tell us that this is more likely to be coincidental than a true data point. Remember that, according to the old statistics rule, correlation does not imply causation. Put each of your metrics to the test to ensure they are measuring something valid. There will need to be extensive testing and re-testing of gathered data to ensure that a hypothesis is proven correct or incorrect.

Create Standardized Measurements

Although at times standard measurements need to evolve with the industry or the growth of a company, the metrics being measured shouldn't change from month-to-month or even week-to-week to suit the topic of the day. Without consistent measurement in standardized metrics over long periods of time, analysis is incomplete at best, and completely inaccurate at worst. Measuring the performance of a strategy over one month in high season, for example, doesn't necessarily mean the same strategy will perform the same way in low season. Therefore, it's imperative that performance is measured over an extended period of time. Otherwise, much of the analysis is rendered obsolete.

Avoid extremes or reactionary changes. Every Revenue Manager knows what happens when presenting a forecast based on data that shows a major drop in revenues, even if it's in line with market changes. Unfortunately, in a majority of companies, politics often dictate that forecast drops such as these are inflated to show better numbers, with the result being an inaccurate and sometimes unachievable number submitted to a management company or ownership group. When these inflated forecasts aren't reached, both the property and ownership suffer, having used inaccurate data to plan for their month.

Revenue Managers can also be at fault in this scenario, when major swings in the forecast come from not completing an evaluation properly to begin with. The problem here often lies in the issue of inconsistency, when a forecast is evaluated by month over month trending one period, and year over year trending the next, or any number of other variables. This lack of consistency and evaluation of multiple data points to create a full picture can create false analytical pictures of the business.

Finally, it's important to discuss the reactionaries. Once an analysis has been done pointing to the need for a strategy shift - for example a shift from occupancy to a focus on ADR (the most common in our experience), and a strategy is mapped out to accomplish this - it is likely that although the long-term results can hold significant benefits, the short-term impact is detrimental as the culture shifts to a new approach. Too many times, this can cause panic in the ranks, and what could have been a game-changing strategy based on data is muddied or altogether abandoned by dropping short term rates significantly to try to mitigate the temporary losses, leaving the business in a never-ending cycle of unsustainable strategy.

Create Sustainable Processes

As detailed in the example above, it is important following a full and accurate evaluation of the analytics available to set sustainable practices with buy-in from the entire property team. Life moves fast, and sometimes we don't take the time to put a change of strategy in place long enough to see results because it can create short-term losses. However, through evaluating and framing this information with long-term gains, properties can have a complete picture of the true success of a strategy. This is not to say that steps shouldn't be taken to try to mitigate short-term losses where appropriate, but trying to apply competing strategies such as occupancy and ADR across the same range can be ineffective at best and detrimental at worst when the strategies are too far apart in goal and application.

So outside of forecasting examples, how does all of this help with applications like managing rate availability or measuring guest price sensitivity? In short, analytics should really be the only driving force in applying rate availability strategies. Depending on the type of business, these analytics could include metrics such as guest reviews touching on value for price paid, market and competitive set rates, demand measurement tools, tracking demand fluctuations when applying pricing strategies and many others.

Perhaps the simplest law of economics is the demand curve, which indicates the lower the price, the larger the volume of demand. However, this does not necessarily indicate that properties should race to the bottom to fill their rooms. Using fallacies to justify strategy decisions often allows us to fall into the thought that dropping rate automatically creates demand. Truth is, it does to a degree, but given the law of diminishing returns - the point at which the level of profits gained is less than the amount of money or energy invested - gaining 10 rooms by lowering the rate by $50 is likely not worth the effort or impact to future strategy. The often-overlooked idea is that there is an ideal equilibrium between where a price and demand ratio should fall. In essence, just because you can gain additional rooms from lower rates doesn't mean you should.

In addition to looking at this curve for one property or market, it is important to understand that guest price sensitivity varies from market to market or even segment to segment. Destination resort guests, for example, behave differently than select-service frequent travelers. In some markets or customer segments, a $10 price change can mean the difference between capturing 10 or 60 reservations. In others, a $75 price change may have little to no impact. Ideally, this concept of elastic or inelastic demand is a data point that all hotels should be measuring, preferably in each of their customer segments, knowing that a retail guest and a group or corporate guest are likely to behave differently. By better leveraging analytics such as lead time, channel mix, segmentation and price elasticity, we can work to achieve the proper equilibrium between prices and availability in our rate offerings.

Of course, our discussion right now is in its simplest terms, relating to basics like rate positioning and forecasting. Measuring metrics such as marketing ROI, ancillary spend by guest and a myriad of other areas can produce significant incremental profits. However, taking the time to achieve a solid foundation of sustainable analytics is crucial to being able to build on solid successes.

Lily Mockerman, President and CEO, founded Total Customized Revenue Management in 2012. It has become the premier provider of revenue management services for the hospitality community. Ms. Mockerman is a devoted leader and practitioner in the revenue management field. She has developed strong analytical skills and experienced foresight, is technology-savvy in many hotel systems and can clearly communicate vision and strategy for her clients and team. Since earning her Bachelor of Science in Hotel Management from Johnson & Wales University, Ms. Mockerman’s career has encompassed a variety of roles and responsibilities, including experience with Starwood Hotels & Resorts, Hilton Worldwide and the independent hotel space. Ms. Mockerman can be contacted at 623-536-7066 or info@tcrmservices.com Please visit https://www.tcrmservices.com/ for more information. Extended Bio...

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OCTOBER: Revenue Management: Technology and Big Data

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For years, hotels have housed their Revenue Management systems on their premises. This was possible because data sets were huge but manageable, and required large but not overwhelming amounts of computing power. However, these on-premise systems are a thing of the past. In the era of Big Data, the cost of building and maintaining an extensive computing infrastructure is incredibly expensive. The solution – cloud computing. The cloud allows hotels to create innovative Revenue Management applications that deliver revenue uplift and customized guest experiences. Without the cloud, hotels risk remaining handcuffed to their current Revenue Management solutions – and falling behind competitors. READ MORE

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