We have a new address!

Greetings to all of our Analytic Hospitality Executives!

We have consolidated the Analytic Hospitality Executive blog with the SAS Voices blog.  All future posts from Analytic Hospitality Executive bloggers will be published on the SAS Voices blog. Please update your bookmarks and your RSS feeds with these links to make sure you don’t miss future posts:

You can find all of your old favorite posts from the Analytic Hospitality Executive by searching on the tag TAHE.

Thank you for your support - we look forward to seeing you over at SAS Voices.

Post a Comment

The Connected World - IoT Data for Travel and Transportation

I should have expected it! The end of January/beginning of February always seems to bring some kind of major snow event in the East.  Here in Cary, NC we experienced only a couple of inches of snow, but that snow covered a solid inch of ice underneath. Several hundred miles north, my daughter walked through almost two feet of snow at her college campus in Philadelphia. And it got me thinking…

Data is like snow; or better, data is like any kind of precipitation.  Small amounts of data can cause headaches and, in our case, muscle aches, as we tried to crack through the inch of ice under our snow in Cary to clear our driveway. But when a lot of data comes at you fast and furiously, like the snow falling during the recent blizzard, you have to deal with both the volume and the velocity of the data. The Washington Post reported an average of more than 20 inches of snow in 2-3 days across 68 square miles equaling almost 3.5 billion cubic feet of snow.

In our increasingly connected world, the data available for travel and hospitality companies can be blizzard-like, with volumes of big data arriving every sub-second.  Or, it can be smaller and slower, but still difficult to understand, as if you have to crack through the ice to get a clear path.  Today’s post builds on previous discussions about issues in the connected world; specifically, what does the Internet of Things (IoT) mean for travel companies in terms of the available data. In future discussions, we will explore the analytics which are necessary to transform this IoT data into Intelligence for the Connected World.

The advent of the Internet of Things (IoT) provides the ability to collect numerous kinds of data, different than previously collected, with a frequency of collection which has never been seen, leading to a volume of data that, without proper stewardship, will cover us like a January snowstorm in Washington, D.C. Sensors and beacons are now installed everywhere – in hotels and airports, in train stations and train compartments, in aircraft, on your key chain, and, of course, in your phone; these sensors can track movement, purchases, and, especially customer behavior. Bluetooth and wireless technology enables this sensor data to travel to gateways for collection and, more important, analysis; because what use is there in collecting this data without analyzing it?

A primary source of this sensor data will come from user smartphones. When the majority of your customers are carrying a smartphone, it’s time to use the capabilities and data provided by that smartphone; as a starting place, realize that smartphones have sensors to detect the device’s motion and location, and they have sensors to detect light and noise as well.  Additionally, installed smartphone apps collect user input and transform that into data. All of this data can be collected, aggregated – and don’t forget, enriched with other data – and finally analyzed to provide insight into actual behaviors, providing more insight than data that is self-reported. I’m reminded here in this election year why both pre-election polling and even exit polls can be far off.  People might say one thing, but it’s their actual behavior that counts!

Let’s switch gears from customer-related data to asset and equipment data for a minute.  Here, too, an abundance of sensor-related data has emerged.  Not only are these sensors measuring everything from temperature, pressure, speed, emissions and hundreds of other elements within a plane or train or car (the “asset”), but they are now smart sensors, monitoring the quality of the measurements and performing advanced calculations as they are transmitting this data to a central server for further analysis.  The Internet of Things means that these smart sensors can be empowered to control the larger asset in which it sits, increasing the efficiency of the asset in near real time.  And, data across all of these sensors can be used holistically to improve operations by predicting part failure and scheduling maintenance to avoid failures.  Oh, but now I’m verging into the analytics. But really, it’s virtually impossible to discuss the available IoT data without considering the analytics that are integral to effective use of the data.

I’ve mentioned a plethora of data and data sources that arise from the installation and application of sensor data in travel and transportation companies.  Natalie will build on this initial discussion and discuss how these data are being utilized in hospitality companies.  As you build your IoT strategy and develop the use cases for applying this data to address business issues in your company, what additional data sources have you encountered? Where are there opportunities for new data from new sensors or from collecting IoT data to use in your environment?  Let us know and we can start 2016 with a lively discussion on the IoT in travel, transportation, hospitality and gaming.

Post a Comment

9 Ways for Hoteliers to thrive in 2016

2015 was a busy year for the hotel industry. ADR, Occupancy and RevPAR achieved record highs. We saw extensive merger and acquisition activity, culminating at the end of the year with the acquisition of Starwood by Marriot and Fairmont by Accor (and don’t forget Expedia’s Orbitz acquisition, of course). Favorable economic conditions are expected to continue, but it is difficult to predict what the impact of the acquisitions will be. As one of my favorite analytic hospitality executives always reminds me, it will probably be the “unintended consequences” that we need to watch out for as the industry adjusts to this consolidation. 2016 will be very interesting, to say the least.

In the tradition of The Analytic Hospitality Executive, let’s kick off the new year with some tips to set you up for success in 2016 and beyond. Here is my advice for what we need to stop doing, start doing and do more of:

Stop operating in silos – This is not the first time I’ve said this. Probably won’t be the last. Departmental barriers and conflicting incentives are holding us back. Hotels must start synchronizing decision making across marketing, revenue management, finance, development and operations, and need to be supported by a collaborative and business-focused IT organization. There have been some encouraging signs that we are moving in this direction. In 2015, Hilton Worldwide pulled together Hilton reservations, customer care, regional marketing, eCommerce and revenue management, under Chris Silcock as Chief Commercial Officer. Wyndham Hotel Groups recently announced that they will consolidate sales and revenue management under Kathy Maher, SVP, Global Sales and Revenue. Organizational changes like this will go a long way to facilitating more coordination. Beyond consolidating related functions under strong leaders, however, we must foster cross-departmental decision making and reward decisions that holistically benefit the organization.

Stop relying on gut instinct – Data and analytic systems have become more accessible. There is no longer any excuse to rely strictly on guess work or gut instinct.   Hoteliers need to foster a culture of fact-based decision making, or risk falling behind. I am not suggesting that a revenue management system or a data mining platform can replace an experienced revenue manager or marketer. In fact, it’s quite the opposite. The systems are there to provide decision support, but the manager needs to make the decisions through the lens of their experience and business acumen, based on what the data tells them. I’ve been encouraged to see more hospitality organizations investing in data and analytics systems and people over the last few years. This must continue.

Stop blaming the OTAs for everything – We spent a lot of time in 2015 talking about rising distribution costs, and the OTAs got vilified all over again. Now, I don’t disagree that rising distribution costs should be of concern to hoteliers. Cindy Estes Green and Mark Lomanno have provided strong evidence that we need to shift this trend, or we will be in trouble. However, we also need to keep in mind that we have control over our distribution costs – it’s not something that the OTAs are doing TO us. It is easy to sit back and simply blame the OTAs. The OTAs have proven their value in generating incremental demand from markets that an individual hotel could never reach on their own. It is crucial to manage the relationship with these OTAs carefully, in light of your pricing strategy and the marketing opportunities associated with each partnership. Consider the concessions that Hilton Worldwide recently got in their negotiations with Expedia. Of course, being a large global company helped, but so did having a strategy, and knowing the role that the OTAs would play in that strategy. It wasn’t just about commissions, it was about terms. Instead of blaming, build your business strategy considering the opportunities to use the right distribution partners as an integral component. Make the costs you pay worth the opportunities you achieve.

Start preparing for the future – This business isn’t getting any easier. Consumer behavior is changing rapidly, as alternatives for research and booking flood the market. Technology continues to evolve, creating more data sources and more operational complexity. Recent hotel company acquisitions will change the competitive landscape. It is up to each of you to prepare yourselves and your organizations for this change. You need to stay updated in the latest trends in technology and consumer behavior. You need to learn how to evaluate a technology investment from a business perspective, and communicate those needs clearly with the IT organizations that support you. You are taking a great first step by reading our blog. Attend conferences (some outside of the hotel industry), read articles, look at software demos, ask for proof and interface with your peers. Organizations like HSMAI have great educational materials. Most importantly, however, is to keep asking questions.   Don’t take anything at face value. With all the noise out there, it is up to you to cut through the hype and determine what will be most effective in moving you and your organization forward.

Start becoming more prescriptive –As more and more different data are available and the influences on our business grow and change, it’s not enough to be able to predict what will happen, you must be able to prescribe solutions to achieve desired outcomes. It’s great that your revenue management system can tell you that you will get to 90% occupancy next month, but what if business goals require you to be at 95%? In the current climate it can no longer be about knowing what will happen, it’s about knowing what to do about it. A prescriptive manager can synthesize a wide variety of inputs, read the market and work with other departments.   Start asking the question “So what?” and “What do you plan to do about it?” of your managers and analysts now. This is useful during any economic climate, but think about how powerful it will be if you were already practiced at this kind of business decision making as we move into the inevitable next downturn.

Start testing to innovate - Speaking of the economy… Strong conditions provide the opportunity to invest resources in experimentation and innovation. 2016 is the time to try out some new techniques, new programs, new strategies and new technologies. Even if you don’t have the budget for a huge technology investment or massive program change, you can use what you have access to do a bit of experimenting. The most important factor is to establish a solid testing methodology so that you can clearly demonstrate success. Analytic hospitality executives are probably familiar with the concept of A/B testing. In this process, random groups of real consumers are shown alternatives and statistical tests show which treatments are most effective. It can be as simple as testing which image increases conversion or which email title generates the most openings.   Every hotel company should learn this technique. It’s so easy to deploy in the digital environment, you’d be crazy not to. You could find you are missing out on a huge revenue opportunity only because you are not talking to your guests in the right way.

Do more collaborating – Going along with “stop operating in silos”, we need to foster a more collaborative environment across the hotel – at corporate and the property level. Even if your company has not yet aligned organization structure or incentives, you can still build informal cross-functional teams and start working through broader strategic initiatives together. As hotel companies take on more complex initiatives like personalization (described in a previous blog), it will become more crucial to get input and buy-in from across the organization. Take the time to learn about goals, metrics and operating procedures across the organization, and get used to gathering input and gaining consensus.

Do more automating – There is simply no reason anymore for analysts to spend large portions of their time on data gathering and cleansing, or for managers to have to wait days to access operating reports. These routine analyses should be automated through business intelligence and analytic applications You should have an automated revenue management system, a reputation management system, a business intelligence application and an automated marketing system, so that the organization can focus on interpretation and prescriptive decision making. When analysts and IT are not stuck on routine tasks, they have more time for more strategic or ad hoc analyses. Make a point in 2016 to invest in some technology that will automate routine tasks, providing the right information to the decision makers in the formats they need to take action.

Do more strategic thinking – I’ll end with this because I say it every year – and it’s important every year. It is easy to get caught up in the day to day and not leave enough time to look forward. Particularly after this year, ripple effects will last well into the next few. You should know where you want your organization to be three, five or ten years in the future, and make a plan to get there. Follow trends and understand impacts. While you are at it, think about your own career. Where do you want to be five years from now, and what will it take to get there?

It is clear that 2016 will be a year of great change for the industry as the dust settles from 2015. We have a lot to do to keep up, but there are a lot of opportunities. In fact, it will be a year of great change for me personally as well. This is my last blog at the Analytic Executive. I am leaving SAS to take on a role leading a hospitality analytics team. The blog remains in the capable hands of Natalie Osborn and Lee Ann Dietz, so you can expect more of the same kind of quality content you’ve always seen from us. I’m not going away completely, though. I’m in the process of working out a new collaboration with the Cornell Center for Hospitality Research, so keep your eye out. It has been a privilege to be able to share my insights with you. Let’s keep working together to move our industry forward!

What are you thinking of starting, stopping or doing more of in 2016? We would love to hear from you!

Post a Comment

The Connected Traveler

A few weeks ago I had the opportunity to attend the 2015 EyeforTravel Connected Traveller Conference. Based on the spelling of the word “traveller” which my American version of MS Word continuously wants to autocorrect, you can correctly assume the conference was hosted in London. During the conference, one phrase from my favorite 80s movie kept running through my head: “No matter where you go, there you are.”

Today’s traveler, no matter where she goes – whether she is a passenger on an aircraft, train, or car-share or staying as a guest in a hotel – is always connected: before, during and after her journey or stay.  Travel companies realize that customers are expecting to have almost instant access to information about their flight; getting accurate information to the customer promptly, and to the right device – and without overloading the customer – is critical.  The same is true of hospitality companies.  At the event, a number of the speakers discussed how their companies are supporting these needs today, and how their future business and technology strategies will continue to support changing customer requirements.  I’m interested in your perspective on the Connected Traveler (one “l”, thank you), whether you think that these trends are accurate and, in particular, if the strategies being proposed will lead to success for travel and hospitality companies.

Most of the speakers discussed what travel or hospitality customers need in order to be truly connected. Dara Brady, Head of Digital Experience, Ryanair, separated these essentials into three airline-related categories:

  • Information – knowledge about flights and greater access to destination-related information
  • Communication – real-time updates on actual and potential disruptions
  • Control – ability to book flights, hotels, car rentals, transfers, etc.

Julien Nicolas, COO Europe, Voyages-sncf.com, described how SNCF perceives the customer journey, from discovery to exploration, purchase, pre-journey, journey, and commitment.  Throughout a customer journey, in which customers can be at multiple stages across different journeys, travel companies must have an omni-channel strategy where the experience is consistent no matter what channel the consumer is accessing. This will ultimately lead to a seamless experience for the customer.  And, that seamless experience should extend beyond the traditional flight/train to hotel experience.  A number of speakers emphasized the changing world that exists outside, or even instead, of this traditional experience.  From ride-sharing to customized private car to excursions to dining, the mobile/connected traveler will often participate in his own travel planning by identifying and executing these activities.

Extrapolating from the fact that all aspects of the customer journey can now be delivered to the customer directly, one might wonder if there is room for the travel agency and business travel services providers.  I think there were different opinions on that question at the conference.  Certainly, brick and mortar and even online travel agencies are seeing travel and hospitality suppliers jump into the space previously wholly-owned by these agencies and intermediaries.  In order to avoid obsolescence, the value provided by these intermediaries must be distinct and targeted to customers that prize these services.

We know that aggregation of services has been one way to serve customers: to be the consolidator across all elements of the journey, or to provide all options, allowing customers the freedom of choice not available when the supplier is the provider.  I was particularly interested in the effort of these companies to integrate on and off-line services.  For example, consider customers who research vacations through a website, but then want or need more personalized attention, ultimately deciding to visit a brick-and-mortar location.  The key to keeping these customers is to make this transition from online to offline a seamless experience; that is, the human travel agent must know who the customer is when he walks in the door and that includes what he has been researching most recently, but also includes information about where he has traveled before and what additional services he might be considering but not yet researched.  It’s remarkable to me that these travel agencies are thinking about this at the same time that Amazon has decided to open its first physical location.  Clearly, the need for human interaction, the desire to touch and feel, whether it be a book or a travel brochure, is propelling some customers to move away from the online environment to something more tangible.  “Everything old is new again,” as they say.

A different, but related, topic of discussion at the conference centered on the technology aspects of delivering these connected services.  I heard from Gabriella Gullbrandson, Business Development, SJ AB, about creating a business infrastructure that would better enable incubation and rapid prototyping of new technology ideas.  The world – and customer expectations in terms of technology – is changing rapidly.  Servicing those needs, much of which involves the need for application development, is not always quick enough when established IT organizations get involved.  Thus, creating a virtual team that can be a nexus for collecting ideas, vetting business case justifications and producing prototypes quickly is essential.  My own experience with analytics in the travel and hospitality space suggests that some form of this rapid prototyping, more than just Agile development, is so important.  Implementing analytics, like developing customer-facing applications (whether involving analytics or not), requires a process that can generate value quickly.  And it is even better when it can generate, within a short to medium timeframe, its own ROI.

In the two-day conference, the participants touched on many aspects of the connected travel experience.  Given that I arrived in London from the United States the day before, nothing hit closer to home than making sure that the connected customer travel experience is pleasant.  Pernilla Edelsvard, Head of Digital, SAS (Scandinavian Airlines, not my company) is taking that a few steps further: “Enchanting the Customer.”  While connecting with the customer, this experience has to be more than just efficient, it should be pleasing to the customer.  The ultimate purpose for companies connecting with travelers (and guests) should be to encourage loyalty and increase revenues – we are all still businesses, after all – so that customers continue to utilize and pay for our services.  She described many of the SAS initiatives to bring together customer connection with the digital world in a way that customers would continue to value before, during and after the journey.  It seems fitting to end this discussion here and get your perspectives.  What new services and technology, especially if they involve analytics, are you bringing to the forefront of your customer interactions?  How are they working?  What have you learned from these initiatives?  Clearly, “wherever you go, there you are” is a humorous and somewhat existential motto, but “wherever you go, you are ‘enchanted’” will bring much more value and revenue to your company’s bottom line.  Let’s stick with that!

Post a Comment

The Profitable Path to Personalization for Hotels

As consumers have moved to digital channels and digital interactions, competition for the hotel consumer has increased dramatically. Now, hotels not only compete with each other, but also with third-party online distributors, and even new disruptors from the sharing economy like AirBnB. However, hotels have one advantage that these other players do not. They “own” the guest throughout the stay. Only hotels have access to key information about preferences and behavior on property that can be used to build meaningful, engaging relationships, and encourage repeat direct bookings. This is why many hoteliers are advocating for personalization.

Personalization at its core is nothing but providing the same good service that hotels have been providing for generations, but doing it on a mass scale across channels, boarders, brands, and interactions. To execute this effectively, hotels need to be able to surface the right messages to the guest at every interaction point throughout the guest journey, whether through a live interaction or through a digital channel. With the wide variety of interaction points, this can be a very complex, technology and business process heavy undertaking.   It is further complicated by the fact that different departments “own” different components of the journey. For example, marketing is in charge of the pre- and post-stay experience. They must deliver the right content and communications that encourage conversions and repeat visits. Once the guest is on property, operations needs to profitably deliver on the experience promised during the pre-stay phase. Finally, if revenue management has not set up the right pricing structure for rooms, upgrades, and even ancillary services, the hotel loses an important revenue generation opportunity. All departments need to set up their business processes such that the hotel can capture as much relevant, detailed behavior and preference data to augment the guest profile so that they can continue to be relevant to each guest and to the business mix as a whole.

To execute on this vision, hotels need to be able to capture data at every interaction point in the journey, comprehend that data through advanced analytics, and then act appropriately based on the results. The big hospitality brands are investing in specialized databases to handle large volumes of data, as well as new, non-traditional data like text or location. Teams of analysts apply advanced predictive models to understand guest behavior, recommend actions to drive results, determine profitable pricing and identify areas of operational efficiency. Websites surface relevant content based on guest profiles and current search context. Real-time technology delivers messages and offers to the guest while they are on property, and powerful visualization technology puts analytic results in the hands of decision makers at the speed of business.

The good news for smaller players is that recent innovations have made the personalization technology that supports the capture, comprehend, and act cycle accessible even to smaller organizations. Responsive web design is surprisingly accessible. Cloud delivered, SaaS solutions provide access to advanced analytics without the need for a large IT footprint or a team of analysts. Accessible visualization technology is putting information in the hands of decision makers faster.

All hotels, regardless of size, should start by pulling together a cross-functional team to map out what they want the guest experience to be, and identify any operational constraints to profitably delivering on that experience. Specifically, the team should look for opportunities to use their hotel or brand’s unique assets to gather behavioral data to augment the guest profile. Once the entire organization is aligned to the personalization vision, they can together determine where to begin the strategic technology investments to support that vision. Remember that execution of the vision is just as important as the vision itself. This is why it is crucial to have all aspects of hotel operations involved to ensure that profitable delivery is possible within the constraints of the business.

The key to successful personalization initiatives is in the organization’s ability to balance the meaningful, relevant, and engaging experiences with their revenue and profit obligations. If the organization does not maintain a focus on driving profitable revenue across channels and outlets, the personalization initiatives cannot be successful. Revenue management, therefore, has a crucial role to play in analyzing demand patterns and establishing a pricing structure that assures a sustainable, profitable revenue stream, because operations need to execute profitably. Analytics can help shore up this balance, ensuring that the guest experience is maintained with an eye toward profitability. Demand forecasts for proper staffing levels, calculating segment or customer profitability, and even analyzing guest feedback to identify training requirements or process improvements, can all contribute to the profitable foundation to support personalization.

In my new book, “Hotel Pricing in a Social World,” I explain how changes in consumer behavior have put new pressures on pricing, but are also providing new opportunities for guest engagement. The book describes personalization in greater detail, including the analytics that support this kind of guest engagement. I also discuss how hotels can foster the revenue-oriented culture that can support strategic, cross-functional initiatives like personalization. You can order a copy here: http://www.sas.com/store/prodBK_68509_en.html.

Post a Comment

A Revenue Manager’s Guide to Surviving the Awkward Teenage Years

Revenue management is a relatively young discipline in the hotel industry. Hotels only started to broadly adopt revenue management processes and systems in the late 90s and early 2000s. While the discipline has achieved success and gained visibility over the last decade and a half, it is still relatively new to the hotel industry. In fact, it could be said that revenue management is still in its teenage years.  In a lot of ways, we are acting like it:

  • Hanging with the wrong crowd: Over the past few years we’ve rebelled a bit and started hanging out with the wrong crowd, particularly during the 2008 economic downturn. We snuck out after curfew with a bunch of rogue technology providers who promised a good time reaching new guests through alternative discount channels. “Go ahead, sell 1,000 rooms at 50% off with no restrictions. Everyone’s doing it.” or “Don’t worry, your high paying guests will never find that out you are discounting rooms 30% day of arrival.”
  • Experimenting: We have been experimenting with dangerous things, data things. Some of which our parents have been warning us about, like regrets, denials, and weather. Other new designer data sources are just emerging, like reputation, customer lifetime value, and forward-looking demand. Some may help us price better, some may be fun to play with for a while, but some could eventually kill our forecasts and pricing recommendations.
  • Speaking our own language: Like teenagers, revenue management has invented its own language. As time has gone on, our core metrics have somehow morphed, until we have created amalgamations like GoNetRevProf-POR-PAR-PASH-PAC, which may make perfect sense to revenue managers, but is probably confusing those outside of the RM world.
  • Bullies: Is revenue management being bullied? Maybe. We certainly can feel pushed around a bit by general managers and sales teams, not to mention by the OTAs and other third-party distributors all wanting a piece of the revenue pie.
  • Let's talk about...: Total hotel revenue management, a "hot topic" in hotel revenue management for a decade or more, is just like the proverbial teenage "hot topic", because:
    • everyone talks about it
    • no one really knows what it is
    • but everyone thinks everyone else is doing it
    • so everyone claims they are doing it too
  • What do you want to be when you grow up?: Everyone is talking to us about our future, but we still don’t really know what we want to be when we grow up. As a new discipline, the career path is still forming. Revenue managers have been advocating for a chief revenue officer position, wondering when they could get into a CEO or CMO job, and even migrating back and forth into sales and digital marketing roles as the disciplines converge.

If revenue management is to survive the teenage years to become mature contributing members of the hospitality enterprise, we need to take the same advice as a discipline, that we were all given by our parents when we were teenagers.

  1. Listen to your parents and teachers – sure, it’s a relatively new discipline and things are changing fast, but this discipline is based on a solid foundation of research and best practices. We need to keep these core tenets in mind as we manage through all the change
  2. Get good grades – if we want to get into that C-Suite, we need to perform now, and communicate that performance widely with general managers, asset managers, owners, shareholders, Wall Street – everyone in the community - to get the attention now that will guarantee advancement later.
  3. Do your homework – so much is changing in the economy, in technology, and in analytics. Each revenue manager needs to clearly understand the implications of these changes on their organization and their jobs, and be able to guide their organizations towards the right decisions. Don’t let a vendor or analyst make the decision for you. Figure it out for yourself.
  4. Play well with others - now is the time for revenue management to work cross-functionally within the organization, instilling a revenue culture throughout marketing, operations, and even finance. We can’t wait for organizational structures or incentive plans to line up. If hotels are ever to reach the vision of satisfying total hotel revenue management, every department needs to be working together with profitable revenue generation in mind.
  5. Plan ahead – understand where you want to be and where you want your organization to be five or ten years down the road and start investing in the resources, skills, and infrastructure that will take you there, but...
  6. Be a kid while you can – being a kid is all about playing and experimenting (safely!). As a young discipline, revenue management is still discovering its true potential. There are still many new revenue opportunities out there. Don’t be afraid to innovate, take a few risks, and play around a bit while you still can.
  7. Finally, just because all the other kids are doing it, doesn’t mean you should too! - remember this classic parental saying? It’s very true for revenue management. Pricing is so closely tied to a hotel’s market conditions, business strategy, competitive set, and organizational goals that one size will never fit all. If you blindly follow your competitors, you’ll follow them right off a cliff!

How do I know that this taking this advice will work? Well, I’ve been thinking about this a lot over the last few years as I have been working with revenue leaders from hospitality organizations around the world. If you want to learn more about how to survive and thrive through the “teenage years,” you can find the results of my conversations, research, and thoughts in my new book “Hotel Pricing in a Social World.

Post a Comment

Beyond the Basics: What’s next in predictive analytics for hotels?

with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS

We’ve taught analytics 101 through the last couple of blog posts, and now that you have passed that course, you are ready to take an advanced course in analytics. Ok, not really, we won’t subject you to that, but it is the appropriate time for us to introduce some more forward looking concepts – particularly because as the volume of conversation around analytics increases, so does the volume of conversation around these very complex topics. As always, our goal is to help you understand these opportunities at enough of a level to participate in a conversation about how they support your strategic initiatives.

The purpose of this blog is to cover three important “emerging” areas for the application of advanced analytics. These three areas, machine learning, digital intelligence and text analytics, have become topics of conversation mostly because big data analytics technology has evolved to the point that we are finally able to capture and store complex, non-traditional data formats and apply advanced analytic techniques to them at scale.

Machine Learning

Machine learning started as a branch of artificial intelligence, but separated from that field in the 1990s, moving towards a discipline more focused on statistics and probability theory.   Today, machine learning refers to any algorithm that is developed on a set of test data and then deployed on new data to perform the same task. The algorithm “learns” the new data and is able to automatically perform the same analysis on it, adjusting for new patterns as they evolve. This “learning” takes place with minimal intervention from analysts, meaning that you should be able to segment a large chunk of new guests without the analyst having to write a new model or reconfigure an old one. Machine learning is a term you have likely heard associated with a wide variety of problems, and probably pitched by some of the analytics vendors you have come in contact with.

Regression, clustering, decision trees, factor analysis, logistic regression and neural networks are all classifications of models that can be considered machine learning algorithms.   The idea is that as the algorithms analyze new data observations over time, they become more accurate with future predictions.

Machine learning is highly related to data mining, and even uses many of the same algorithms. However, data mining is typically focused on exploration and discovery of previously unknown properties or relationships in the data. Machine learning, on the other hand, uses known properties or relationships in the data to predict properties or relationships for new data added to the data set. This distinction between exploration and prediction is crucial to understanding how and where to use machine learning algorithms. It could be said that data mining is used to develop the machine learning algorithms. Machine learning algorithms are deployed in production software environments, like responsive websites or marketing algorithms.

Machine learning is used broadly across many industries. Some common applications include search engines, recommender systems (like Amazon or Netflix), advertising, detecting credit card fraud and natural language processing. In hospitality, machine learning will most commonly be used in segmentation analysis and website analytics.

Text Analytics

Text analytics have become more widespread since the advent of the social web. Text analytics can be used to mine the content of any unstructured text document, either created on external sites like Facebook, Twitter or TripAdvisor, or created internally like call logs or open ended questions on a guest survey. There are several methods available to quantify the contents of these unstructured text documents. These methods are based on natural language processing, a type of algorithm that understands language in context and can interpret or infer meaning from it. Natural language processing is most effective when it is applied natively, as opposed to on translated text, which is why it is important for a global industry like hospitality to work with software that has the largest available portfolio of languages in their text analytics.

  • Content categorization. This identifies key topics and phrases in electronic text and sorts them into categories. It eliminates the manual work of reading and tagging documents, giving you much faster results. Text documents can be organized and tagged for search, making it easier to find, sort or process the content. This approach makes it easier to assign certain issues to specific departments that can resolve the issue. It also makes it easier for internal teams to find specific content stored in the text repositories.
  • Text mining, similar to data mining. This uncovers related concepts in large volumes of conversations. It surfaces key topics that can be used in future analyses, like predicting or understanding guest behavior.
  • Sentiment analysis. This helps you understand guest opinions by applying natural language processing to the text documents. It identifies how guests feel about key attributes of your product, brand or service – often in great detail.

Text data is by nature big data, so it needs to be stored differently than traditional quantitative information and will require a large amount of processing power to analyze. Once the data is quantified as we describe here, the results can be incorporated with traditional data sources into a wide variety of analyses, including revenue management algorithms or predictive analytics for retention, response likelihood or lifetime value calculations.

Given the prominence of reviews and ratings in hospitality, and the wide variety of applications, Natalie will cover this area in more detail in our next blog.

Digital intelligence

Digital intelligence is the process of deriving insights from digital interactions, so analysts must work with new types of data like click stream. Digital data is collected at every stage of the guest journey in a variety of systems and formats. The biggest challenge is to stitch together these fragmented sources of data, which come from online, offline, and even third-party sources. All of this data needs to be pulled together in a format useful for both analytics and reporting. There are challenges associated with integrating this data, cleansing it and ensuring it is analytics-ready, particularly because much of the data is in non-traditional formats.

Data-driven marketers use advanced analytics to perform sophisticated analyses, like regression, decision trees, or clustering, but they have traditionally been limited to using offline data (data collected through on-property interactions, or through reservation systems). This has been primarily due to restrictions on access rights to online data from third-party technology vendors. Even if hotels get access to online data, commonly available web and digital analytics tools mainly aggregate and report on historical information and thus are not well suited to perform predictive analysis. In this aggregated environment, obtaining an omni-channel, integrated view of a single guest across the fragmented digital universe has been extremely difficult. As a result, it has been practically impossible to get a data-centric, comprehensive view of the guest that could feed integrated marketing analytics, or more specifically, provide prescriptive recommendations for marketers. Enter digital intelligence.

Digital intelligence is defined as: “The capture, management and analysis of customer data to deliver a holistic view of the digital customer experience that drives the measurement, optimization and execution of digital customer interactions.”1 This requires that marketers focus on understanding the “who,” “what,” “where,” “when,” and the “why” of digital experiences, collecting detailed one to one data across channels, as opposed to aggregated snapshots channel by channel. As with any data project, it’s also important to consider the downstream activities and use cases you wish to support.

For example:

  • Predictive analysis to identify what unique behaviors or attributes in a visitor’s digital journey are closely correlated with revenue generating events (like a conversion or up-sell). These behaviors and attributes can then be identified and fostered for future visitors.
  • Analytically forecasting website visitation by traffic source, and identifying which ad-centric channels have the largest effect in increasing overall traffic (attribution modeling). The ad strategy can be adjusted based on which channels are most productive.
  • Predicting online and offline behavioral drivers of digital conversions using analytically driven segmentation techniques, and improving outbound and inbound targeting rules for future marketing communication and personalization efforts.

To support the opportunities outlined here, web and mobile data, if collected and prepared appropriately, can be merged with your company’s or company-owned customer data, and then streamed into your analytics, visualization, and interaction automation systems.

Recent innovations in technology are making it possible for hospitality companies to move beyond the limitations of traditional web analytics (i.e., aggregated data and historical performance analysis as opposed to predictive modeling). Marketing departments can now integrate digital data with offline guest profiles, and use that complete picture of the guest in predictive modeling to support personalized content delivery, offers and recommendations digitally as well as when the guest is on property.

There are new and exciting use cases for predictive analytics on the horizon for hotel organizations. As the industry as a whole grapples with big data, you can better come to terms with the data from the explosion of interaction points with our guests. Using rich data and predictive analytics techniques you can drive towards improving the guest experience, optimizing operations to improve costs, maximize revenues and profits as well as increase the overall value of the relationships that you have with your guests.

Post a Comment

Back to the Basics Part Two: What can hoteliers do with analytics?

with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS

This week, we continue our fall “back to the basics” refresher series on analytics for hoteliers. Last week, in part one, Natalie and I reviewed the analytic methods that can be utilized by hoteliers. This week we will explore how key functions within the hotel can leverage predictive analytics. Let’s get started with Pricing and Revenue Management.

Pricing and Revenue Management

Revenue management is generally the most data and analytics intensive department within the typical hotel today. To accurately price rooms, revenue management systems forecast demand, and then optimizes the price and availability of rooms to maximize the revenue from the limited capacity of hotel rooms. Within this process, statistical analysis is used to model no-shows and cancellations, “unconstrain” demand and calculate price sensitivity. At this point, most hotels use a revenue management system that has been specifically designed to execute the complex analytics required to deliver an optimal price. The analytics processes are configured to the hotels’ specific operating conditions and connected to selling systems to deliver price and availability controls.

Still, there are many analyses that revenue management might conduct outside of the revenue management system. For example, they might want to use descriptive analytics to analyze demand for a restaurant or spa as part of a total hotel revenue management program, or data mining to understand how consumers value different attributes of the room to better configure rate spectrums.

Marketing and Customer Loyalty

Marketing and customer loyalty are fast following in the footsteps of revenue management when it comes to utilizing predictive analytics. To develop a better relationship with the guest, segmentation and profiling models are used to group guests in segments that have similar characteristics, whether they are business defined segments or demographic or behavioral defined segments. Return trip models are used to calculate the probability or a guest returning to the property in a specific period of time. Lastly, one of the most widely used predictive techniques for marketing and loyalty is that which calculates the customer lifetime value, determining how much a guest is worth during the expected lifetime of his/her relationship with your company. Understanding a guests predicted lifetime value can help determine the treatment of those guests, including any incentives in the form of promotions and discounts.

Marketers are using automated solutions for the campaign process, from designing the campaign, predicting response rates, executing the campaign and then tracking performance of the campaign. When analytic results are incorporated into this automated process, targeting improves and campaigns generate more lift.

The emerging area of opportunity for marketing is in digital intelligence. Marketers are using performance statistics from online channels to understand consumer behavior and better design the click through to conversion process. Hotels are beginning to use profile information combined with search context to identify what a guest may be looking for and surface relevant content, as well as follow up if they don’t convert.   A hot topic for marketers today is attribution modeling, which is a statistical technique to identify which channels or partners contributed to an eventual conversion. With rising costs of distribution, it is more important than ever to have a clear picture of who is contributing to actual sales and how much they are contributing.


Forecasting is particularly valuable to operations. Accurate demand forecasts can support labor scheduling and supply ordering. Revenue forecasts assist budgeting and planning. Statistical modeling can be used to understand the drivers of guest satisfaction, or the effectiveness of training programs. Text analytics interpret the content and sentiment of reviews and open ended guest survey questions to identify service improvement opportunities or design new offerings. Optimization can produce a labor schedule that minimizes labor costs while maintaining service levels. There are some automated systems, such as for labor management, but many hotel companies also utilize tools for ad hoc analysis. Compelling visualizations that communicate descriptive statistics plus analytical results are particularly useful for operations, since they need to stay focused on the next best action to take to properly serve guests, rather than spending a lot of time analyzing complex spreadsheets.

You can see from the departmental descriptions above that there could be some synergies between the data and analytics that are used by each group. Revenue management could benefit from knowing expected response rates to campaigns, and marketing could design better campaigns if revenue management let them know when to expect need periods. Demand forecasts could be leveraged across the organization, and certainly budgeting would run much more smoothly if portions of that process could be automated and supported by analytics rather than gut feel. The most advanced hotel companies today are moving towards a more integrated decision making, where data and results from each department are leveraged across the organization.

Many hotels are broadly implementing visualization tools for descriptive analytics. Revenue management departments are heavy users of advanced analytics in their systems, and some marketers are applying basic segmentation analysis or customer value calculations. Those hotel companies that are striving for competitive advantage will go beyond descriptive tools to apply advanced, predictive analytics, moving the entire organization from reactive to proactive decision making.

Post a Comment

Back to the Basics Part One: An analytics primer for hoteliers

with Natalie Osborn, Senior Industry Consultant, Hospitality and Gaming Practice, SAS.

It’s back to school time, and back to school reminds me of getting back to the basics. So, we thought we’d start the fall with a “back to the basics” refresher series on analytics. To accomplish this, Natalie and I have teamed up for the first time! We at the Analytic Hospitality Executive have been encouraged by seeing hotels increasingly embracing advanced analytics over the past few years, but the downside of this trend is that the resulting noise in the market as more players enter the fray, and more hoteliers talk about their initiatives has muddied the waters a bit in an area that is already quite complex. In this, part one of the series, Natalie and I will give insights into the types of analytics that could be used by a hospitality organization.

As competitive pressures increase, hospitality executives struggle to achieve balance between maintaining a memorable guest experience and meeting revenue and profit obligations. Err too much on the side of guest experience by, for example, giving away too many amenities, and profits suffer. Focus too much on costs, like cutting front desk agents, creating long waits at check in, and the guest experience is damaged. Analytics can help strike a balance between these two critical goals.

Before we talk about analytics, it’s important to acknowledge that good analytics start with good data. Over the summer we wrote about opportunities in data acquisition and data management. It is crucial that the team has a solid understanding of what data you have, and what that data represents. Starting with a single source of the truth, when it comes to the key metrics that the organization relies on, will save time and increase accuracy. As we pointed out a few weeks ago, you don’t have to wait for big data, start with what you have and work from there.

Analytics Explained

Analytics are broadly categorized into two main groups, descriptive and predictive. Descriptive analytics are what’s known as business intelligence. These analytics describe what is happening in the data through metrics like percentages or averages, typically displayed in static reports or dashboards. Descriptive analytics answer questions like “how many, where?” or “what happened?” Since most hospitality organizations struggle to even access data, the capability to access descriptive analytics, slice and dice and drill down can seem like a revelation (and to a certain extent it is), but it still is only providing a snapshot of what happened in the past.

Predictive analytics help to anticipate trends and foresee opportunities. Predictive analytics use historical data to predict the future, answering questions like, “what if these trends continue”, “why is this happening” and “what’s the best that can happen”. These analytics allow managers to proactively plan for the future, hedging against risk and taking advantage of opportunities. Managers react to descriptive analytics. Predictive analytics move the organization from reactive decision making to proactive decision making.

There are several categories of predictive analytics. We will first define these categories and then describe how different functions in the hotel can apply them:

  • Statistical modeling:  Statistical modeling helps you understand “why” trends in your data are happening by identifying which factors have a relationship with each other and how much they influence key measures. Correlation, hypotheses testing and regression are common statistical techniques that you may be familiar with. Statistical modeling is all about identifying and understanding relationships.
  • Predictive modeling: With predictive modeling, you use historical data, current conditions and key demographic variables to predict behavior or outcomes. This is typically used when you have some idea of the relationships already or have a well-defined goal or usage for the results. Regression is also in the predictive modeling category, as well as segmentation modeling and customer lifetime value calculations. These predictive models tend to be the ones that are promoted into a production environment, meaning that they are used for routine decision-making (predicting campaign response rates or assigning new guests to a segment) as opposed to exploratory analysis.
  • Data mining: This set of techniques is becoming more common as data sets grow. Data mining is useful to detect patterns in large datasets and either describe those patterns or use the patterns to predict outcomes. Data mining is used when you do not know what you are looking for or what outcome you are expecting. As I alluded to, data mining can be either descriptive or predictive. Common data mining techniques include decision tree and clustering algorithms. Frequently data mining is used to uncover patterns or trends that are later use to build a formal predictive model to be used in a production environment.
  • Forecasting: These models use historical patterns and current conditions to predict a future state or trend (revenue, demand, guest counts). There are many different kinds of forecasting models designed to suit different kinds of problems. For example, a different method might be used if there are trends or seasonality in your data versus for data that is relatively stable.
  • Optimization: An optimization problem is a mathematical problem that calculates the best possible answer or outcome considering operating conditions and business constraints.  This is a frequently misused term, so it is important to understand what exactly this refers to. An optimization problem has a defined goal (maximize revenue, minimize labor costs), which is subject to some constraints (for example, the capacity of the hotel, expected demand, a budget), and the output is a set of decisions that will best solve the goal while respecting the constraints (the price to charge by room type or the number of employees to schedule per shift).

These predictive analytic techniques can be delivered in an analytic tool which allows the analyst complete control, and are especially good for ad-hoc (non-routine) analyses, or they can be built into a solution with pre-defined inputs, methods and outputs. For example, revenue forecasts for budgeting might be done in a forecasting tool, but demand forecasts for revenue management are part of a revenue management system. Most organizations will utilize both delivery methods depending on the problem and their internal capabilities.

In part one we have reviewed the analytic methods that can be utilized by hoteliers. Join us next week for part two, where we will explore how these analytics can be used within the hospitality environment. In the interim, you can learn more about analytic solutions for hotels here.

Post a Comment

Just get me my data! (Please): How advances in data management are making it easier for business users to access data

We’ve been talking about data recently at the Analytic Hospitality Executive. I’ve advocated to use whatever data you have, big or small, to get started today on analytic initiatives that will help you avoid big data paralysis. In this blog, I’m going to get a bit more technical than usual because I have recently been learning about some innovations in data management that I believe will dramatically change the game for analytic hospitality executives. I think it’s important that business users have a high level understanding of these issues so you can help your IT departments to put the right data management infrastructure in place.

Regardless of the size of the data, one of the biggest challenges in hospitality has always been that disparate systems collect and manage all of the wide variety of data that we need to gain insights about our business. These systems speak different languages and collect data in different ways and in different formats. In order to effectively analyze data from disparate systems, the data needs to be integrated (meaning combined to form one, unified, view). This involves extracting data from source systems, transforming that data (transposing columns, standardizing values), and loading it into a data storage area. This process is known as ETL (extraction, transformation, loading). It involves detailed knowledge of where all the data is, an extensive amount of coding, and needs to be changed every time an upgrade to a system is made or a system is added or replaced.

Many companies invest in a data warehouse to integrate and store data from disparate operational systems. The benefits of data warehouses are:

  • All of your data in one place – the data warehouse integrates data from the disparate systems into one location according to a pre-defined schema.
  • Speed - data warehouses are very good at quickly extracting, transforming and loading (ETL) data from the transactional system and can quickly render reports on historical data
  • Reduces the processing burden on operating systems – instead of hitting the transaction system directly when you need data, you make the request from the EDW. The data is pulled from the transaction system at some scheduled interval, so it can focus its energy on executing transactions instead of delivering data.

However, data warehouses also have their drawbacks.

  • Relatively inflexible:
    • They have a fixed data schema, so any new data or changes to data collection in source systems needs to be recoded.
    • They are optimized for reporting but not necessarily for analytics. Analytics typically require wide tables (a lot of different information about one entity for predictive purposes). Reporting requires long tables (many instances of total sales by period). Analytical resources need to write code to restructure data in formats that are appropriate for analytics and probably store the results somewhere as well.
  • Batch processing: the ETL processes for a data warehouse typically operate in batch (all data transferred at once with less frequency, say once a day or once an hour). This means that data in the data warehouse is only updated periodically.
  • Processing intensive: The ETL processes can also be very processing intensive. Large amounts of data are moved around, and transformations can be extensive depending on how diverse data formats are and how “dirty” the data is.

This inflexibility means that adding data or creating new views, tables or analyses requires a lot of coding, which breaks every time something new is added to the system (and we never add new technology or new data to the hospitality infrastructure, right?). This is time and resource intensive. Processing takes time, slowing down access, increasing time to results and consuming computing resources that could be used for analytics or reporting.

Enter data federation. Data federation is a data management mechanism that treats autonomous data stores as one large data store. The rules for how the data relate to each other are kept in the federation layer, and data integration is done “on the fly”. This means that data is stored in its original format in the individual systems and then only integrated when the user wants to access it.   It can also mean that the data is available in “real-time” – whatever the source system is holding currently is available, rather than waiting for the batch to run.

The benefit of data federation is that with reduced movement of data there are fewer chances for data errors. There is a significant reduction in the workload associated with moving data around, especially if some of it is not ever going to be used. This frees up computing resources. Data federation also increases the speed of access to the data for the users, as data is available closer to “real time”.

Typically, data virtualization goes hand in hand with data federation, so you might have heard this term as well. Data virtualization is defined as any approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data like how it is formatted or where it is physically located. Virtualization facilitates data access, because the user doesn’t need to know where the data is stored, or what format it is in to access and use it. The virtualization layer takes care of that. It can also provide some data cleansing, data profiling and data modeling capabilities. (Note that you can have federation without virtualization, or virtualization without federation, but they most typically operate together for maximum benefit. You really don’t want me to get into that, although some of it is quite logical).

The biggest benefit of data virtualization is provides much easier data access for business users. The location and characteristics of the data are transparent to the business user who wants to access the data for reporting, exploration or analytics. They don’t have to understand technology architecture or write complex code. The second benefit is a dramatic reduction of coding burden on IT. IT does not have to write special code every time the user has a unique need, and for some technical reasons that are not important to us, the ETL coding burden is lesser as well.

There are a few things to consider with both data federation and data virtualization.

  • Impact on transactional systems: Data federation applications can still burden transactional systems with too many requests for data, so you may still need a data warehouse to store data from certain transactional systems.
  • Data Governance: A unified approach to data management like this will require different, and stricter, data governance rules. IT will need help from the business to understand who uses the information and how, so you need to be prepared to establish strong data governance (which is a good idea anyway)
  • Historical information:   With a data federation method, you can only access the data that is in the source systems at the moment you ask for it. This means that if the source systems aren’t keeping historical data or if they write over history, you need to store that information elsewhere (like in a data warehouse).

We may never get away from the need for EDWs (enterprise data warehouses), but we may be able to get away with smaller versions in an environment that still facilitates access to data by business users. Implementing data management technology like I describe above will require investment and business process change, but it should dramatically streamline the data management process, helping business users get to their data when they need to.

The goal of this blog was to help you get a high level understanding of data management options. My hope is that information like this will help you to have more informed conversations with IT as you are planning your data and analytics strategy. This e-book describes data virtualization in a bit more detail, but also in business language.

Post a Comment
  • About this blog

    We’re hospitality industry specialists at SAS, the world’s leader in business intelligence and analytical software. We’ve partnered with The Center for Hospitality Research at Cornell University to find solutions to hospitality industry challenges. In this blog we will leverage the existing knowledge of the faculty, existing research and the experience of industry peers to answer the questions that hospitality executives face every day. Questions regarding topics such as revenue management and price optimization, social media analysis, sustainability, patron/guest lifetime value optimization, labor planning and marketing automation and optimization.
  • Subscribe to this blog

    Enter your email address:

    Other subscription options

  • Archives