Pricing as a strategic tool: A conversation with Maarten Oosten

Kelly McGuire
Kelly McGuire|February 7, 2013

Along our 2013 theme of building a strategic analytics culture within a Hospitality and Travel organization, I had a conversation this week with Maarten Oosten.  Maarten is a senior manager in our Operations Research Center of Excellence at SAS.  He is a cross-industry pricing and revenue management specialist, and has lately been thinking a lot about how companies can leverage pricing as a strategic tool.  Maarten will provide much more detail in our upcoming Cornell CHR/SAS webcast this month.  I am excited about previewing the topic in advance, as I think it’s a very interesting way to think about the revenue management function.

Maarten reiterated what we’ve discussed at the analytic hospitality executive before, that the job of the revenue manager is much broader than simply setting prices (or managing a revenue management system).  They are frequently asked to perform or support analyses of distribution channel strategy, new product or service development, market share and competitive positioning. These types of analyses are much more strategic in nature, and require using traditional revenue management inputs and outputs in different ways.

Just as revenue managers need to think of their data in different ways, they should also take the opportunity to think about their objectives in different ways.  The traditional objective of maximizing short term revenue, while clearly important, might not always be aligned with an organization’s overall business strategy.  Pricing drives revenue, but it can also help to achieve other strategic goals as well.  Companies who want to open new markets, stimulate growth in specific segments, increase loyalty or shift channel demand might decide to adopt a pricing strategy that is not specifically designed to maximize revenue in the short term, but rather, helps the company to achieve a longer term business objective.  For example, undercutting the competition’s pricing in a market could impact your short term revenue, but it can provide the opportunity for you to gain market share and drive revenues, in the long term.

Maarten will describe this in more detail during the webcast, but he emphasized to me that taking this strategic view definitely does not mean throwing out the revenue management system or its recommendations.  Rather, he says, the revenue management system outputs will provide valuable support for making these strategic decisions more intelligently.  For example, a rental car company may want to foster loyalty from their most valuable segments by ensuring that there are always upgraded cars available on the lots, even if they could have rented those cars at the usual rate.  They could simply decide to have 20 extra intermediate vehicles on the lot every day.  Alternatively, revenue management forecasts could help them to refine that number, ensuring that the loyalty members get their upgrade, but freeing up some additional cars that aren’t needed for full-price rentals.  Companies can, and should, also use revenue management data to perform tradeoff analyses, so that executives can fully understand the implications of their strategic decisions.  Maarten thinks of this as adding an additional layer around the revenue management system that supports this longer term thinking, rather than replacing or removing the RM system altogether.

Speaking of revenue management systems, Maarten also advocates that, with this eye to managing to a business objective, companies think about how they manage their revenue management systems.  Revenue management systems’ output is a long, complex matrix of pricing recommendations.  Typically, revenue management systems are designed to either automatically update selling systems with these prices unless the revenue manager makes a change, or to force the revenue manager to review all prices before they are updated.  Both methodologies require day to day, detailed work reviewing pricing – whether by running down a list, or by managing alerts and exception reporting.  Maarten suggests that this process can, and should, be greatly simplified.  Prices can be grouped together in like categories for review, for example, and these pricing categories should be able to either automatically update or not.  Those categories that have specific strategic value can be managed more carefully, while other categories may be allowed to update automatically.  Maarten calls this “targeted revenue management”, as opposed to managing by exception.

I encourage you to watch our webcast in February to hear more about this topic.  We’ll be discussing this on the blog later this year as well, so stay tuned to the Analytic Hospitality Executive.  We’d also love to hear from you.  How are you thinking about pricing as a strategic tool?

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A primer on PEAD: An interview with Pamela Moulton on analysis of hospitality stock performance

Kelly McGuire
Kelly McGuire|January 29, 2013

In preparation for our upcoming Center for Hospitality Research (CHR) and SAS webcast, I had the opportunity to speak with Pam Moulton, co-author of a recent CHR report called, “Earnings Announcements in the Hospitality Industry: Do You Hear What I Say?”  Now, I must make a confession.  My analytic prowess has never extended to the financial industry, so I have only a very basic understanding of the workings of the stock market (think: buy low, sell high).   However, I found Pam’s paper accessible, easy to understand, and most importantly interesting, especially after she walked me through some background on the motivation for her research.

Obviously, any research into the behavior of the market that provides the ability to better predict stock prices can be very valuable to both companies and investors.  The particular behavior covered by this paper is the phenomenon known as post-earnings-announcement drift (PEAD), which is the finding that after a firm announces their quarterly earnings, stock prices adjust up or down depending on the news, and then continue to drift in that direction for a period of time before reaching their eventual “fair price”.  Pam was interested to know how hospitality companies’ stocks reacted to earnings announcements, and whether their behavior was the same or different as the market in general.

I’ll start at the beginning (which is where Pam started with me).  For those of you who are already familiar with this area, skip right to her paper.  For the rest of us, here’s the background:

Most of us know that publicly traded companies are responsible for reporting performance to the market on a quarterly basis through an “earnings call”.  During this call, key company executives (usually the CEO or CFO), summarize their performance, list key factors that are contributing to that performance, and talk about their expectations for the future.  At times, there are “surprises”, either good or bad, which result in the value of the stock being worth dramatically more, or less, than it was believed to be worth prior to the new earnings information.   In a perfectly efficient market, the stock price would immediately adjust to the new “fair price” based on the updated company information.  However, this does not always happen.  Instead, stock prices react to some degree, and then “drift” in the same direction eventually stabilizing at that “fair price”.  The timespan for this drift in the market in general can be up to 60 trading days after an earnings announcement.

The complication in the process of communicating results is in the manner by which the information is disseminated.  To advise clients on the broad range and large quantity of publically traded stocks, brokerages (which execute trades for their clients) typically employ analysts.  The analysts’ job is to be an expert in a particular area of the market (usually industry based).  They follow the performance of specific companies within the industry, and also any other factors that might impact the financial prospects of that industry (in hospitality, for example, gas prices or global tourism trends).  Their function is to both make broad recommendations about the prospects for the industry as a whole, and also to make specific recommendations about what to do a with company’s stock (buy, sell, hold).  They listen carefully to quarterly earnings calls and quickly publish reports that detail their interpretation and recommendation.  These reports are purchased immediately by institutional investors who want to react quickly to news, and the contents eventually make their way into “publicly” consumed resources like the Wall Street Journal.

So, the challenge here is that in order for the market to be perfectly efficient (react “that day” to triggers that impact the value of the stock), company managers need to do an effective job of communicating information, analysts need to correctly interpret the information, and investors need to consume that information and take action quickly.  The volume and direction of the actions will then move that stock price to the fair price.

Why is this important?  Obviously, the timing of a stock purchase or sale is a key determinant in whether you’ll make money from that purchase.  When a company makes an announcement that is likely to trigger a change in the stock price, you have one of those “buy low, sell high” opportunities that result in financial gain (or prevention of loss, I guess).  If the price “drifts” it means that the “ideal” timing of the action you might want to take is somewhat more flexible.  Pam gave me an example:  Say a stock is currently priced at $20 and company management makes a positive “surprise” announcement – earnings are higher than expected.  The announcement may mean that the new fair market value of the stock is $25.  If the stock exhibits post-earnings announcement drift, the stock price might go up to only $22 on that first day, then a few days later to $23, and so on until it eventually reaches $25, the new fair price.  So, if you were a potential investor, and didn’t get the news for a couple of days, you still have an opportunity to make some money on the stock, because it will continue to drift in a positive direction for a while.  You may not make as much as if you had bought that first day, but the opportunity is still there.  Of course, the reverse is true in the case of a negative earnings surprise – post-earnings announcement drift means you won’t lose as much if you don’t sell right away.

I asked Pam if PEAD was an advantage or a disadvantage, and of course, she told me that the answer depended on the perspective and the direction.  For investors, regardless of the announcement, PEAD could be an advantage (as described above), there’s more time to make a decision that is financially advantageous.  However, if you are a “high speed” trader with a positive surprise announcement, there’s maybe a disadvantage in that you might have to hold a stock longer than you want to get the full value from a positive announcement.  From a company perspective, with the thought that you want to attract and retain investors by keeping that stock price up, they may wish to have “positive” surprises reflect instantly in the stock price to attract investment, but “negative” surprises take their time to avoid the negative press coverage that a sudden big drop in stock price can attract.

Clearly there are some very complex interactions that are driving stock prices (I mean, I knew THAT, but still, interesting to understand it in more detail), and the better we understand them (through analytics, of course) the better we will be able to take advantage of information in stock prices – whether personally, for our own portfolios, or professionally, on behalf of our companies.

Pam will present the results of her research in our upcoming webcast (and you can read her paper as well). You will probably be surprised to find the similarities and differences in how hospitality stocks behave (Pam was!).  Hopefully, this discussion will provide some good background for those that needed a bit more context to appreciate her work!

 

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Social media and lodging performance

Kelly McGuire
Kelly McGuire|January 22, 2013

Chris Anderson from the School of Hotel Administration at Cornell recently released a report through the Center for Hospitality Research (CHR), summarizing the results of a series of studies he’s conducted to determine “The Impact of Social Media on Lodging Performance”.  Because of the relationships with the CHR, Chris was able to bring together data from three CHR research partners (ReviewPro, STR and Travelocity), and two other data providers (comScore and TripAdvisor).  This allowed him to provide a unique perspective on how social media moves markets.

As our loyal readers know, I am keenly interested in this area, and have done some research in it myself.  This research is quite complimentary to what Breffni Noone and I have focused on.  We studied consumer reaction to user generated content (UGC) and price in our work, while Chris is looking at UGC and hotel performance.

Chris’s series of studies can be summarized in three findings:

  1. The percentage of consumers who use reviews on TripAdvisor is increasing steadily
  2. If a hotel can increase their aggregate user ratings by one point (e.g. 3.3 to 4.3), they could increase their price by 11.2 percent before impacting occupancy
  3. A 1% increase in a hotel’s reputation score (as measured by ReviewPro’s Global Review Index ™), leads to a 0.89% increase in ADR, a 0.54% increase in occupancy and a 1.42 percent increase in RevPAR.

Chris will discuss his methodology and results in more detail in our upcoming CHR/SAS webcast to air in February 2013.  You can also download his paper from the CHR.  Instead of repeating what you can get from him, I’ll give you my perspective on his results.  As always, we’d love to hear your thoughts as well, so we look forward to your comments, questions and ideas!

The first point I would like to make is that in Chris’s study, even though he uses the word “reviews”, with the exception of the work on TripAdvisor, he is actually working with user ratings (i.e. the quantitative metric, generally 1-5).  He used the hotel’s aggregate user rating at the time of purchase in his Travelocity study (along with the number of reviews), and ReviewPro’s GRI ™ is the result of an algorithm that rationalizes quantitative metrics across all major OTAs and review sites to come up with an indexed score (see http://www.reviewpro.com/product/global-review-index).  Obviously, numbers are easier to work with than unstructured text, so it makes sense to use them in this context.  This does raise a couple of questions in my mind:

  1. In my research with Breffni, we found that consumers relied much more strongly on the unstructured text reviews than on the quantitative user ratings.  While the ratings were significant, they were much less so, and when the reviews and ratings conflicted, consumers relied on the reviews.
  2. Some research has shown that the quantitative score that a reviewer provides is often inflated, and also frequently not correlated well to the review that they write (see this CHR report by Rachierla, et. al for an interesting analysis of this paradox).

What does this mean for Chris’s study?  Probably nothing major, but given how much press “reviews” get, I thought it was worth pointing out.  When you are looking at influence on markets versus influence on individual purchases/booking behaviors, then it could be argued that user ratings are more of an indication of the aggregate, historical market perception of the hotel, and therefore a better metric to be used for the purposes of tracking overall performance (in particular in the third study).  However, if one makes that argument, the second point is of concern, and probably bears further thought and research.  If a high rating is strongly correlated with a positive review (also assume that even the positive review contains no details that would “turn off” a prospective purchaser), then Chris’s results hold.  If, as some research has shown, ratings are typically inflated, or are not easily interpreted objectively (i.e. my definition of what makes a four may differ greatly from someone else’s), then it might be important to see the same KPIs in this study compared with review sentiment. Again, this certainly doesn’t mean that results are not valid, it is just a point that managers should be aware of as they decide how this research applies to their business.

This brings me to my second point regarding how hoteliers should apply the results of this study in their environments. There is no doubt that this study reinforces the point that hoteliers must continually monitor UGC, and use what they learn to maintain and improve customer service.  However, I would argue hoteliers must think before they rush to raise prices based on this research.  The Travelocity study showed the impact of moving from a lower to a higher rating. The STR performance data showed relationships between UGC and KPIs.  Since the research was based on historical data, this suggests that many hotels with higher UGC tend to already be commanding a premium price in the market.  Hoteliers need to verify that the opportunity is there for them to raise price.  For example, if you are already at that higher rating, your price may be where it needs to be already.  Our research has shown that consumers prefer to pay a lower price, all things being equal, but they will pay more if one hotel is clearly rated better or has better reviews.  It also showed that hotels with bad UGC would see no benefit from lowering price.  Both studies imply that in order to price effectively, revenue managers (and hotel executives) must understand not only their price, demand and value proposition, but that of the competition.

Chris makes the excellent point that better ratings lead to more pricing power.  How hoteliers chose to use that pricing power depends not only on their position in the market versus the competition, but also their long term business strategy and goals.  Are there branding, market share or future development considerations?  What about your plans for attracting business that isn’t directly influenced by user generated content (contract, groups, wholesalers)? How would a price change impact that?  Are there loyalty or marketing implications?  Later this year in the blog, we plan to spend some time talking about how companies can use price as a strategic lever, not just a tactical revenue-maximizing tool.  This study provides support for the importance of including analytics derived from UGC in that strategic discussion.  It should (and will) spark some interesting conversation among hotel departments, as the implications for each individual property are debated!

I hope you will tune into our webcast in February to hear more from Chris about this study!

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Achieving the balance in hospitality with analytics

Natalie Osborn
Natalie Osborn|January 14, 2013

Happy New Year Analytic Hospitality Executives! I trust that you enjoyed the holiday period and have returned from the holidays well equipped to deal with the year ahead.

At the conclusion of last year, Kelly explained that throughout 2013 we will be focusing on how to build a strategic analytic culture within your organization. This is a much discussed topic around our office. Today I wanted to bring you into the conversation and share our vision for how to integrate analytics into your organization. We’d love to hear your perspective on how this maps to where you are.

Working in a company solely focused on analytics, it has been very exciting for us to see pockets of analytic competency emerge in the hospitality industry. Analytics can help hospitality companies achieve the fine balance between delivering memorable guest experiences and driving revenue and profits. For many of you, I know that balance ends up looking a lot like the teeter-totter show in the illustration below (or, if you are from the same side of the world as I am, the see-saw). If you focus too much on increasing the revenue and profits side, and you negatively impacting the customer experience side. Likewise, focusing on increasing customer experience can negatively impact revenue and profits.

This balancing act can be draining on your organization and make you feel like you are constantly switching from one side to another in the attempt to achieve some stability. This is where analytics can help. Predictive analytics, such as forecasting and optimization, have been used in revenue management applications in hospitality companies for many years. But, revenue management analytics currently only addresses one side of our teeter-totter – the revenue and profits side.  As hospitality companies are in the business of selling an experience, we should also focus our analytic efforts on our relationship with our guests and patrons.

Every contact that your guest or patron has with your organization is an opportunity to increase the value of that relationship. Committing your organization to a 360-degree view of your guests and patrons starts with a concerted effort in data management.  Consolidated information about your guests and patrons allows you to understand their total current and potential value, helping you to evaluate and optimize your offers.

Analytics does not just optimize revenue, but can also optimize your contact and service operations, enabling you to serve your customer in a manner that is not only more personalized, but also more profitable. Surfacing the needs and preferences of customers can help you design your service offerings, ensuring that your spaces are configured for the correct activities and that your resources are deployed where they will be most effective.

Analytics can help to achieve the balance between profits and customer experience, but they must be ingrained in a hospitality company’s culture to have full effect.  For many, the journey to a strategic analytic culture starts with quick wins, through the introduction of analytic tools into select departments within their organization. Creating these pockets of competency provides the momentum that is needed for an organization to aspire to an overall analytic culture. However, the real advantages come when analytics is used to integrate decision making between departments.

Integrating decision making between departments can start to break down traditional silos and allow more forward-facing decisions at a higher level within an organization.  As the use of analytics spreads through an organization, effective presentation of analytic outcomes becomes critical. Hospitality executives are simply too busy to consume analytics at the same level of detail that most analysts must.  Presenting analytic results in a visual format, whether charts or graphs that highlight high level business opportunities is important to elevating the use of analytics to the executive level. And buy-in at the highest level of the organization is crucial for sustaining a proactive, integrated analytics program in any organization.

Later this week Kelly and I will be at the eye for travel Smart Travel Analytics show in New York City. Over lunch, we’ll be hosting a round table to discuss how to build a strategic analytic culture in your organization. If you are at the conference – please drop by and say hi – we’d love to hear how you are working on integrating your analytic efforts.

 

 

 

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Reflecting back and looking forward

Kelly McGuire
Kelly McGuire|December 18, 2012

2012 has been another exciting year for Analytic Hospitality Executives!  I have been energized by the growing interest in analytics across the hospitality industry, in all sectors, and throughout all departments within the organization.  In fact, I have met more hospitality people with “analytics” somewhere in their title in the last six months than over my entire career.  This is truly an exciting time to be an Analytic Hospitality Executive!

This is not just true in the US, where I make my home, but across the globe as well.  I had the privilege of visiting India for the first time this past January, and was intrigued by the challenges the hospitality industry faces there (see my blog post from earlier in the year for more details).  The domestic brands are growing quickly, and they are facing challenges with finding and training qualified labor and also with meeting customer expectations while maintaining a profitable cost structure (sound familiar?).  In addition to these challenges, expansion by all of the global brands into India is putting pressure on domestic brands to compete for the international traveler as well.  This means gaining exposure on the global stage, as well as developing systems and processes (like loyalty programs) that can attract this kind of traveler.  (It goes without saying the Indian brands will also need to develop strong analytic capabilities in marketing and revenue management in particular to keep pace with these global players).

I also spend some time in Macau, Hong Kong and Singapore this past spring.  The growth of the gaming industry in Macau is just incredible.  Smart operators in Macau realize that the “if you build it, they will come” mentality will come to an end someday, so to survive and thrive, they need a competitive edge.  They will need to better understand patron behaviors and preferences to attract profitable patrons. Most of the major companies in Macau are quickly building up analytic capabilities to answer these challenges. No different than in other areas of the globe, hospitality companies in Asia are being challenged by mobile and social.  As companies develop and execute programs to increase engagement and drive bookings, analytics will become critical.  The real-time component introduced by mobile will challenge the velocity at which results must be calculated and delivered.  The volume and variety of social data is a tremendous source of information for hospitality organizations, but the right analytics is needed to turn that volume of data into meaningful, actionable information.  This is no different than what we are facing here in the US – and further demonstrates that we are living in an interconnected global community, where actions have far reaching ramifications!

More than anything else, 2012 reinforced to me how interconnected all of the functions within the hospitality industry are.  At the Analytic Hospitality Executive, we spent a good portion of the year talking about innovations in revenue management and price optimization that will help to overcome some of the market challenges facing the revenue management function.  These challenges are driven by the same factors that are impacting other departments (like marketing); price transparency, shrinking booking windows driven in part by mobile bookings, and increased competitive pressures.  It is not new news that solutions developed by one department can create problems in other areas, but the pace of today’s business environment means that it is even more critical than ever to ensure that decisions are synchronized across the organization, leveraging all available information.  As much as this is a technology challenge, it is an organizational and culture challenge as well, requiring alignment of goals and incentives along with access to the right data and systems.

The American Statistical Association has joined other leading statistical societies to declare 2013 “The International Year of Statistics”, and I think this will hold true for the hospitality industry as well.  In fact, I think 2013 will be the year not just of statistics but also of Visual Analytics.  They say a picture is worth a thousand words.  Well, that’s true in analytics too!  Hospitality companies will need to continue to justify the investment in analytics software and the people to support it, reaching higher and broader in the organization to evangelize the value of analytics.  At the same time, the pace of business will continue to increase.  We will have less time to do more, and those who are able to communicate results effectively will be the ones that continue to succeed and drive companies forward.

Quickly and effectively presenting analytic results, in the forms of charts, graphs or even stories that reinforce a point will be crucial to gaining acceptance.  Rows and rows of data, complex charts with equations, p-values and regression outputs are not the language of business.  Yes, we’ll still need to ensure that results are based on analytical rigor, and we will need to be prepared to support the conclusions we make.  Telling the story in a simple and engaging format that quickly drives home our points will get the attention of busy executives, owners, shareholders and stakeholders.  For a number of years now, we’ve looked for “geeks who can speak” in revenue management to tell the revenue story to stakeholders across the enterprise.  We’ll need this particular skill across the organization moving forward!

Throughout 2013 we at “The Analytic Hospitality Executive” will be walking our readers through the process of building a strategic analytic culture throughout your organization.  It’s time to tie all of those pockets of analytic excellence we’ve built up over the last few years into one high-powered analytic entity, with each group contributing their own expertise to the overall goal of moving the entire organization forward, profitably.  We will continue to highlight emerging research from our co-sponsors at the Center for Hospitality Research at Cornell, discuss insight we have gained from conferences and events, and challenge our readers with interpretations of current events.  We plan to do all this with an eye to how each piece contributes to a business strategy with analytics at the core.

This entry marks the end of our first year of the “The Analytic Hospitality Executive” blog.  I have thoroughly enjoyed my first foray into blogging, and I sincerely appreciate the efforts of my co-author Natalie Osborn in coordinating, driving and co-contributing to the good work we’ve done this year.  I have enjoyed interviewing many industry leaders and academicians throughout this year as well.  And most importantly, I have appreciated you, the loyal followers of the blog.  I have received great feedback from you throughout the year, and have enjoyed provoking conversation and continuing to learn from such a vibrant community.  I wish you all a great holiday season and a safe and happy 2013. I look forward to continuing our dialog next year!!

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Cornell Hospitality Research Summit 2012

Natalie Osborn
Natalie Osborn|November 29, 2012

Recently, several of us at the Analytic Hospitality Executive participated in the Cornell Hospitality Research Summit (CHRS), which is put together by our partners at The Center for Hospitality Research. This event brought together 240 participants from 21 nations in addition to the U.S., with about 60 percent industry practitioners and 40 percent academic researchers. This conference is great example of where industry problems meet academic research.

CHRS 2012 kicked off with a panel of industry CEOs, who highlighted many of the industry's current challenges and pointed toward possible solutions. The panelists pointed out the clear connection between operations excellence and value creation, and suggested that hotels should choose to offer services that fit their corporate values. The panelists also raised one of the industry's greatest challenges: how to offset the focus on price and location in hotel sales.

The second keynote panel comprised the deans of hospitality education programs from around the globe. For their part, the deans also see the industry's many changes and point to hospitality educators' constant struggle to keep up. The panel members pointed to the need to provide a holistic approach to education that builds skill sets and engages students in hospitality and service.

One of the issues that was raised in several of the sessions that I participated in was the issue of attribution models. Hospitality and travel industry practitioners are struggling with understanding how their customers make buying decisions online, so that they can have more influence on the buying decision process. Where does a customer visit or research or prior to buying, and how much do each of these visits influence the decision to purchase?

Is this something that you are struggling with in your marketing analytics? How have you approached solving this puzzle so far? We’d love to hear from you.

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Revenue management and price optimization in review

Kelly McGuire
Kelly McGuire|November 11, 2012

Over the last 10 weeks at the Analytic Hospitality Executive, we have explored the topic of Revenue Management and Price Optimization for the hospitality and travel industries.  We’ve looked at how changes in the market, the changing needs of revenue managers and advances in technologies have put pressure on traditional revenue management approaches.

To kick off our theme, Alex Dietz explained the differences between revenue management and price optimization in his posts on Revenue Management vs. Price Optimization: Part One and Part Two. This distinction has been increasingly difficult to make, due to the evolution of revenue management practices and changes in the very definition of revenue management.

I provided an overview of why revenue management systems are just not working anymore, including the conditions in the market today that have fundamentally changed both demand patterns and purchase behavior, the advances in forecasting and optimization technology that have outpaced the analytic capabilities of today’s revenue management systems, and the broader and more strategic role of the revenue manager that means most systems no longer meet all of their needs.

I also explored in more detail how the market has fundamentally changed since revenue management was introduced to hospitality, including becoming more complex and dynamic, and how revenue management systems can no longer cope with these changed market conditions. Alex reviewed why limitations in today’s revenue management systems mean that  revenue management analytics are becoming outdated, and how the changing role of the revenue manager is putting pressure on not just the analytics, but the experience of the user.

I shared that SAS has been researching the problems in revenue management and price optimization for some time, and have used our experience in other industries to rethink pricing and revenue management technology for the hospitality and travel industries.  SAS Revenue Management Price Optimization Analytics is the culmination of that research. Part of the solution to the problems of current revenue management systems is to use advanced technology now available to avoid simplifying assumptions, which I reviewed in “Why Revenue Management needs “big analytics”.” Alex explored the importance of price elasticity, and looked at how to account for competition in revenue management.

Lastly, I caught up with Ravi Mehrotra from IDeaS – A SAS Company, which has been working closely with SAS on the hotel revenue management problem, to talk about how he is thinking about the future of hotel revenue management and his perspective on the work that IDeaS has been doing with SAS.  Ravi specifically highlighted how general purpose optimization problems will not help to solve complex total revenue management problems, and talked about how IDeaS and SAS have created new optimization approaches to account for this complexity.

It has been a very interesting 10 weeks – but, did we miss anything? Are you having any challenges in revenue management and price optimization that have not yet been explored? Let us know – we would love to hear from you.

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The future of hotel revenue management: A conversation with Dr. Ravi Mehrotra

Kelly McGuire
Kelly McGuire|November 1, 2012

This week I finally had a chance to catch up with Dr. Ravi Mehrotra, President & Founder of IDeaS – A SAS Company.   As most of you remember, SAS acquired IDeaS, a leading provider of pricing and revenue management Software, Services, and Consulting to the hospitality industry, in 2008.  Since that time, Ravi and his team have been working closely with SAS's revenue management and price optimization researchers, sharing their domain expertise in hotel pricing and revenue management.

Since we've been talking about the future of revenue management for the last couple of weeks, I was interested to hear from Ravi about what excites him most about revenue management and his vision for the future, as well  to get his perspective on the work that IDeaS has been doing with SAS. 

How do you think that your current customers have benefitted/continue to benefit from the SAS acquisition of IDeaS?

IDeaS was founded more than 20 years ago on a mission to help hoteliers make better pricing decisions to optimize revenue.  Our unique approach to solving problems with our industry-leading solution – adopted by both major hotel chains and independent properties alike –has been underscored by our commitment to superior client service and innovation.  Since the SAS acquisition a few years ago, we strive to leverage the strengths of both organizations. IDeaS now has greater access to analytics research that has been created and tested for the global marketplace – research that is now being combined with IDeaS’ domain expertise of the hospitality sector, to move beyond managing revenues and more toward optimizing profits with forecasting and predictive technologies.

With these resources, we are more focused and committed to our founding mission that gives our clients greater control over their destiny.

As you think about the work you’ve been doing with SAS, what are you most excited about?  What do you think will have the most impact on your current and future customers?

Both SAS and IDeaS place a high value on the power of predictive analytics, and I think the most exciting work that we have been doing together comes from the combination of SAS’ analytics horsepower with IDeaS’ expertise in revenue management and price optimization in the hospitality domain. The marriage of SAS' advanced analytics and platform technologies with IDeaS' revenue management and Software-as-a-Service (SaaS) delivery model will offer an unmatched capability set for quickly driving value for the hospitality industry and beyond.

The result is an entirely new IDeaS revenue management solution that embeds SAS’ Revenue Management and Price Optimization Analytics. Our new solution, which will be commercially available in 2013, will bring hoteliers the ability to better manage, price and optimize all revenue sources. Because we work so closely with our clients, we know their needs and idiosyncrasies, and can configure our SaaS solution and decision support to specifically meet those needs. That’s always been our approach. Now more than ever, by delivering powerful new analytics in multiple ways, we enable hotels to transform their profitability and address their biggest revenue management challenges.

What are some of the biggest challenges revenue mangers face in today’s volatile market, and how do analytics help them address these challenges?

Today, the breadth and scope of the revenue management function is far reaching.  Hotel revenue management professionals face increasing pressure to maximize overall profits from the entire asset while navigating the complexities of highly uncertain and rapidly changing markets. Essentially, revenue management has evolved from a focus on rooms to optimizing revenues and profits from all revenue streams (rooms, function space, food and beverage, parking, spa, golf, retail, other activities). The challenge is to find the most profitable mix of business for the entire hotel asset while accounting for the lifetime value of all guests

And the biggest barrier to meeting this challenge is a lack of qualified manpower with respect to analytics skills, geographically remote work environments and the slow adoption of new technology.  In addition, there is a lack of integration between systems that control different revenue streams.  In order to advance the cause of optimizing revenue streams across the entire hotel asset, hotels will need to hire and empower qualified RM professionals with a suite of advanced models and hotel analytics, invest in new technology and advances in analytics software for forecasting and optimizing decisions, and provide the necessary training to harness the power of a revenue management solution.

What is your view of the future of analytics in revenue management?

Today’s revenue management problems are simply too complex, with too many variables, and require real-time decisions. Traditional, known general purpose optimization techniques are not going to allow us to find optimal solutions to such complex total revenue management problems.

Together, IDeaS and SAS have developed proprietary methodology, models, and solution techniques that enable us to capture the domain characteristics of total revenue management problems and allow us to find an optimal dissection of the problem for making decisions in real time.  We have created a new optimization approach that breaks down the complexities and variables, yet still delivers automated, self-adjusting revenue management decisions – all with real time efficiency. This means that revenue managers will be able to focus more on strategic, high impact decisions for their businesses and trust that their revenue management solution is accounting for the highly dynamic and competitive market forces.

It's pretty clear from our conversation that just looking at a bunch of reports is not going to keep hotels competitive and make revenue managers proactive instead of reactive.  In today's complex market, the only way to stay ahead is to know what's coming.  It's critical for today's hotel revenue managers to have access to advanced, predictive analytics, delivered in a format that supports fast, dynamic decisions.  Definitely stay tuned for more from Ravi and IDeaS, as they continue to innovate in this space!

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Accounting for competition in revenue management

Alex Dietz
Alex Dietz|October 30, 2012

In my last post, I explored the importance of factoring price elasticity into a revenue management approach.  This week, we’re going to explore the importance of accounting for competitor pricing.

In today’s market, consumers have an unprecedented ability to shop around, with the internet now comprising approximately 35% of all hotel room bookings (2010 figures*).  The hospitality and travel customer has wide access to pricing information – allowing her to readily evaluate competitive pricing each and every time she begins shopping for a room.  As a result, many hospitality and travel operators take advantage of a wide variety of competitive information sources, including competitive rate and competitive performance benchmarking information.  This competitive information is used to inform both operational pricing and revenue management decisions, and to act as a gauge for overall revenue performance.

Too Much Focus on Competition?

An unfortunate side effect of this sort of focus on the competition is that it can lead to a “following the leader” mentality that really is not consistent with good revenue management practices.  We see many revenue managers whose primary instinct for pricing is driven by the question: “What is my competition’s price?” and not by the question: “How much inventory do I have left to sell, and how much demand exists for that inventory?”  For a telling story on the downfalls of “follow the leader” pricing, I strongly recommend reading this blog story about Amazon’s $23 million book about flies.

As the $23 million fly book story illustrates, it is imperative that we continue to use sound judgment in making pricing decisions, even as we incorporate competitive rates and competitive effects.  In hospitality and travel, it is particularly important to remember that a price change can be perfectly sensible for a competitor – but not be something that you would want to match.  For example, if I have managed to book a large group for my property during an otherwise low season, I may well increase my rates during the period that the group has booked: I have little inventory left, and need to maximize the value from it.  But, in this same scenario, my direct competitors would likely be foolish to match.  So, the right rate for me is the wrong rate for my competitor.

Competitive Information – A Weakness in Traditional Revenue Management

Traditional revenue management science was not designed to deal with competitive rate changes.  As was discussed extensively in my post entitled Revenue Management vs. Price Optimization: Part One, traditional revenue management science made the assumption that prices would remain relatively stable – and this included our fares and rates, as well as our competitors’.  With this assumption, simple time series approaches to demand are indeed sufficient to capture the variability of demand.

Unfortunately, as was discussed last week, the assumption of price stability is no longer valid.  Both our own and our competitors’ pricing is changing on a regular basis – and our customers have full view of our relative price position vs. our competitors.  This fact can lead one to assume (as many revenue managers do) that if a property is “competitive” in the market, then that property will “capture its share.”  The unfortunate corollary that often accompanies this belief is that in the absence of rate matching, a property gets little or no demand at all.

As a result of both this assumption, and the revenue manager’s desire to incorporate a significant driver of profitability (i.e., competitor pricing), revenue management vendors have often taken a short cut in addressing competitive influences into their systems.  Vendors employ heuristics or constraints to force pricing into a competitive range, in post-optimization processing.  Unfortunately, this approach doesn’t address the type of example that I’ve noted above – the circumstance where the right price for a competitor is wrong for you.

Using Competitive Information the Right Way

To properly deal with competitive influences, we need to recognize that consumer demand is complex, and driven by a number of factors in addition to price.  For hotels, this includes location, on-site amenities, room configurations, distribution channels, familiarity with the market, customer loyalty – and much more.  As our team developed SAS Revenue Management and Price Optimization Analytics, it was a critical goal for us to capture competitive impacts in this way – as part of an overall model for demand forecasting, rather than as a post-optimization heuristic.

In effect, when competitive data is available, we are modeling demand not simply as a function of our own price (as discussed last week), but as a function of how our price relates to the market.  As a result of modeling demand in this manner, we are able to recognize the impact on profitability of being below, at, or above market pricing – and to optimize rates and availability accordingly.  Incorporating elasticity and competitive effects in this way has other benefits, as well:

  • Demand forecasts are improved, because they are now capturing these factors that have a strong influence on demand (our own and our competitors’ pricing)
  • Demand forecasts react to price changes the way that revenue managers (and GMs) expect – higher prices lead to lower demand
  • Demand forecasts react to competitive price changes the way that revenue managers expect – higher competitive prices contribute to higher demand for our property
  • Revenue Managers have greater confidence in the forecasts, because they incorporate the factors that they know are important to the customer

In summary, we believe that the competitive price modeling within our recently introduced SAS Revenue Management and Price Optimization Analytics offers revenue managers an analytically-based approach to managing competitive price effects that has been neglected by other systems to date.

 

References:

* “Distribution Channel Analysis: a Guide for Hotels” Cindy Estis Green & Mark Lommano, An AH&LA and STR Special Report, published by the HSMAI Foundation

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The importance of price elasticity

Alex Dietz
Alex Dietz|October 26, 2012

In my post in September on Why revenue management analytics are becoming outdated, I made note of several important limitations in the traditional revenue management approach.  Today, I’m going to focus on just one of these issues: price elasticity.

Why Price Elasticity is So Important

As I noted back in my 2-part entry on Revenue Management vs. Price Optimization, a number of important trends have taken hold since revenue management science was developed by the airlines:

  • In the airline industry, low cost airlines introduced simplified fare structures with fewer fares and significantly reduced fencing.  Today, these simplified fare structures dominate most air travel markets – effectively invalidating the assumption of demand independence made in revenue management models.
  • Revenue management has been introduced into markets where strict fences on rates or fares never existed.  Many hospitality and travel providers have rate or fare structures that do not contain strict fences – and so the assumption of demand independence is again problematic in applying traditional revenue management science.
  • Rates have become increasingly dynamic.  Revenue managers are now frequently managing the price at which rates are sold on a day to day basis.

These trends have taken hospitality and travel far afield from the expectations of the original revenue management scientists who modeled the airline revenue management problem.  As a result, price sensitivity needs to be a key component of revenue management decisions – and the inclusion or exclusion has ripple effects across the operations of the system.

Demand Modeling Using Price Sensitivity

So, as our team here began working on the development of SAS Revenue Management and Price Optimization Analytics, we knew that it was critical that we model demand as a function of price.  Of course, our model could not be limited to just price: we also knew that hospitality and travel demand continues to vary based on a number of other factors, including:

  • Time of year
  • Day of week
  • Holiday and special event periods
  • Remaining time prior to arrival
  • Competitive effects (more on that next week)
  • …and so on

Here at SAS, we have significant experience in modeling price elasticity, as a result of our work on price optimization solutions for the retail industry.  There, too, it was necessary to construct demand models that captured price effects as well as these other effects.  This experience was extremely beneficial in our development – especially in dealing with automating such calculations on a large scale, and dealing with a variety of different products and market conditions for different products in different geographies.

However, calculating price sensitivity of demand is particularly challenging in the context of revenue management because price is commonly being managed relative to demand – this is, after all, what revenue managers get paid to do (see chart below).  So, when demand for a property is high, the prices tend to be high.  When demand is high we raise prices, but since demand is high, customers are willing to pay those higher prices to have access to the inventory.

Price Endogeneity: The problem of positive price elasticity

As a result of this management, traditional regression techniques will frequently yield a positive relationship between price and demand.  This effect, known as price endogeneity, masks the underlying relationship between price and demand.  In order to effectively calculate price sensitivity in a hospitality context, endogeneity must be accounted for. We use sophisticated algorithms to overcome this issue as we model demand in SAS Revenue Management and Price Optimization Analytics.

Using Price Elasticity to Optimize Rates

The primary use for price sensitivity of demand in revenue management is price optimization of rates. Using traditional revenue management methodology, typically referred to as yielding or inventory optimization, rates are made available or not based on the level of forecasted demand.  In contrast, price optimization considers willingness to pay when setting prices to maximize revenue.  Price optimization provides a better answer for market segments whose rates for a given stay date are managed using variable pricing (we call this “price-able”).

But, in the hospitality industry, not all market segments are price-able.  Hotels make many agreements where the rate is fixed, and the only lever the revenue manager has is to allow or restrict access (we call this “yield-able”).  In addition, hotels often link qualified rates (such as corporate agreements) to the best available rate (BAR) offered to transient guests (e.g., 10% off of the best available rate).  The existence of these different types of rates, and the differences in how they can be sold and managed, makes for important conditions that need to be considered during optimization. For example, it is not sufficient to consider the effect of price elasticity on transient BAR alone when other rates are linked to that rate – the elasticity of the segments associated with those linked rates must be considered, as well.  Modeling the elasticity of these different segments independently allows SAS Revenue Management and Price Optimization Analytics to determine whether a property is better off reducing the best available rate to stimulate additional transient demand, or whether such a reduction will lead to dilution of ADR in these other segments that the property revenue as a whole suffers.

Optimizing Prices and Availability

Because many rates are still managed by availability, it is not sufficient to optimize pricing alone – even when we consider complex rate relationships.  The lever of availability is there, and optimizing those levers remains a part of hospitality revenue management.  In developing SAS Revenue Management and Price Optimization Analytics we have used a hybrid optimization approach that allows us to recognize that some rates are price-able, some are price-linked, and some are yield-able, and some are a mix (e.g., price-linked and yieldable).

Due to the interconnectedness of both rates and inventory, decisions regarding availability for yieldable segments can dramatically influence the best available rate decision – and vice versa.  Our hybrid approach allows optimization to optimize price and availability in an integrated manner – recognizing that price changes impact demand, and that this will impact the optimal availability decisions.  Similarly, the model recognizes that, when there is a sufficient supply of high-ADR, yieldable demand it may be beneficial to raise the transient rates for the remaining rooms.

 

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