“Big Data” Revenue Management

Alex Dietz
Alex Dietz|May 17, 2013

Back in October, Kelly rather succinctly stated that “Revenue management has always been a “big data” problem.”  This is very true.  This week, as we continue our exploration of big data in hospitality, I’m going to delve deeper into the data needs for revenue management.  I’ll be exploring the important sources of information that should be driving revenue management decisions – what they are, and why they are important.  My primary focus here will be data used to support automated revenue management decisions – but I’ll also touch on the type of data that revenue managers should be looking at regardless of whether they use an automated system or not.

The Basics

For any revenue manager looking to make data-driven decisions, the starting point is to make sure that you have the following types of information at your disposal:

  • Stay history – data regarding the final number of rooms sold by date, and the revenue generated in the past
  • Inventory history – data regarding the number of rooms available for sale by date in the past
  • Future reservations – data regarding on-books reservations (this data should be captured and stored regularly – daily or weekly) for future arrival dates
  • Future Inventory – data regarding the number of rooms available for sale for future arrival dates
  • Future Rates – information regarding the rates that are available for sale in the future

If you are a revenue manager trying to do this job by hand, you probably have a spreadsheet that contains the stay history and future reservations data.  You might also have some of the other data listed above in that spreadsheet – but my experience is that most revenue managers don’t.  Why?  Because that information is in their head!  After all, every competent revenue manager knows the capacity of their property, as well as the rates that they are selling.  Many revenue managers are surprised to learn that an RM system needs these other pieces of information – after all, they don’t have them stored – why would a system need them?  I’ll cover some of this as I describe the uses of each of these pieces of information, starting with the data most commonly kept by revenue managers.

Stay History

This information is absolutely critical to understanding the overall demand patterns for a hotel.  What are our high seasons and low seasons?  What is our day of week pattern?  Is the property full on weekdays or weekends?  Does our average rate fluctuate according to this pattern, as well?  The most important and basic information that we need to manage revenue is based off of this data.

Future Reservations

Future reservations information is critical to understanding how many rooms are left to sell.  It’s pretty difficult to manage revenue on a date that’s already sold out – so this information tells us how much space is left to manage.  In addition, as we keep track of this information, we’ll be able to discern a booking pattern for our property – how far in advance of arrival do guests book reservations – and determine if this booking pattern fluctuates by arrival day of week or by season?

Future Inventory and Inventory History

Revenue management is, at its heart, the act of balancing demand and capacity through management of pricing.  As such, future inventory information is essential to every revenue management decision imaginable.  In fact, capacity is essential to the understanding of information provided by both stay history and future reservations – without matching capacity information, those other pieces of data are of limited value.  In particular, the process of unconstraining (i.e. estimating sales lost when rates and rooms were unavailable for sale) depends upon the availability of accurate historical capacity information over an extended period of time.

This is why I am so surprised at how frequently hoteliers fail to track this data: the assumption that capacity is stable over time in a hotel is frequently an incorrect one.  Wings are upgraded, and rooms out of service for a long period.  Everyday maintenance issues take individual rooms out of service for short periods of time.  Room type configurations change – and so on.  If you are a revenue manager that is currently managing revenue using spreadsheets, but thinking that you might look at a revenue management system in the future, do your future vendor a favor, and start collecting this data today.  They’ll thank you later, I promise.

Future Rate Information

This, of course, is where it all comes together.  After all, if managing rates and availability is the problem, then knowing what the rates are and how they are sold is pretty essential information, isn’t it?  And most revenue managers do have a strong understanding of the rates that they sell, including important factors such as qualifications for rates, typical contract elements (for contracted rates at the property), distribution costs (for distributed rates), and so on.  It is this broad understanding of rates that allows the typical revenue manager to handle what is obviously a very complex job.

Capturing this information outside of a Property Management System (PMS) or Central Reservation System (CRS) is often quite difficult, though – rate data is both tremendously complex and highly dynamic.  In fact, revenue management systems frequently don’t really even try to keep up with all of the complexity and change.  They do this by simply capturing historical average rate information and forecasting the rate value patterns forward.  In most cases, and done properly, this compromise works fine.

But that’s not all…

Next week I’ll continue our investigation into “big data” and revenue management, with an exploration of the data that is critical to revenue management analytics, including the “next generation” needs of true rate optimization solutions.

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Making analytics more approachable with Data Visualization

Natalie Osborn
Natalie Osborn|May 3, 2013

At the Analytic Hospitality Executive, we have been focused on creating strategic analytic cultures in hospitality organizations. Last week I took a deeper dive into information management, and what can be done to get your data in shape and flowing around your enterprise. This week we are taking a closer look at how to make your analytics more approachable with data visualizations.

So, what is Data Visualization?

Data visualization is the representation of data in a pictorial or graphical format. The purpose of data visualization is to simplify and promote the understanding of data values, and communicate important concepts and ideas.  Data visualization gives business users the ability to use information intuitively, without deep technical or analytic expertise.

Analytic skills are in short supply.

Let’s face it – in hospitality organizations analytical skills are often in short supply. Moving forward, in the US it is estimated that demand for deep analytical resources will be 50 percent higher than the supply by 2018[1]. As a result, hospitality organizations will need to figure out a way to make analytics more approachable, so that analytics become more accessible to more parts of the business. Data visualization can help – tools that make pervasive use of visualization make it easier for non-technical personnel to “see” the point of even complex analysis.

Data visualizations enable non-technical users to experience and share “aha moments” with an impact unmatched by static graphs, spreadsheets or reports. They allow a user to move from simply collecting and reporting data to deriving business insights from that data. Advanced data visualizations can support more in-depth and complex analytics. A visual tier that sits on top of the analytics program lets users view the results of complex analytics in a format that is easy to digest.

With the right exploration and visualization tools in place, more people in more departments across your organization will be empowered to take advantage of your data and analytics. At the same time, your analysts can focus their efforts on solving the tougher analytic questions, versus preparing and reporting data. However, it is important that the data visualization tools you choose are robust and dynamic. You will need fast answers, drill-down capability and exploration capabilities to meet all of the dynamic needs of a hospitality organization.

Example of a data visualization for slot machines in a Casino

Speed to insight, and better results.

Analyzing data and displaying the results with graphs and charts makes patterns, trends and outliers easily visible.  Analytic visualizations are critical to gaining fast insights from your data. If sophisticated analyses can be performed quickly, even immediately, and results presented in ways that showcase patterns and allow for querying and exploration, people across all levels of your organization can understand and derive value from massive amounts of data faster than ever before.

Faster insights also drive better results. A recent article from McKinsey refers to the tangible benefits companies are already seeing “when companies inject data and analytics deep into their operations, they can deliver productivity and profit gains that are 5 to 6 percent higher than those of the competition”.  Not only that, an IDG Research survey conducted as part of a SAS and CIO Marketplace research report identified that,  of those organizations that are considering using data visualization, 77% of respondents cite improved decision making as a top benefit, while 45% cite better ad-hoc data analysis and 44% cite improved collaboration.

Data Visualization is an important part of the journey to a achieving a strategic analytic culture at your organization. Using data visualizations to put analytics in the hands of more and more people in your organization can help drive you towards a strategic analytic culture. How are you using data visualizations in your daily operations?

[1] Sources: US Bureau of Labor Statistics, US Census, Dun & Bradstreet: company interviews, McKinsey Global Institute analysis.

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Hospitality information management: the foundation of a strategic analytic culture

Natalie Osborn
Natalie Osborn|April 18, 2013

Last week Kelly gave us an overview of how to build a strategic analytic culture from the ground up. This week I want to dive into one of the areas that Kelly highlighted - the commitment to information management.

Information management is the foundation for a strategic analytic culture and requires creating, cleansing, storing and accessing information from across your enterprise. That means more than just being able to get to your data. It also means building common, agreed-upon definitions of key metrics, so that when executives review information they can spend their time making decisions rather than arguing about definitions.

Business analytics is about using data to discover insights that change the way a hospitality organization operates – elevating it to a strategic analytic culture. Without a strong foundation of reliable and accurate data, results are suspect, and buy-in becomes impossible. A sound information management strategy puts you on the road to analytic success by giving you full confidence in your data.

Upgrade your information architecture.

 No matter how you feel about the term big data, you can’t disagree that every hospitality organization at least has “large data.” Think about all of the different types of data across the typical hospitality organization: from individual reservation history to guest folio details, or capturing every interaction in a guest profile – not to mention digital data like Web page behavior, mobile tracking or unstructured text data from social media. To effectively use the insights trapped in these volumes of data, you need a modern data infrastructure that can support enterprise-class analytics, multiple projects and dynamic visualizations.

Bridge the gap between IT and the business units.

The IT department needs to rethink the way that data is formatted and presented. The data requirements for predictive analytics, business intelligence and reporting are very different from traditional relational databases. Today, data needs to be structured to support different kinds of analytics. This is where the culture of information management comes in. A strong partnership between IT and the business units must be fostered to ensure that the infrastructure supports a new way of looking at the business. Going back to the resource investment required, a key new resource to add to your organization may need to be a “translator” between IT and the business units – someone who understands how to interpret business requirements and put them in an IT context.

Capitalize on advanced analytics, not reporting.

For analytics to truly be a game changer, hospitality organizations need to recognize the difference between reactive and proactive decision making. Using your data to create reports, drill-downs or alerts helps you to keep a finger on the pulse of your business. But these things only show you what happened. They will not tell you why the problem is happening or what effect it will have in the future.

Predictive analytics, like forecasting and optimization, can help you figure out why things are happening, show you what will happen next, or even lead you to the best alternative action, considering all of your operating constraints. Hospitality organizations that use predictive analytics to move from reactive to proactive decision making can change the way they operate. They are no longer fighting fires. They cease to be surprised. Instead, they can stay one step ahead of trends, set strategy and achieve goals. They gain advantage over the competition, increase value to shareholders, and continue to surprise and delight their guests.

Over the next several weeks, we will be drilling down on the topic of data management and visualization. How are you leveraging the data assets of your organization?  Do you have access to reliable and accurate data that conforms to the definitions and metrics that are agreed-upon by your entire organization?

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Creating a strategic analytic culture - from the ground, up

Kelly McGuire
Kelly McGuire|April 4, 2013

We’ve made the point in previous blogs that analytics help hospitality executives achieve that critical balance between providing a meaningful guest experience and meeting revenue and profit obligations.  The balance is achieved when analytics programs are promoted from departmental initiatives to part of enterprise wide strategy development.  Hospitality executives face the challenge of fostering this strategic analytic culture within their organizations.

Strategic analytic cultures have the following characteristics:

  1. Executive management commitment – A strategic analytic culture starts and ends with senior leaders in the organization committed to enterprise-wide use of analytics, demanding “proof” for all initiatives and decisions
  2. Uses analytics to set business strategies – Business strategies are set and justified with analytics, not gut instinct or aspiration.
  3. Commitment to Information Management – Good analytics are nothing without good data.  An organization committed to analytics is passionate about creating, cleansing, storing and accessing their analytics-ready information assets seamlessly across the enterprise.
  4. Enterprise use of analytics – Not every department has to employ PhD statisticians, but widespread access to and use of analytics is necessary to be strategic.
  5. Culture of Fact Based Decision Making – Organizations with a strategic analytic culture are driven to back up every decision with data.  They don’t rely on gut instinct or past history.

Obviously to achieve the characteristics listed above, organizations will need to invest resources in people, processes, organization and technology.  Any one of these alone will not get you there.  Three focus areas help organizations build a strategic analytic culture:

  • Business Analytics Skills and Resources
  • Information Environment and Infrastructure
  • Internal Analytic Processes

A focus on Business analytics skills and resources involves finding the right balance of resources and making analytics more approachable.  Your resource strategy must strike a balance between business acumen and analytical rigor.  Make sure you have enough of each so that the analytics meet the needs of the business, and the business can be supported by the analytics strategy.  In today’s labor environment, analytical skills are in short supply.  As you strike that balance, you will want to augment your technical resources by making analytics approachable to the business users as well.  Highly graphical, wizard driven tools allow business users to quickly and easily visualize their data and apply analytics without needing to be PhD statisticians.  They can find their own answers faster, and better direct your analytic resources to the problems that require deeper analysis.

The second focus area, Information Environment and Infrastructure, deals with what Natalie wrote about last week: upgrading your information architecture, bridging the gap between IT and the business, and capitalizing on advanced analytics, not just reporting.  A strategic analytic culture gets better answers faster with big analytics.  A big analytics environment requires a different approach to information architecture.  Relational databases do not store data in an analytics-ready format.  Some modernization is required, and IT needs to be thinking proactively about future growth and development to be ready to answer the needs of the business as they arise.  To continue to be responsive, IT will need deeper relationships with the business. They must understand how and when users will need the information in order to architect the right environment to deliver it.  Being strategic means becoming proactive instead of reactive.  Your analytics environment needs to accommodate advanced proactive analytics, rather than simply reporting and visualization.  Only with these high-powered, high-impact analytics can the organization anticipate the future rather than reacting to the past.

Finally, focusing on Internal Analytic Processes sets the organization up for a sustainable, strategic analytic culture.  Analytics need to be managed as an ongoing process - not a one off project, and internal processes around the analytics program should facilitate collaboration.  Only when you have systematized processes for accessing data, exploring that data, creating models, deploying the models into production and operationalizing results, can you facilitate extension of analytics across the enterprise.  Collaboration between departments and users means decisions are made considering all possible information, not just what is available to one department.  When departments collaborate in this manner, enterprise-wide business strategy is set using the analytic results.

So, how do we get started? 

The journey towards a strategic analytic culture starts with a small, highly visible, step.  When I talk to executives that have established strategic analytic cultures, the process started from one of two directions, either a CEO that was committed to analytics and fact-based decisions (like Gary Loveman at Caesars), or a department that was created as a “skunk works” project which grew in visibility as they demonstrated success.  The second path is hard, but more likely what most organizations will face.  You need to find a group of employees that are willing and able to take on the challenge, and pick a business problem that is easy to define and potentially impactful.  You may want to think about an interdepartmental project (like between revenue management and marketing), to demonstrate collaboration along with innovation.

Make sure you can answer “yes” to all of the following questions:

  • Is it a business problem that resonates with the organization?
  • Can it be easily defined?
  • Is it relatively small in scope, but relatively large in impact?
  • Are the objectives easily measurable?
  • Does the required data exist?
  • Do the skill sets required to derive results exist?
  • Is the process repeatable, or is there at least a path to repeatability?
  • Is there an executive who would be willing to provide support and guidance (formally or informally)?

As you embark on the project, ensure that you have clearly defined the personas, scope and objectives upfront.  Ensure that involved resources are empowered to prioritize this project and set up open lines of communication.  Plan for plenty of time for brainstorming, defining metrics and gaining consensus across all key players.  Most importantly once you demonstrate success, speak of it loudly and often across the organization.  Ensure your executive sponsor socializes the success with his or her peers.  Have another similar project ready in the pipeline in a different part of the business to get more executives involved.  And then brace yourself for the floodgates to open.  The executives I talk to say that once the momentum from a few demonstrated successes started, the projects came flooding in, and their positions within the organizations gained visibility and prestige quickly.

It’s a marathon, not a sprint.  Keep in mind that this is a process. It takes time, careful planning and a phased approach.  You must be patient and diligent, but the results are worth the effort!

We’d love to hear from you? How are your efforts to create a strategic analytic culture coming along?

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Managing for Big Data in Hospitality

Natalie Osborn
Natalie Osborn|March 27, 2013

As Kelly mentioned in her previous post on defining big analytics, “Big Data” is a buzzword that has been eschewed by many hospitality business leaders. When you consider the disparate operational systems that are in play at a typical hotel or casino, it’s not surprising that hospitality leaders are having challenges acknowledging big data, or even large data. In many cases, the data is so disparate, that it is difficult to think of it as a unified asset. Certainly one of the most common complaints I hear from analysts in hospitality and gaming is their struggle with getting together data that they can use for analytics.

The hospitality industry, like many other industries, is experiencing increases in the volume, velocity and variety of the data that they collect and store. In many ways, hospitality organizations are facing what their own patrons and guests are facing: there is too much information in too many places to make an accurate decision in the timeframe in which it is needed. Analysts know that the answers to business problems are buried in their data, but lack the ability to extract the data and determine those answers.

Whether you think of it as big data or otherwise, these data problems place a huge amount of pressure on hospitality CIO’s and their IT departments. The data requirements for predictive analytics, business intelligence and reporting are very different from managing a relational database in the context of operational systems. The demand for analytic-ready data is only increasing as the use of analytics spreads across hospitality and gaming organizations. For many hotels and casinos, consolidated information about their guests and patrons is essential to delivering on customer service, managing loyalty programs, and targeting appropriate offers and promotions.  When you add the need to access this information in near or real time for online and mobile environments, it becomes critical. Hospitality organizations that are getting started with a solid data management approach are paving the way to future success with analytics.

A solid data management approach is one that pulls data from many different sources, cleanses that data as it comes into the analytic environment, and rationalizes data by key assets, for example by customer or by product. All of that sits on top of an enterprise data access foundation that makes it easy and seamless to access multiple data sources. The first step to a focus on data management is to bring the data together, improving the flow and flexibility of data with data integration programs. Next, the data needs to be assessed and improved with respect to quality. Monitoring programs ensure that this quality continues. Lastly, enterprise data access provides comprehensive data access connectivity and seamless, transparent access to virtually any data source, allowing any area of an organization to leverage its strategic data assets.

Once accurate and credible data is flowing across the organization, IT departments can start to leverage self-service applications that allow business analysts to do their own data visualization and exploration. This not only helps alleviate the pressure of data demands on IT organizations, but also helps with the spread of analytics across the enterprise.   Lastly, the practice of analytically derived, fact-based decision making will only become sustainable if endorsed by the executive team, allowing hospitality organization to make the most of their big data assets.

Is your organization already managing for big data? What data management approach are you taking to manage your big or large data?

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"Big Analytics" for Hospitality

Kelly McGuire
Kelly McGuire|March 13, 2013

In last week’s post I proposed that the hospitality industry move past debating whether they have a big data problem and move towards admitting that they have a big analytics opportunity.  In this post, I want to give you some more specific examples of the benefits of “big analytics” (and “big data analytics”).

In Natalie’s post from earlier this month, industry experts weighed in on the opportunities and challenges associated with analytics in hospitality in today’s market.  The overriding theme was around accessing data and turning it into meaningful, actionable information quickly.  Big Analytics is all about speed to decision making.

For hospitality, big analytics comes into play in two high level areas:

  1. Making analytical decisions in real time: The guest is standing in front of you, on your website or even just passing by, and you need to get a relevant message to them right at that moment.  Your manager is on the floor trying to figure out what do next based on current operating conditions.
  2. Running scenarios, evaluating options and testing alternatives: Even if you don’t have to make a decision in the moment, faster answers give you more time for analysis.  Multiple runs with adjusted parameters or dynamic what-if analysis provide opportunities to do a more complete evaluation before making a decision.

Big Analytics for Real Time Analytical Decisions

Personalization has always been a hot topic (and big challenge) for hospitality companies.  Delivering that memorable experience which cements loyalty and increases return likelihood on a mass scale across huge global enterprises with high line-level turnover is a seemingly insurmountable challenge.  But, if you can interpret behavior and deliver a relevant recommendation to the employee (or device) that the guest is interacting with while the guest is right there, “mass personalization” becomes achievable.  If you get it right, you have a “surprise and delight” moment.  If you get it wrong, you have an artificial, seemingly scripted interaction that makes the guest feel like one of the crowd.  Big analytics lets you run predictive models based on current, observed guest behavior compared to their past behavior (or that of similar guests) to determine what you can offer, mention or provide to encourage the behavior you want – in time to make it happen.  You can uncover cross and up sell opportunities, fill empty restaurant seats, or just wish someone a happy birthday or a welcome back – all right in the moment.

Big analytics also comes into play when your guest is on your website.  Matching what you know about them, or how they compare to previous visitors, with their current click patterns can help the analytics predict what content should be surfaced to maximize their likelihood to convert.  Extend that to the mobile strategy with location data (and, by the way, now we’re starting to get into some big data – I’m just saying…).

What about operational managers on the floor that want to make real time, proactive decisions about what gaming tables to open and close, how to deploy staff or which restaurant to send guests to?  You don’t have time to wait three hours to calculate an updated forecast, when you are trying to decide whether you can save labor cost by cutting a few servers early.  Is gut instinct enough when high revenue impact decisions like raising and lowering minimum table bets are on the line?  Big analytics helps you to calculate the advanced predictive models that support your management decisions at the speed of business.

Big Analytics for Scenario Testing

Many hospitality companies may argue that they either aren’t ready for real-time or don’t need it.  Whether the scenarios I described above are relevant or achievable for your organization or not, there’s a very strong argument for using big analytics to help your analysts make better decisions faster.  After all, good analysts are in high demand, so increasing productivity – and making better decisions – with your existing analysts will have a huge positive impact on your business.  Here are some hospitality examples where big analytics moves the needle, even if “real time” isn’t the goal.

As the decisions that marketing departments must make about their campaign plans become more complex, marketing departments are turning to optimization to help.  Marketing optimization algorithms output a contact strategy for each campaign that meets a campaign goal (maximize response, minimize costs), considering all relevant campaign constraints (opt in preferences, channels, contact strategies, guest eligibility, prevailing rate, blackout dates, budgets, etc).  As the number of available offers increase, along with the size of the customer database and the number of channels, so does the size of the problem.  Without big analytics, the answer comes back in hours.  This could be fine, if you don’t have any options within the problem.  Frequently, however, the parameters of the problem have ranges (guest can be contacted between 2 to 4 times a month, channels can be added or removed, eligibility can change).  With big analytics, you can run multiple versions of the problem in the same amount of time, and determine whether relaxing or removing a constraint gives you an even better answer.

Revenue management is the hospitality industry’s classic big analytics problem.  To come up with a pricing recommendation, detailed forecasts are calculated and fed into a complex optimization algorithm. The detail and complexity requires intensive processing power, and many passes at the data to solve.  In order for results to be relevant, at a minimum, these forecasts and optimizations need to be able to run overnight so that prices can be updated once a day.  If it takes any longer, with new reservations coming in all the time, recommendations will be out of date before they are produced.  Without big analytics, even to only meet this overnight goal, revenue management systems would have to make sacrifices and take shortcuts – summarizing data, restricting forecasting methods and replacing true optimization with heuristics.  With big analytics, harder, more complex problems take minutes or even seconds to run.  Revenue management algorithms can utilize more and more detailed data, forecasting options can be expanded, true optimization can be utilized, AND the entire process can run more frequently – providing better pricing recommendations faster.

Even better for revenue managers, with big analytics, scenario testing and “what if” analysis is now possible.  Revenue managers often see the need to override the system based on their unique market knowledge (or demands from the general manager).  Without big analytics, revenue managers make the adjustments and have to wait until the next day to see the impact on price recommendations.  With big analytics, revenue management systems can support “what if” analysis.  With the push of a button, revenue managers can see the impact of parameter overrides on the highly interrelated pricing problem before they put the overrides into production.  Not only can they better support their own decision making (and learn how the system reacts to certain types of changes), but they also have evidence to support their position when they disagree with the GM’s demands!

Clearly Big Analytics present big opportunity for hospitality. These are only a few examples.  Can you think of any other areas where better answers faster would benefit you?  We’d love to hear from you!

Have you seen the "Hospitality research you should be using now" webcast?

 

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Defining "Big Analytics"

Kelly McGuire
Kelly McGuire|March 7, 2013

Big Data has become a big buzzword in the market.  So much so in fact, that many business leaders are eschewing the term just on principle.  Underneath the hype are some very real business issues that bear definition and discussion.  Big data is not just about volume, it’s also about variety and velocity.  If many different types of data are coming at you fast, you are in just as much trouble as if you have petabytes of the same kind of data.  Big data is relative.  Gartner says that big data happens when you can’t manage or analyze the data that you have.

Regardless of whether you believe you have a big data problem or not, what most hospitality companies really have is a big analytics problem.  If you regularly push the “go” button on your software and then go out for coffee while you wait for the answer, you may have a big analytics problem.  If the Friday afternoon ritual involves kicking off a query just before you leave the office (and spending the weekend hoping you set it up right), you may have a big analytics problem.  When you reach the point at which you can’t derive meaningful information from your data, you definitely have a big analytics problem.  You get the idea.  There may not be a lot of records, but if you need to make multiple passes on the data, running complex algorithms with each step getting answers can take hours.  While you may not always need instant responses, hours long processing limits your ability to explore, test, and react, let alone to look forward.

We’ve spoken before in this column about the advantages of making proactive decisions with analytics.   Reactive analytics are standard business reports, ad hoc reports, OLAP cubes or even alerts.  They are very useful to keep a finger on the pulse of your business, but, since they are descriptive, and based on historical data, they can only help you fight fires, reacting rather than managing.  Proactive analytics – like forecasting, predictive modeling and optimization are forward looking.  They can help you pick up trends, predict the best action to take next or arrive at the best possible answer considering all of your operating constraints.

Reactive analytics are simply Business Intelligence.  Whether on large data or big data sets, you are really just visualizing information so you can react.  There are many reasons why reactive analytics are important (not the least of which is to build regulatory reports or view budget performance), but advantage comes when you are able to deploy proactive analytics, moving from business intelligence to “big analytics”.

Big analytics streamline the technical processes associated with large data sets and complex advanced analytics.  This speeds up the time it takes to calculate an answer.  Instead of waiting hours, it takes minutes.  Even if you don’t need an answer back instantly, saving this kind of time means more time to test options so that you can find the best possible answer, more time to make decisions, and more time to solve new problems.  With “big analytics” the data doesn’t feel as big anymore.

The chart below helps to put big data and big analytics into context.  You could have large data or you could have “big data” but either way, your ability to apply proactive analytic capabilities to your organization will result in competitive advantage.

Source: http://blogs.sas.com/content/corneroffice/2012/10/08/what-kind-of-big-data-problem-do-you-have/

With any analytic initiative, success is determined by how effectively your organization harnesses data and uses it creatively, builds models that enable you to make more accurate predictions and to improve outcomes and transforms itself so that it is more agile in acting on reliable insight.  In my next blog post, I’ll talk more specifically about how hospitality organizations can apply big analytics.

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Hospitality research you should be using now

Kelly McGuire
Kelly McGuire|February 28, 2013

Is our rewards program really improving our bottom line? What is the impact of social media on our hotel’s performance? Do earnings announcements impact our stock prices? How can we use price more strategically? These are some of the most important questions decision makers in the hospitality industry can ask about their organizations, and are questions that we have been discussing here at the Analytic Hospitality executive over the last several weeks in the lead up to our February webcast “Hospitality research you should be using now.”

During the webcast, you’ll hear Mike McCall, Ph.D. discuss the results of his latest research project, ”How Big is Too Big? Decomposing the Effects of Reward Program Enrollment on Profitability.”  Chris Anderson, Ph.D., talks about his Center for Hospitality Research (CHR) report, “The Impact of Social Media on Lodging Performance”. Pamela Moulton, Ph.D., reveals how earnings announcements impact hospitality company stock prices as she discusses her CHR report “Earnings Announcements in the Hospitality Industry: Do You Hear What I Say?,” and SAS’ Maarten Oosten, Ph.D., reveals how pricing can be used as a strategic tool.

I hope that you can join us for the webcast, and afterwards join the discussion here in the comments.

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Game-changing hospitality analytics start with good data

Natalie Osborn
Natalie Osborn|February 25, 2013

Lately, those of us at the Analytic Hospitality Executive have been talking a lot about what represents the biggest game-changers for the hospitality and travel industries. You’ve heard us speak about big data and big analytics  here before, and you can expect to hear a lot more on hospitality analytics throughout 2013. But – don’t just take our word for it. While we were at the Cornell Center for Hospitality Research Summit, we asked several of the thought leaders that attended the summit what they thought about the role of analytics in hospitality and travel.

Cathy Enz PhD., Professor of Strategy at Cornell University shared that the role of analytics in hospitality and travel is increasing in substantial and notable ways. “I think using information rather than our hunches, our superstitions, our past history is a very powerful business tool,” Cathy said. “Our biggest challenge is to turn all the information that we get into usable knowledge to help us make decisions. The challenge of dealing with Big Data is how we translate it into executable information.”

Ted Teng, President, CEO at The Leading Hotels of the World, shared Cathy’s sentiment. “We are an industry of the emotional decisions. We badly need analytics and good data for us to make the right decisions.” Ted explained that the hospitality market has completely changed and industry operators can longer rely on how they did things 20 years ago.  “There’s a lot of talk about big data out there. I’d be happy with just small data, some data, that allows us to make better decisions that are based on facts rather than based on our emotions.” Ted has a really interesting point, just collecting data is not going to help make the decisions that the hospitality industry needs to solve. It’s the using the right data in the right ways that will make the difference in how analytics in used in our industry.

Kirk Kinsell, President, The Americas, InterContinental Hotels Group, sees the role of analytics in the hospitality industry is to get to a single version of the truth. “It’s hard enough acting on the environment out there, let alone having multiple different points of view inside of an organization,” Kirk explained. “Getting a single version of truth allows the hospitality companies to focus and act together to drive for better outcomes,” he said.

Mark Lomanno, Executive Board Member at newBrandAnalytics, sees the role of analytics only increasing. “Traditionally the role of analytics has been more in the financial metrics measurement category, to some degrees in the operations category, and in the marketing category, however in the future all those will come together,” Mark said. He explained that over time, he sees that online hotel reviews and comments in social media will replace traditional guest satisfaction measures as the primary gauge of customer satisfaction, and that you will be able to start predicting occupancy and rates by the quality of the comments that are on your hotel. “This will force operations and marketing to work very closely together to react very quickly to what the consumer is saying,” Mark said.

The use of analytics is becoming more wide-spread and more accessible for hospitality companies. Analytics are not just the concern of revenue management, but are becoming standard operating practice in finance, operations and marketing. Analytics can go a long way towards achieving the delicate balance between customer service and revenue and profits, as we discussed here a few weeks ago. However, the process of implementing game changing analytics is a journey that must be taken one step at a time, and sometimes that first step is the hardest. To be successful, hospitality companies must first get control of their data, by implementing repeatable data management practices. Once data becomes a manageable asset, you can start to move through the different stages of analytic maturity. Over the next few months, the analytic hospitality executive will be drilling down into big analytics, data management and visualization, representing the first stages of a journey toward analytic maturity. I hope that you will join us!

Later this week, we will be back with details of how to connect to our February webcast “Hospitality research that you should be using right now.” In this webcast we feature research from Chris Anderson, Pamela Moulton and Mike McCall, all with the Center for Hospitality Research. Joining them is SAS’ own Maarten Oosten, who talks about how he sees pricing as a strategic tool. Check back with us on Thursday for details.

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Can the impact of enrollments in customer loyalty programs be measured?

Natalie Osborn
Natalie Osborn|February 15, 2013

Does the investment you make in enrolling your customers in loyalty programs result in quantifiable benefits for your firm? That’s the question that Mike McCall, Ph.D., and his co-author Clay Voorhees Ph.D., are attempting to answer in their current research project for the Cornell Center for Hospitality Research (CHR). Mike will provide more detail on his findings so far in our upcoming Cornell CHR/SAS webcast later this month, but I had a chance to catch up with him in advance of the webcast.

“Few firms have attempted to identify whether loyalty programs provide lift, or are just another cost center,” Mike told me. “The real question for us to answer with this research is if there is a way to measure that lift,” he explained. And before you can identify in which camp your loyalty program belongs, you need to understand why people join loyalty programs. Mike thinks that most people join loyalty programs to feel special. “If a firm is enrolling everyone that comes through the door they may not be creating the kind of program that encourages loyalty through feeling special,” he told me. In fact, a firm may be bringing in people who are looking for discounts or deals and not necessarily looking to be one of your firm’s regular customers.

“Not everyone who joins a loyalty program necessarily contributes to your bottom line,” Mike elaborated. Mike looked at two groups of people when conducting his analysis: those that joined the program and those that didn’t. “It was those people who were made to feel special by the program that tended to contribute positively to the ROI of program,” he said. When I asked how customers were made to feel special, Mike explained that they were those customers were promoted within the program within the first year.

Mike encourages firms to look carefully at the customers they are enrolling in their loyalty program, and think about the various characteristics they would want from a loyalty program member. “For example; are they going to be repeat customers, are they going to spread positive word-of-mouth, are they going to the kind of customers that you will want to promote to a higher level in the program?” Mike explained. Mike notes that those customers that you are likely to promote are those that directly contribute to the lift gained from loyalty programs. “Those are the customers that you need to focus on enrolling,” he said.

So far, there has not been a lot of analysis of the impact of loyalty programs. Generally, Mike thinks that there is a lift that can be gained from loyalty programs. How you should maximize, or optimize that lift is another story. “To get started, you need to collect the right data on your customers,” Mike explains. Then, it is all down to analytics. Mike finds that many firms implement loyalty programs because every other firm has one. By using analytics to understand what drives loyalty and profitability from all of your customers, you can then identify a strategy for your loyalty program that is aimed at maximizing loyalty and profitability. “That’s how you should be measuring your loyalty program, not just by how many customers have been enrolled,” Mike said.

You may remember Mike from our conversation last February on whether loyalty programs actually drive customer loyalty. Mike will present the results of his latest research,”How Big is Too Big? Decomposing the Effects of Reward Program Enrollment on Profitability” in the upcoming SAS and Center for Hospitality Research joint webcast later this month.

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