What can hotels learn from casinos? Focus on the service environment.

What can hotels learn from casinos? In my first post on this topic I explored how casinos apply predictive analytics to help them focus on their customers. In this post I will explore how casinos use analytics to help manage the service environment with the dual goals of improving profitability and customer experience. But first, let’s kick off with some of the crazier myths about how casinos manipulate their physical environments to make you, the patron, gamble more.

I’ve heard it claimed that casinos pump hyper-oxygenized air into casinos to keep your mood buoyant and attention focused on gaming. I have also heard claimed that there is a big red button in the basement that can dial up or dial down how much patrons can win! Neither of these rumors are true, but there are a lot of moving parts in a casino.  Casinos do manage their service environment, and they use analytics and data management to help them do it. They focus on things such as the location and placement of the slot machines and gaming tables, the denomination of those machines and minimum bets of the tables, as well making sure that they have the skilled team members to manage these experiences and also the patron themselves.

When it comes to planning the operations of a casino floor, bad decisions can mean significant losses in customer loyalty and potential revenue. The challenge is how to plan the right mix of gaming choices, denominations, and table or machine placement to optimize the patron’s interest. In Canada – 85% of gaming revenue is made up of slot machine revenue. As a result, slot operations are a primary focus of their analytics.

Saskatchewan Gaming Corporation, operator of the Casino Regina, has placed a lot energy and analytics into how they manage their slot operations. They pulled together all of the transaction data on their slot machines and created a best-case predictive forecast into how each game would perform in the year to come. In the process, they began collecting insights into leading predictors of patron preference for machines. This helps them optimize profitability. They also looked at how they can predict the impact of potential changes to slot performance based on ‘what if’ scenarios.

Finally, Saskatchewan Gaming Corporation used advanced optimization to determine the best approach to future business, considering factors such as physical space and budget. This information helps the casino company to optimize its slot machine purchase options, including the analysis of which machines to replace and when to replace them, while also ensuring the patron experience is not hampered through excessive machine down-time. Analytics allows this casino company to offer the right games, in the right locations to attract loyal and valuable patrons.

Optimization techniques are not isolated to machines for casinos but can also be used to ensure that the right service operations staff are available to serve patrons. It’s important that if you offer a patron a meal, that you have a seat available in the restaurant for that patron. Patrons who have to wait for service in a restaurant or a seat at a gaming table may be tempted to take their business elsewhere. Forecasting demand for each service area, and matching the right skill set to the area is very important for casinos. Just think of all of the different skill sets required to run a gaming floor, from the different dealers to the cashiers, pit boss and machine technician. Optimization approaches are needed in every aspect of casino operations.

Casinos also carefully manage the patron’s service experience while they are in the casino. Casinos use master data management techniques to ensure that they can use what they have learned about their patrons to enhance the patron experience. Master data management can deliver the results of analytics to the service operations team members, to ensure that each patron is recognized and treated appropriately and most importantly - consistently.

Managing the patron’s service experience is particularly important when something starts to occur that none of us really want to think about when we are in a casino, and that is losing. If a patron hits a losing streak their enjoyment may start to decrease.  The patron may decide to leave the casino and “try their luck” somewhere else. Casinos know if they offer patrons a free meal or a drink at the bar, allowing the patron to step away from the losing experience and focus on something else, the patron will start to enjoy themselves again. Casinos trigger alerts to their service teams to identify patrons who are losing. These alerts can be informed with analytic such as customer lifetime value and next best offer, which helps the service team take the appropriate action to correct the customers experience while at the same time remaining profitable.

While the loss of a casino patron may be much more “of the moment” than that of a hotel guest, the use of analytics and data management to help detect and avoid that act of attrition is something that all hotels could benefit from. How are you detecting behavior changes in your guests? Do you have steps in place to identify when the customer experience is going wrong, or when the customer is about to leave you?

Casinos use master data management (MDM) techniques to communicate important customer preference information to staff who sit at each interaction point. Master data management is the processes, governance, policies, standards and tools that consistently define and manage the critical data of an organization to provide a single point of reference.  One of the benefits to service organizations of using MDM is that when that single point of reference is a customer profile, the master data can ensure that the treatment of customers is consistent and that preference information reaches all customer points of contact.

How do you ensure the service experience is consistent across your operation and throughout your estate? Are you relying on information hidden in the comments field of your property management system? Are there small “moments of truth”, such as the preferred pillow or using the preferred method of address that mean the guest stays satisfied?

When you proactively manage the service environment, you can deliver on a great guest experience and avoid customer attrition. Casinos use solid data management techniques to help communicate their guest’s preferences and predictive analytics to identify moments when intervention is required.

No hyper-oxygenized air required.

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Reviews, Ratings and Hotels – What research tells us (so far)

As those of you who follow me know, I’ve been working on a series of studies with Breffni Noone (Professor at Penn State) about the impact of user generated content (UGC) and price on consumer buying behavior.   When we speak about this research, we tend to hear the same sets of questions from most audiences – whether they are revenue managers, marketers or even outside the industry.  In this blog post, I’ll review our research to date, and then I’ll answer some of the common questions we hear based on what we’ve seen in our literature reviews for our research.  Next month, I’ll explain our latest study which compares business travelers to leisure travelers.

Price and UGC – Influence on quality and value perceptions

Now that user generated content is available at the point of purchase, consumers have more information than ever before as they evaluate a hotel purchase.   In order to continue to build profitable pricing and positioning strategies, hoteliers must understand how all of this information together influences consumers.   Previous research has shown that consumers’ perceptions of quality and value are the primary drivers of purchase behavior.

In our first study, we designed a scenario based on the online purchase of a four-star hotel for a weekend leisure break.  We presented a hotel where the price varied low to high (relative to a reference price), the reviews were either mostly positive or mostly negative, and the ratings were low (2.8) or high (4.8).  We then asked consumers to report their perceptions of the quality and value of that hotel.  You can read my post about this study here.

The main takeaways from this study were:

  1.  In the presence of ratings and reviews, consumers do not use price as an indication of quality. Hotels can lower price (within reasonable bounds) to generate short term demand, without impacting consumers long term quality perceptions.
  2. Reviews are the most powerful value indicator for consumers: Consumers look to the reviews over aggregate ratings to form quality and value perceptions.  We hypothesize that the uncertainty associated with the hotel experience leads consumers to gather as much information as they can before purchase. Reviews provide this, ratings do not.
  3. Competing on price alone is not a winning strategy. Consumers will look closely at UGC and price. This means you must understand your price position and your reputation position versus the competition.
  4. Good reviews are not a license to charge more: Despite the power of positive reviews, consumers still prefer to pay the lowest price. All things being equal, if your price is lower, you’ll drive demand to your property.
  5. It’s hard to overcome “bad” UGC: Lowering the price of a badly rated, and negatively reviewed, property drives no additional value in the minds of      the consumer. If you happen to be in that unfortunate position, you should keep the price up, and take what you can get – which according to our results won’t be much. Use your energy to fix the problems with your property instead of worrying about how it is priced!!

How consumers choose – Price, UGC and Value

In our first study, participants evaluated one hotel.  We were interested to understand how they would behave if they were forced to make a choice.  We designed a choice modeling experiment that would help us understand the tradeoffs consumers make and the attributes that drive value perceptions.  In this study, we again used a four-star hotel for a leisure break, and told participants that all hotels had equivalent amenities and location.  We then showed them three hotels in which we varied levels of the following attributes: the brand (known or unknown), price (low, mid, high), ratings (low, mid, high), TripAdvisor Rank (low, mid, high), review sentiment (negative, positive), review language (emotional, descriptive) and review content (service, physical property).  We asked them to select the hotel they would book.  They repeated this exercise three times.  By tracking their patterns of choice, we were able to understand how each of the attributes, and the levels of those attributes influenced choice behavior and value perceptions.    You can read more about this study in this blog post.

There were four main takeaways from this study:

  1. Reviews  and price are the most important influencers of choice. While consumers did pay attention to aggregate ratings, TripAdvisor rank and to a lesser extent, brand, positive reviews contributed the most to consumer choice behavior followed by lower price.
  2. Negative reviews remove you from the choice set. Period. Lower price or higher ratings do not overcome the impact of negative reviews. Consumers simply will not choose a hotel with negative reviews.
  3. Consumers prefer to pay a lower price. While consumers would go for a higher-priced hotel when the reviews and ratings were better than the alternatives, all things being equal, they will look for the lowest price. Hotels need to understand their position relative to their competition both on reputation and on price in order to take advantage of any pricing power associated with positive UGC.
  4. Consumers only notice high ratings and rankings. Our results showed that consumers only notice ratings and rankings when they are high as compared to other choices. Consumers do not place any value on the comparison between low and mid-level ratings and rankings.

These two studies were focused on the leisure traveler.  Our next study looks at choices of the business traveler.  I’ll describe that study in next month’s post.

Any discussion of the influence of UGC on consumer purchase patterns tends to raise a similar set of questions.

How do your findings relate to the Cornell study where a one point increase in Reputation Index resulted in an 11.2% increase in pricing power?

Our study results definitely complement the Cornell Center for Hospitality Research Study done by Chris Anderson in 2012.  If you missed it, I covered it in this blog post.  That study focused on performance measures, where we looked at consumer behavior.  We did provide one nuance to that work, and that is that the pricing power associated with improving your reputation index by one point will only happen on the “top end” of ratings – as in, if you raise your score from a 3.5 to a 4.5.  Consumers will not notice any movements on the low end – as in a 2.8 to 3.5.

My advice is to take any of these study results in the context of your market and your business strategy.  You will have increased pricing power with a good reputation (likely if you have a well-run, high quality hotel, you’ll have a good reputation, good performance and you’ll already be at the top of the market).  How you take advantage of that pricing power depends on your business strategy and your market conditions.  Our study demonstrates that consumers prefer to pay the lowest price they can.  With a great reputation, you have the ability to drive share with an aggressive pricing strategy.  You can drive revenue with a good reputation – but only assuming you are clearly better than the competitors in the market.  Both Chris’s study and ours reinforces the point that you need to have a good understanding of where you sit in the market on reputation and price, and a good understanding of your hotel’s business strategy, before making any pricing decisions.  The job of revenue manager is not getting any easier!

Should I respond to reviews?

Many hotels have invested a good deal of resources in responding to reviews – both positive and negative.  I have seen one study so far that looked at “response to negative reviews” in the context of hotel performance.  There seemed to be a relationship such that hotels that responded to negative reviews had better performance (ADR, RevPAR, Occupancy).  There is plenty of anecdotal evidence as well.  My own opinion – this comes down to service recovery.  You would address a problem if it were brought to you in person or via letter, so responding to negative reviews is a good idea.  I suspect that ‘over-responding’, or not being genuine, however, can do as much damage as that negative review, so I’d say be careful.  More research needs to be done in this area.

Should I be worried about my TripAdvisor Rank?

I have heard plenty of stories from industry about improvements in TripAdvisor Rank correlating with more bookings.  In our study, TripAdvisor rank was influential only when it was high, and the attribute was not a hugely influential overall.  However, there are two aspects of TripAdvisor Rank – the absolute number itself and the positioning that the rank gives you in search results.  In our study, we just presented the number (x out of 217 in the market).   Consumers may not be focused on the number itself, but being higher on TripAdvisor gets you noticed more because you will show up sooner in the search results.  There has been plenty of research on how many pages consumers will search through before making a decision, and it isn’t many.  Despite what our results say, I’d still recommend working to get the best rank on TripAdvisor that you can so that you show up as early as possible in the search.

Are the number of reviews important?  Should I be working to get more reviews?

I have seen a number of studies that show that consumer confidence increases with the number of reviews.  Consumers “believe” the UGC more when there is more of it.  I would definitely recommend that hotels work to increase the number of reviews posted across all types of review sites.

There is one exception to this.  I saw one study where increased number of reviews actually had a negative relationship with RevPAR at the luxury level.  These researchers hypothesized that as the number of reviews increased for luxury properties, the perceptions of exclusivity decreased, which left some consumers thinking “I don’t want to pay this much to stay where everyone is staying”.

Does the “quality” of the review influence decision making?

I have seen some research indicating that consumers “trust” a review more if the review quality (length, grammar, etc.) is good.  While there isn’t much that a hotel can do about this, you can feel secure that a badly written, negative review will not be valued as highly as a well-written positive review.

There is still much more work to be done– but what is clear from all of the research described here, is that reputation definitely drives consumer decision making.  To stay competitive, hotels need to monitor their reputation, and to use the information within the reviews themselves to continuously improve their offering.   Stay tuned for the  results of our new research next month!

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From the desk of the CHRS: What is the New Science of Service Innovation?

By Cathy Enz, PhD., Lewis G. Schaeneman, Jr. Professor of Innovation & Dynamic Management, Cornell School of Hotel Administration.

With the theme “The Future of Service Innovation: The New Science of People, Organizations, Data, and Technology”, the Cornell Hospitality Research Summit 2014 this October is focused on how to create new knowledge.  When we think of science, we think of knowledge gained through a systematic process that includes collecting and testing information, often with the desire to find solutions to particular problems.

As a forum for direct, research-based dialog between industry and academe, the CHRS 2014 will be all about what is new in our understanding of service innovation, broadly defined.  So the “new science” of service innovation is a desire to apply new knowledge that is based on systematic analysis or data collection to address problems we face in hospitality organizations.  The domains of people, organization, data and technology were selected for emphasis because producing solutions to complex problems in these domains are where much of the action is within innovation.  New ideas, business models, processes, and practices will be key to our discussions at the summit.

Distinguishing service innovation from other types and forms of innovation is also important to hospitality.  While many discuss invention and technology interchangeably with the word innovation, we believe that the hospitality industry needs to pay close attention to both product and process innovations.  In a roundtable held on campus several years ago, we defined service innovation as the introduction of new or novel ideas that focus on services.  New ways to deliver value, new service concepts or new service business models are just some elements of this type of innovation.

In our own exploration of service innovation we find agreement from many practitioners that along with shifts in technology, continuous operational improvements, investments in employee performance, and management of the customer experience are ways to facilitate the delivery of service innovation.  In the upcoming summit our different presentation tracks will focus on these elements of service innovation delivery.

As co-chairs of the summit, Rohit Verma and I feel that innovation must also happen in how we share knowledge.   We hope to create knowledge on the spot by a focus on exploring big questions, stimulating debate, and providing ways to assure active audience participation.  The New Science of Service Innovation is based on using information in a systematic fashion, and we think the CHRS 14 is just the beginning of many new approaches possible for building a community of practice around innovation in the hospitality industry.

As the leading source for research on and for the hospitality and related service industries, the Cornell University School of Hotel Administration invites representatives of industry and academe to the 3rd Cornell Hospitality Research Summit, on the beautiful campus of Cornell University in Ithaca, New York, October 12-14, 2014.
 A conference unlike any other, the CHRS is designed to create new knowledge through the intentional interaction of industry and academic presenters and participants.

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What can Hotels learn from Casinos? Focus on knowing your guests.

Did you know that total consumer spending on gambling at commercial casinos in the US was calculated at $37.34 billion in 2012? $8.6 billion of this revenue was returned to states and local communities in the form of taxes and other levies. The US casino entertainment industry also provides jobs to 332,000 people who earned $13.2 billion in wages, benefits and tips during 2012. It’s hard to deny that gaming is serious business. And like all serious businesses, the casino entertainment industry is leveraging predictive analytics to ensure that they stay on a winning streak.

Over the next three posts, I am going to explore the three main areas where analytics make the difference for casino companies. Knowing their patrons is one of the areas where casinos apply predictive analytics to help them to stay on the leading edge. The second is the process of running the casino, this areas covers the machines, equipment, physical surroundings and even the service experience of the patron themselves. Lastly, we will look at pricing in the casino environment – how casinos forecast and manage supply and demand for their products.

But first let’s address one of the biggest myths about the casino business right up front. The casino business is not about “winning” or “losing” money. Casinos are in the business of providing entertainment. When a customer comes to a casino, they are not buying a chance to win (or lose) big, they are buying entertainment. The fun of playing at a table or at a slot machine, the pleasure of dining in a restaurant, seeing a show, playing golf or enjoying the hotel, spa and pool amenities are all of the reasons that patrons visit casinos.

Any business that is focused on providing entertainment must center on its customers, and it would be difficult to find an industry that is more focused on its customers, or patrons. But the casino industry has a challenge. Patrons can be very fickle. There are a variety of entertainment options available and only limited entertainment dollars in every patron’s wallet. What’s more, the casino entertainment experience has been built on recognition. Valuable patrons are rewarded with offers such as free meals, free or discounted play and free rooms in exchange for their patronage. Over time these offers and rewards have become expected. If a patron of a casino doesn’t feel that they are getting the offers and recognition they deserve, they can easily chose to find their entertainment elsewhere. One of the most important things for a casino is to know their patrons’ preferences well so they can provide the entertainment experience to keep them coming back, while at the same time understand the patron’s value so that offers and rewards remain profitable.

So how do casinos know their patrons so well? They know them from the data they collect. And the data we are talking about is data on the customer experience. Casinos do have some challenges when it comes to data. Think for a moment about all the experiences you can have in a casino. Gaming – whether it is table games, slot machines, even betting on horse racing. Eating and drinking in restaurants, bars, cafes, even your hotel room. You can see a show, play golf or relax in the spa or by the pool. That’s a lot of points of experience which results in a lot of transactions that are each managed by individual systems.

Pulling all that data together is a challenge. First casinos need to bring together all of the customer transaction data from all of the disparate systems that manage it. Then they need make sure that the data is of good quality and that there are no duplicates or bad data. Once the data is clean and ready, casinos build a 360 degree view of their customers and start to apply predictive analytics.  This gives the casinos a unique understanding of their patron, including current and predicted customer lifetime value, the theoretical win they expect to gain from the patron whether it is daily or by trip for out of town patrons. They also build segmentation and response models to help them understand how to market to the patron and how they are likely to respond to offers.

Companies with a game-changing analytics set their business strategies based on what the analytics tell them.  In their book, “Competing on Analytics - The new science of winning,” authors Thomas H. Davenport and Jeanne G. Harris highlight Caesars Entertainment as a great example of this. By gathering and analyzing patron data through their Total Rewards program, Caesars found out something very important. They realized that their core, profitable patron was not who they thought it was. It was not the high-roller who dropped thousands of dollars per trip, but it was the high-frequency, lower spend per visit slot player.  They designed their entire brand position, rewards program, service offering and business strategy around this player, and went on to become a market leader in the US casino industry.

Foxwoods Casinos is another great example. Foxwoods segmented their customer database to such an extent that they were able to understand their guest preferences very well and get extremely granular with their campaigns. In some cases, for example, they can send 400 variations of a single mailer out to their customer base. Let’s think about that for a moment. Can you imagine sending FOUR HUNDRED different versions of the same mailing to your customer base?  When you know your customers well you can give them exactly what they want, and keep them coming back.

The same challenges with disparate systems that exist in casinos exist for hotels. The hotel front office system may capture all of the transactions if a guest charges to their room, but what about those guests who do not room charge, but use a credit card when dining, or those guests who do not stay in the hotel but still use the hotel restaurants, spa, and meeting rooms? Are they identifiable as a unique customer, or are they a nameless transaction in a point of sale system? Building a comprehensive view of all of your guests can be a challenge if you have disparate systems. A data management strategy can assist you with bringing together data from different systems and consolidating it into unique customer records.

Once you have a 360 degree view of your guests, the options for analytics are vast. Perhaps you want to look across all of your customer records and understand what relationships exist between those that use the restaurants at your properties and those that use other facilities, for example, your spa. This can certainly help with understanding how changes to the restaurant operations such as opening hours or even a renovation will impact the revenue of the spa. When it comes to marketing, a better understanding of guest preferences and spending patterns can help drive segmentation, which in turn drives better tuned offers.

While hotel companies are unlike casinos in many ways, one of the fundamental similarities between the two is the need to focus on knowing the guest. Casinos have implemented loyalty card programs that capture data and reward their patrons no matter which area of the business they are patronizing. And data collection doesn’t stop at only those patrons who show their loyalty card. Through data matching it is possible to identify those customers that do not use loyalty cards, but can be identified using their name, address or other identifier. How much do you know about guests who do not use your rooms? Do you know enough to tailor a promotion for them that will encourage spending in other outlets, or even other properties?

When you know your guests well you can give them exactly what they want, and keep them coming back. Casinos use solid data management and predictive analytics to help understand their guest’s preferences, and predict what guests want. In my next post I will explore some of the ways that casinos optimize their physical environment and operations – stay tuned!

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Moving beyond campaign management

Most organizations have adopted a campaign management solution to help them plan, execute, automate and measure their outbound marketing campaigns. A common goal in the usage of a campaign management solution is to support a profitable data-driven marketing campaign strategy across outbound channels to grow revenue, reduce marketing costs with less reliance on IT. However, a growing need is how marketing can leverage the investment in campaign management to optimally manage the customer experience. Meaning, marketing connecting with the customer at the “right time” to ensure an engaging and optimal experience, which increases loyalty, as well as revenue generating opportunities.

For the marketer, this can mean leveraging campaign management, as well as     technology, to encourage customer loyalty, grow profitable relationships, but also providing a meaningful and impactful customer experiences across all channels.

The challenge for the marketer is how to best implement a marketing technology strategy that leverages current investments (i.e. campaign management), as well as how to evolve to keep up with customer demands.  Depending on the organization, a short-term strategy could be to focus on analytics and data management to compliment campaign management strategies, or it might be time to develop a strategy that incorporates omni-channel marketing, marketing optimization and digital intelligence.

Data and analytics

Before considering how to incorporate interactive channels, digital intelligence or marketing optimization into a marketing strategy, it would be wise to start with a commitment to data management, as well as an assessment of how analytics supports marketing goals.

Meaningful marketing and analytics are not possible without good data. The foundation to any marketing analytics strategy is to consolidate, clean, store and provide access to clean data, which can yield benefits, such as a panoramic view of the customer. Data to consider should include, but not be limited to:

  • Transactional data, as one might expect, is information captured during transactions. It includes financial information, as well as when and where the interaction occurred.
  • Behavioral data encompasses actions that occur once, such as abandoning a cart, as well as activities observed over time that help you establish patterns of behavior. This includes email response data, social and mobile data, online behavioral data and campaign response history.
  • Computed data is created by performing calculations on one or more variables. The resulting factor can be as simple as distance from a retail store or as complex as expected lifetime profit value.
  • Integrated online/offline, is the ability to incorporate online, or digital data with offline to provide a panoramic view of a customer. The benefit is to be able to get a full understanding of who a customer is, where they engage, their potential value, etc.

Additionally, beyond being able to access clean data, is the need to commit to agreed-upon data definitions, data dictionary and key metrics, so decisions can be made consistently across an organization. Once data is clean, organized and governed, an organization can then effectively incorporate analytics into a campaign management strategy

Incorporating analytics into a campaign management strategy can help the marketer not only understand customer behavior, but anticipate behavior, which can enhance the customer experience.  The benefit of doing so can result in increased response rates, higher marketing ROI and reduced churn, as well as marketing efficiency gains and cost reductions.

Marketing analytic strategies should include segmentation that allows you to identify how customer segments are most likely to respond to marketing tactics and how much to invest in these segments. Effective customer segmentation will allow marketers to better understand target populations and deliver the right message at the right time. Also, predictive modeling should be leveraged to identify which customers are most likely to respond to a particular message or offer, which can increase ROI, marketing effectiveness customer engagement.

The key is to be able to seamlessly incorporate analytics into marketing campaigns, which can be cumbersome and time consuming. Marketing solutions that include “dynamic scoring” or “inline scoring” capabilities will greatly reduce the time and resources required to take action upon insight gained in analytics. Incorporating analytics into marketing tactics will enhance the decisions you make as you execute on your strategies and plans so you can be more effective and achieve better results.

Marketing Optimization

After ensuring a commitment to data management and marketing analytics, the next logical consideration is to incorporate marketing optimization techniques into your marketing strategy. Marketing optimization includes capabilities that help you maximize economic outcomes by making the most of each individual customer communication while considering your company’s resource and budget constraints, contact policies, the likelihood that customers will respond.

With a marketing optimization strategy, marketers can target customers to maximize profitability, click-throughs and response rates, while taking into account customer disposition, stated preferences and analytically-driven propensities, and other business goals and objectives relevant to campaigns and communications. The benefits of incorporating marketing optimization into a marketing strategy include increased marketing ROI, higher response rates, reduced opt-outs, enforced customer contact policies, eliminating competing offers and increased customer engagement.

Key considerations for marketing optimization include incorporating offer-level propensity scores, integration with campaign management to streamline marketing operations, the ability to create “what if” scenarios to determine the optimal optimization scenario and post-optimization analytics. Marketing optimization should not be limited to just outbound campaigns, but with inbound marketing tactics as well.

Omni-channel

A campaign management solution should not be marginalized and used as a “list puller”. The key is to create an omni-channel marketing strategy to align outbound and inbound marketing tactics across all channels, inclusive of direct mail, email, mobile, social, web, call center, kiosk, etc., where customers typically engage.

To enable this type of strategy, the marketer must be able to quickly define target segments, assign offers, schedule campaigns and analyze results. Additionally, the marketer must be able to incorporate analytics “inline” with marketing flows to score customers for the next best action when it matters most – when and where they engage. When considering how to expand beyond list pulling and incorporating an omni-channel marketing strategy, your technology should support:

  • Event-triggered campaign tactics to ensure timely, relevant marketing strategies
  • Integrated email, social and mobile on one platform
  • One user-interface for the creation of business rules for inbound and outbound
  • Integrated analytics to allow for the next best action to take for each customer
  • Effectively measure campaign performance and response attribution across all channels

The benefits of including additional channels into a campaign management strategy include the ability to design and deploy more profitable campaigns across all channels, but also the ability to lower marketing costs by consolidating marketing technologies and eliminating technology and data silos.

Digital Intelligence

A November 2013 Forrester report titled “Digital Intelligence Replaces Web Analytics” helps paint a vision of how technology could be leveraged to best manage the proliferation of channels, devices and big data to enhance the customer experience, beyond the use of traditional web analytics or campaign management tools.

The “empowered” customer expects consistent, timely and relative content across all channels, which presents challenges for the marketer. It is not just the web – this includes social, mobile and meaningful email content. To address these challenges, the marketer needs to consider the following resources and capabilities:

  • Eliminate data silos to get a single view of the customer across all channels, which is enabled by the integration of online and offline data.
  • The ability to collect, transform and access online data for analytics, targeting and integration with offline customer profiles.
  • Integrated marketing analytics in both outbound and inbound tactics.
  • Multi-channel decisioning capabilities that must be easy to manage and deploy.
  • Marketing optimization to ensure the right content is being targeted to the right customer at the right time.

In addition to moving beyond campaign management, it will important for any marketing department to assess organizational readiness. Questions to ask would be are marketing functions departmentalized? Do you have visibility into marketing performance across all channels and tactics? Do you have the right skillset(s) to support marketing analytics, omni-channel marketing and/or digital intelligence? Do you need a partner to augment your current staff?

Lastly, technology is only a piece of the equation. Put the customer first. Steve Jobs once said “you have to start with the customer experience and work backwards to the technology”. The key is to align your marketing technology and strategy, with that desired customer experience

In summary, building upon your campaign management capabilities will enable you to expedite the purchase path, enhance the customer experience along the way, but also provide insight into what is working best to drive revenue.

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Capture, comprehend and act on the voice of your guests: Text analytics for Hospitality

In service industries such as hospitality and gaming, the ability to capture, comprehend and act on guest or patron feedback is critical. Previously we learned from Kelly that user generated content from social review sites influences the purchase process for hotel rooms.  Take a moment to think about how many times a hotel or casino interacts with their guests or patrons. With each of those interactions, there is the potential for data to be created. What better way to understand the wants, needs and preferences of guests and patrons than by mining that data?

But when it comes down to it - what percentage of the data generated by these interactions are you actually able to use? With so many channels for interaction, such as social media review sites and forums, guest profile comments, guest survey responses, call center logs and emails, whether internal or external, the amount of data can be overwhelming. One of the biggest challenges with feedback data is that the main insights are contained in unstructured text data, which can be complex and labor-intensive to gain insights from. But what if you could organize and visualize customer feedback data, would that help you quickly gain the insights that you needed?

The answer is yes. You can use a variety of analytic techniques to help you interpret or quantify unstructured text data, whether it is publically available or internally generated. The technique you choose should depend not only on the type of data involved, but also on the business problem you’re trying to solve.

When it comes to social data, descriptive statistics can provides a snapshot of current or historical performance. This method is used to answer questions like: “How many followers? How many reviews have been posted over the last two weeks? What is my average rating across the major review sites? How many times did someone mention the word ‘comfortable’ in a review?” This type of analysis is most commonly found in reputation management applications or other applications that help hospitality and gaming companies monitor and respond to social activity.

Social network analysis identifies connections among users in a social network, as well as the impact of the activity of those users. It also identifies interconnected groups of individuals and shows the influence each participant has within social networks. This technique was developed to identify fraud in the financial services and healthcare industries, but these days it’s also used by marketers in the communications, retail and hospitality industries to identify those that are most influential to the purchase decision.

When you need to analyze and quantify unstructured text data, text analytics is the best analytic option. Most text analytics procedures are based on some form of natural language processing (NLP). NLP is a methodology based on linguistics that uses both predictive analytics and rules-based processing to interpret the context and content of unstructured text documents. Within text analytics there are several types of methods that can be used on unstructured text data, whether it’s internal, online or transcribed from voice. The three main categories of text analytics are:

  • 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, which is similar to data mining. This method 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 NLP to the text documents. It identifies how your guests feel about key attributes of your product, brand or service – often in great detail.

Unstructured data can be notoriously complex, but applying text analytics makes it easy to filter, search and cross-reference this data. Hospitality and gaming companies have plenty to gain from a deeper understanding of their customers expressed need and preferences. Without text analytics, however, the time required to read and code all of that information can be highly prohibitive.

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The Analytic Hospitality Executive Blog: Announcing a new format!

We are very excited to announce that we are going to slightly change the format of our Analytic Hospitality Executive blog starting this month!  Based on our analysis of the performance of our blogs (and you better believe we look at this stuff carefully!), understanding the topics and people our readers like best, we’re going to move to a column format starting this month.  This way you will know exactly when to expect the bloggers you like best and the content you want to hear (or you can just keep reading them all!).  With this new format, we will be able to deliver even more great content, and most importantly, keep those analytical conversation going!

Natalie and I will each have a monthly column, and we’ll also feature a hospitality research oriented blog from our partners at Cornell.  We’re also introducing a new blogger, Bruce Swann, who is our Customer Intelligence for hospitality expert here at SAS.  You may remember him from Natalie’s post “What’s the future of email marketing” a few weeks ago.

The first full week of every month, I’ll post a blog about trends in analytics in hospitality.  Expect more from my “Pricing in a Social World” research with Dr. Breffni Noone in the coming months.  I’ll continue talking about building a strategic analytic culture, strategic implications of new revenue management analytics and practices, leveraging analytics to support key business initiatives like personalization and industry best practices.   I’ll provide my perspective on how our analytic hospitality executives can prepare to respond to the latest industry trends, challenges and opportunities.  Expect plenty of real-world perspectives, examples, quotes and interviews from practitioners, as well as a solid grounding in current research.

The second week of every month, we’ll feature a hospitality research blog working with our co-sponsors at the Cornell Center for Hospitality Research (CHR).  For the rest of this year, we’ll be working with the organizers of the Cornell Hospitality Research Summit (of which the CHR is a title sponsor).   Expect to hear highlights from scheduled speakers and sessions in advance of the conference in October, and then we’ll continue the conversation with session recaps and take-aways for the rest of the year.  Longer term, we will feature the latest research, responses to industry trends and industry predictions from the research faculty, as we did earlier this year in our blog post “What will 2014 Bring? Thoughts from the Research Faculty at Cornell’s Hotel School.”

The third week of the month, Natalie will continue to be explore the multiple applications of data management and analytics in the hospitality industry. The fourth week of the month, Bruce Swann will join us to discuss things all things digital marketing! When there’s extra time in the schedule, (or when the mood strikes), we’ll produce special feature blogs on topics like revenue management, interviews with research faculty, recaps of industry events, or even reactions to current news.

We have sincerely appreciated the great response from all of our readers to our efforts with this blog thus far, and we look forward to continuing our conversations with you in the months and years to come!  As always, we welcome your comments, feedback, questions and tweets!

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From the desk of the CHRS: Why the Cornell Hospitality Research Summit Has a Hospitality Exemplary Practice Award

The Cornell University School of Hotel Administration invites representatives of industry and academe again this fall to the 3rd Cornell Hospitality Research Summit (CHRS). A conference unlike any other, the CHRS is designed to create new knowledge through the intentional interaction of industry and academic presenters and participants. Although a tremendous amount of information has come from the presentations in the last two CHRS gatherings, one of the more interesting sources of solid industry ideas has been the Hospitality Exemplary Practice awards. Sponsored by IDeaS Revenue Solutions, the award is designed to highlight industry best practices, based in research which have solid implementation.

“The hospitality industry has a long history of innovation, and the CHRS has a specific focus on innovation and best practices,” said Vivek Bhogaraju, Director, Global Strategic Alliances, of IDeaS. “IDeaS is pleased to work with Cornell on the Hospitality Exemplary Practices award, to recognize the best practices that raise the tide for all industry participants.”

For example, one past winner was Best Western, which used consumer studies, supported by Maritz Research, followed by breakeven analysis to develop a system-wide upgrade. The research first had to determine which changes were important to the guests, and then the subsequent study demonstrated the value of making the upgrade to Best Western’s membership.

McDonald’s also got a nod for their research-intensive development of the McCafé Beverage Program, which has been a consumer hit. This was not an accident, because every part of this program had to pass three tests: customer taste tests, operations testing, and market analysis. With regard to operations, for instance, the products had to meet the company’s legendary time standards for order fulfillment.

The conference also acknowledged Wyndham Worldwide for working with Cintas to create an eco-friendly uniform program, which used fabric made with recycled polyester that has helped divert nearly 70,000 water bottles from landfills. The award judges noted the seven-step improvement program implemented by Viventa by Taj—Holiday Village, in Goa, which included relaxing check-in policies, reorganizing breakfast offerings, and instituting a new photograph-based approach to room preparation. Guest satisfaction scores increased by 20 percent after the program was rolled out, and the resort’s revenue per available room substantially exceeded its market “fair share.

What we’ve learned from these awards is that hospitality industry innovations and managerial practices must be based on rigorous research, and the conference would like to recognize even more of the industry’s best practices as part of CHRS 2014. That’s why the conference has asked people to nominate companies—or for companies to nominate themselves—to be exemplars for continued industry advancement for the Hospitality Exemplary Practice Award.

If you’d like to nominate yourself or someone else, follow this link!

As the leading source for research on and for the hospitality and related service industries, the Cornell University School of Hotel Administration invites representatives of industry and academe to the 3rd Cornell Hospitality Research Summit, on the beautiful campus of Cornell University in Ithaca, New York, October 12-14, 2014.
 A conference unlike any other, the CHRS is designed to create new knowledge through the intentional interaction of industry and academic presenters and participants.

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Responsible Use of Big Data: Evaluating New Data Sources

At the beginning of the year, I released my 14 actions for 2014.  I outlined a list of actions that hotels can and should take right now to ensure they are set up for success in the years to come.  Action #4 cautioned analytic hospitality executives to carefully evaluate new data sources.  I thought this action in particular was worth some additional discussion.

In this “big data” era, new data sources are cropping up every day – from internal sources and third party data re-sellers.  With all of this activity, plus constant messages from big data vendors and technology experts about the value of capturing everything you can get your hands on (I recognize that I am part of this as well, of course), it’s tempting to think that you can just shove all of that new data into a database and you’re good to go.  Regardless of how inexpensive storage space is getting and how fast processing is becoming, capturing, storing and analyzing data still takes resources  - technology and human capital.  Further, the wrong kind of data, used in the wrong way, will simply add overhead and noise to your analysis, rather than providing any additional insight.

There are myriads of detailed technical and analytical methodologies for assessing and transforming data to make it useful for reporting and analysis, which I won’t go into here.  In this post, I will provide some business-oriented suggestions for how to think about a new data source, and discuss potential problems that could arise from throwing too much data at a problem.

I’ve said this many times before, the first important step in evaluating a potential new data source is to determine what business value you will gain from accessing that data.  You should clearly and specifically define not just the insight you expect to be able to gain from that data source, but also who will benefit from that insight and how the company will take action.  Assess how the data could contribute to an existing business analysis, improve a decision making process, or help you gain new insight.  Knowing the “fit” at the level of business value will help you justify the investment in acquisition.

Once you understand the potential business  value, you need to be sure the data can actually deliver.  The second step is to understand the characteristics of the data source.  Ask the following questions:

  • What is the data? Make sure that someone in the organization has a clear understanding of the data fields, how they are calculated, what level of detail is available and what they mean. You will also need to understand how this data relates to other data in the organization. For example, if you are looking at time series data, does the level of detail and the intervals match any related sources? Also determine whether the data is unique, or highly related or correlated to another source.
  • How is the data collected?  Understanding where the data comes from will give you a sense of how reliable it is.  If it is heavily driven by user entry, then you need to assess the business process around the data collection.  User driven data is notoriously unreliable unless it has tight business process around it.
  • How often is the data updated and how?   Your systems will need to be set up to receive and store the data in a timely fashion.   If the data comes too fast, and the ETL process takes too long, it might be useless by the time you are able to access it.  For example, tweets or geo-location data are stale almost as they are created, so if you aren’t able to process them in time to use them, it’s not worth the trouble.  Further, if the data delivery process is unreliable (as in it frequently doesn’t show up, or shows up with missing values etc), and you are counting on it for a critical piece of insight, you may want to look elsewhere.

Finally, determine whether you will need any additional technology or resources to manage the data source.  Unstructured text data can be highly valuable to the organization, but it’s large, and it requires some specialized analytics to interpret.    There are also human capital implications for adding new data sources.  Do you have enough people available to manipulate and analyze the data so that it can be effectively used by decision makers?  Obviously, if you need to make an investment in new technology and new resources, more work is required around my first point – understanding the business vale.

If you are just interested in using the new data source for reporting, or descriptive statistics, the previously outlined steps will keep you out of trouble.  Throwing more data at a predictive modeling or forecasting analysis is trickier.  I am going to introduce some statistical concepts that you should be aware of as you are thinking of incorporating more data into an advanced analytic application.

Some of you may be familiar with Occam’s razor.  It is a principle of mathematics developed in the 14th century which basically states that “simpler explanations are, other things being equal, generally better than more complex ones.”  Many statisticians follow this guidance, believing that you should always select the simplest hypothesis until simplicity can be traded for predictive power.  Occam’s razor cautions us that simply throwing more data at a statistical problem might not necessarily generate a better answer.

In fact, statistical analysis bears this out in some cases.  Note that when I talk about “more data” in the next few paragraphs, I am talking about more “predictor variables” not more observations within the same data set.  Generally speaking, more observations will help to increase the reliability of results, since they will help to detect patterns in the data with greater confidence.

Two different statistical phenomenon can occur in predictive analysis with the addition of predictor variables to a model.  In both cases, the addition of variables decreases the reliability or predictablity of the model.  I’m only going to define them at a very high level here, so that you can verify with your analysts whether there’s a concern.  There has been plenty of research on both of these issues, if you want more information.

The first issue to watch out for is multicolinearity.  This happens in a multiple regression analysis when two or more predictor variables are highly correlated, and thus do not provide any unique or independent information to the model.  Examples of things that tend to be highly correlated could be height and weight, years of education and income, or time spent at work and time spent with family.  The real danger in multicoliniarity is that it makes the estimates of the individual predictor variables less reliable.  So, if all you care about is the model as a whole, it’s not that big of a deal.  However, if you care about things like what variable has the biggest impact on overall guest value, or on likelihood to respond, then you do have to watch out for multicoliniarity.

The second thing to watch out for is overfitting, which happens there are too many parameters relative to the number of observations.  When this happens, the model ends up describes random error, not the real underlying relationships.   Every data sample has some noise, so if you try to drive out too much error, you become very good and modeling the past, but bad at predicting the future.  This is the biggest danger of overfitting a model.   This is particularly problematic in machine learning algorithms, or really any models that learn over time (like revenue management forecasting, for example).

So, what is the bottom line here?  Don’t assume more is better, prove it!

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What’s the future of email in hospitality marketing?

This week I spoke with Bruce Swann, Customer Intelligence Solutions Manager for SAS, about the role that email will play in the future when it comes to hospitality marketing. Bruce is a veteran of marketing, having more than 15 years of experience working with marketing and CRM technologies, including marketing automation, predictive analytics and marketing resource management, as well as interactive marketing disciplines like web analytics, social media, mobile marketing and email marketing. At SAS, Bruce works closely with prospects and customers in the hospitality sports and gaming industries to help design the optimal customer intelligence platform based on current and future business requirements. As you can imagine Bruce is a big proponent of data and analytically driven marketing approaches. This week we chatted about how to get more impact for your investment when it comes to email marketing. And of course that raised the following question…

With so many marketing channels available to a consumer today, is email even still relevant?

Bruce explained that without a doubt – he believes that email is still relevant and a very powerful channel for a marketer to leverage for customer engagement and driving revenue. “In fact, while many think email is fading, quite the contrary; it is growing exponentially.” Bruce referred to a recent eMarketer report that projects there will be nearly 240 million Americans with email accounts by 2017.

“One of the contributing factors to the growth of email is mobile accessibility and the prevalence of smartphones,” he said.  “Also, with the advent of new devices, such as Google Glass and wearable devices like smart watches, access to email will be that much more convenient for the consumer,” Bruce explained, “and this presents new options for the marketer to target and engage customers.”

Bruce feels that the challenge for the hospitality marketer is not whether email will still be relevant, but how to be relevant with email and stay ahead of the competition.

How can a marketer be more relevant with email?

Bruce thinks that the bottom line is that it is very difficult for a marketer to be relevant with email, considering email is not the only inbox that a marketer competes with. “There is also Facebook, Twitter, Google+, Instagram, Pinterest and a multitude of other social channels,” he explained. In addition, it only takes a second to lose a customer to an opt-out. “You need to consider that of the top two reasons for a customer to unsubscribe from an email list, the top one is simply volume, or too many emails, and the second is that they are getting content that is just not relevant,” Bruce elaborated.

For an email to be relevant, hospitality marketers should ask themselves the following before an email campaign is launched - Is the email compelling and timely? “Marketers already know that the best way to engage with a customer is to leverage data-driven marketing to target them with content that is persuasive and compels them to act on the call to action,” Bruce explained, “but often what is missed is the timing of the email.” For example, will the email get buried in an inbox and ignored, or delivered to the inbox at the wrong time or the wrong day? “Analytics can provide insight into the best time of day or day of week to target a customer, but an even more productive approach would be to leverage triggered emails,” he explained.  Triggered emails are sent (or
"triggered") based on an action taken by the recipient.

The “triggered” in triggered emails refers more to the technology used to send the email. “Marketers need to remember to factor in some strategy behind the email as well as content to help boost revenue,” Bruce said. He gave examples of triggered emails that included welcome to the hotel emails, reservation purchase confirmation, website behavioral triggers such as interest in specific property features such as the spa, or even abandoned booking. “A recent Epsilon report states that triggered emails have open rates nearly 60 percent higher than non-triggered messages,” Bruce explained, “and the click-through rate on triggered messages is nearly 130 percent higher than on business-as-usual messages.”

What should a marketer consider with data-driven marketing when it comes to email marketing?

Bruce thinks that to really be relevant, personalization is just not enough. “Marketers needs to leverage all they know about a customer to determine the optimal content target,” he said. “For example, a customer who has viewed videos of hotels on the website might be far more receptive to view a video embedded in an email; a new customer might be more receptive to a cross-sell or upsell offer than a lapsed customer; a re-marketing email might resonate better with a frequent online shopper; one who browses on a mobile device should be targeted with mobile-friendly content; also - consider sending socially targeted emails to subscribers who have mentioned you on social media,” Bruce explained.

“For this to work effectively, however, relevant data needs to be considered, such as preference center data, web behavioral data, social and transactional data,” he said. Data also needs to be brought together to provide a more panoramic view of the customer. “Not having a more thorough view of a customer, or missing key data elements, is a major obstacle to data-driven marketing, which impacts the ability to target the customer with the optimal content,” he said. When you incorporate data-driven marketing into an email strategy, along with a focus on what customer is interested in, what they are telling you and what you know about them, you can better determine what to communicate in a way that is relevant and drives engagement. “This also enables more effective segmentation strategies,” Bruce explained, “which will enhance the ability to target customers with engaging content.”

How can analytics be used in email marketing?

“In general, predictive analytics can be used to predict what content, or offer a customer is likely to respond to,” Bruce said, “but another effective use of analytics in an email strategy is segmentation.” Analytics can help determine email marketing segments where the customers within the segments have common needs and priorities. “This means the marketer can better align the content and timing of an email campaign with customers being targeted,” he explained. One of the benefits of this approach is that it can prevent poorly targeted email campaigns from overwhelming subscribers with seemingly meaningless offers. “Analytically driven segmentation narrows the focus content, based on the segment’s characteristics, which in turn decreases contact fatigue or opt-outs associated with poorly targeted emails,” Bruce said.

When it comes to measuring the success of email marketing, what metrics should a hospitality marketer care about?

“Beyond standard operational metrics such as opens, clicks and conversions, the marketer should also measure engagement,” Bruce said. As he explained it, engagement pertains to email activity that drives not just opens, clicks, time on site, and conversions, but also activity that increases repeat business, increased spending and lifetime value. “I would recommend that you seek out metrics that help answer questions such as how many people clicked through on your email but didn’t convert? Of the people who converted on your website, how many of them came from social media? How many opened your last email campaign? Which results in more leads, PPC or organic search: email or social?” Bruce said. “These are the answers that will result in more targeted, engaging and revenue-driving email marketing.”

In summary, when it comes to email marketing strategy, the hospitality marketer should keep a few key considerations in mind: to be relevant with email, the approach must be compelling and timely. The content of the email will mean nothing if it arrives in the inbox along with a deluge of other ill-timed emails; the timing will mean nothing if content is bland and meaningless to the recipient. By incorporating data-driven email marketing, triggered campaigns and analytics, email strategies are more likely to drive engagement and revenue opportunities.

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