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.

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It's not the size of the data, it's what you do with it.

With all of the discussion about big data these days, it is easy to think that every problem is a big data problem. Yes, there is a lot of data out there these days, and of course we all love a nice big data set, but you don’t always need tons of complex data to derive important insights about your business. Continuing with the theme of avoiding big data paralysis from my last blog post, when it comes to data and analytics, it isn’t the size, it’s what you are able to do with it that really matters – as long as you are doing it right, of course.

Don’t assume every analytical problem is a big data problem.

As I alluded to the in the introduction to this blog, just because you are faced with an analytical problem, doesn’t mean that you automatically have a big data problem to go with it. Remember, before there even was big data, there still was math and statistics. For predictive analytics, the more observations you have, the more confidence you can have in the results. However, remember the rule of thumb from back in your statistics classes – 30 observations is enough to have confidence in the results such that you can derive insight. Detecting patterns, or identifying drivers, even with small sets of data, can give you directional guidance, even if you are not coming up with an exact answer with 100% surety (which you never have in statistics anyway, but that’s a theory for another day).

For example, if you were trying to improve guest satisfaction scores, you could run a basic correlation analysis using a small sample of guest surveys, maybe 50 surveys drawn at random from all of those submitted in the last month, to see which detailed questions correlate most strongly with overall satisfaction. If you found out, for example, that the time to check in was highly correlated with satisfaction, and both scores are lower than you like, there is probably a need to invest in more staff, more training or better processes at the front desk. This insight, even from a small sample, will give you directional guidance as to where to invest your efforts. Very important to remember, however, that correlation does not imply causation – it does not identify the direction of the relationship. You can’t say that long check in lines are CAUSING overall satisfaction decreases. You can say that, because they are related, as check in scores improve, overall satisfaction scores should also improve.

Even plotting a series of data in a trend line instead of looking at them on a static report can result in additional insight. For example, plotting cover counts at Tuesday lunch every week would identify overall trends (increasing or decreasing counts), seasonal patterns (we tend to be slower in cold weather), or even outlier events (the large conference from last month).   This information could help to build programs to increase business and track the success of those programs after implementation.

Many analytical problems can be solved without big data storage or big data analytics.

Most hotels will have to invest in their IT infrastructure to handle big data and big analytics initiatives. Unless you work with a SaaS or cloud provider, hotel companies will need to invest in new database infrastructures and new processing methods for solving huge problems calculated against complex data sets. (I cover these kinds of investments at a high level in my blog “Big data is a big opportunity for hotels” Part 1 and Part 2.)

However, there are many analytical problems that can be solved without these “big” technologies. Before the technology innovations that facilitate the storage, access and processing of big data went mainstream, many organizations relied on sampling (working with a manageable subset of the data – like the guest survey above) simplifying assumptions, or data sets that were smaller, and therefore, were able to be processed in a reasonable amount of time. You do not have to wait for the modernization initiatives to be complete before you are able to gain any insights from your data.

For many organizations that are just beginning a journey down the analytics path, starting with big data storage and big data analytics (even if delivered in the cloud) can be a bit like killing a mosquito with a sledgehammer. Too much technology, too fast. The organization needs to grow with the data and technology, or it simply won’t get used. Excel has its limitations, of course, but if it gets people comfortable working with data and performing some basic analytics, then it’s serving a good purpose, but only if, clearly, the organization is able to take action on those insights. Even better, powerful new visualization tools are facilitating broader access to data, and some even have some light analytics like correlation or trend analysis that can help analysts derive predictive insights in a wizard driven environment. The best part is that any value derived from these smaller analyses provides justification for a larger investment down the road.

Understand the problem first – and then select the right data and analytic technologies to solve it.

This cannot be repeated enough. Analyzing data for the sake of analysis is not productive – in fact will just lead to distraction. To move the business forward, you should start by defining the problem you need to solve, not by analyzing a data set. This will keep you and the team focused and productive.

It may be obvious from the title of this section, but here is a good way to think about it:

  1. Define the goal or problem you are trying to solve (increase revenue, decrease labor cost, better guest segmentation, improve engagement from loyalty program members).
  2. Figure out what data you need to solve the problem – this is blue sky thinking
  3. Match that list to the data you actually have – this is where reality sets in. Determine whether what you have is sufficient to provide insight. Make a plan to collect the data you don’t have.
  4. Pick the analytic technique – understand whether you are looking for something descriptive (How many? What’s the average?), which you could derive using any standard reporting tool, or something more predictive (Why is this happening? What are the factors that are causing this to happen?), which might require a statistical package or application.
  5. Decide how the results will be communicated – are you building a report, displaying results or providing recommendations? This also involves understanding who the results should be communicated to, how often and when.

No one type of analytics is better than the other. Each does different things, solves different problems and requires different software and architectures.

Much like a hammer won’t fix everything that goes wrong with your house (much to my disappointment, as that is pretty much the only tool I know how to use effectively), one analytics methodology or technology architecture won’t solve all business problems.

Descriptive analytics like reporting, determining averages, or setting up alerts are based on historical snapshots. They are very useful for keeping your finger on the pulse of the business. Predictive analytics are forward looking. They will help you anticipate trends and identify opportunities. Statistical analysis helps you figure out why something is happening. Optimization tells you the best that can happen given your operating constraints.

A related series of analytics, like revenue management, where each result feeds the next step (demand modeling, then forecasting then optimization), requires a completely different technology architecture than quarterly performance reporting. The heavy-duty analytics in revenue management require an architecture that is designed for fast analytical processing, especially considering that prices need to be updated at the speed of business. The data-intensive process behind a performance report requires a data architecture that loads data fast, calculates report fields efficiently and enables the flexibility to drill-down, sub-set or explore.

You can’t get an optimal price from a historical report, and your revenue management system isn’t designed to be a full-service, business intelligence tool.

What you do with it matters. Find the “so what”

Whether you are dealing with big data or small data, as the title says, what you do with it matters. All of the best analytics in the world don’t matter at all if the consumer of the information doesn’t know what they are supposed to do with it – or worse yet, there is nothing that can be done. The single most important thing about data and analytics is the action you take based on the results you are presented with.

Even with small data, you need to carefully consider how to present the result to the end consumer so it is very clear what action needs to be taken. Is it a performance report that will be used to inform shareholders and stakeholders? Are the numbers they care most about highlighted, with enough backing to ensure they understand where they came from? Maybe you just need a single answer. Instead of providing reams of charts and graphs, just give the recommendation up front. We need to work on our check-in process. Tuesday lunches are getting busier, so we need to schedule more staff. Your analysis is the backup for this recommendation, but only if the information consumer needs backup.

In these last two posts from me, I hope I have convinced you that some data is better than no data and some analytics are better than no analytics. Even if all you have to work with is small data, you can still move the business forward. We have a tendency in this industry to get trapped by inertia, and by “the way things have always been”. You will never convince your organization to do something if you keep doing nothing waiting for something to happen.

Big data isn’t just for big business - learn more with SAS Insights.

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How to avoid “big data paralysis” - A Guide for Hoteliers

We have spent a good deal of time at the Analytic Hospitality Executive advocating for the value of big data for hospitality. Just a few months ago, for example, I wrote a two part series on how Big Data was a “big opportunity” for hotels and casinos. Our goal at this blog is to help you understand opportunities to leverage data and analytics to move your business forward. Big data and big analytics, and the technology to take advantage of them in particular is a complex and fast moving topic. New opportunities constantly present themselves. It is difficult to sort through what will be sustainable and what is a passing fad. It can be confusing, risky and uncertain. It is difficult to justify investment today when the game may be changed completely by tomorrow.

It is just this challenge that I want to address in this blog. With all the highly publicized opportunities in big data and the ever evolving technology landscape, most hospitality and travel organizations are proceeding with caution when it comes to this area – and with good reason. These are expensive investments with many moving pieces.

There has to be a balance, however. Hospitality companies need a solid data and analytics program to support their overall business strategy, and to stay competitive. This strategy should be carefully constructed in light of the business requirements and organizational goals. However, none of these initiatives have to be perfect right out of the gate. There is too much potential in the data to wait for the perfect data warehouse or the most robust analytics package with the most innovative real-time collection and delivery. This isn’t rocket science or brain surgery. Something is better than nothing. Directional guidance can be highly valuable, even if it doesn’t point you to the optimal solution.

I was reminded of this years ago when I was doing a consulting engagement at a casino buffet restaurant. Casino marketing was running a two-for-one promotion that was creating long waits for tables. So much so that the customer satisfaction scores that the managers were bonused on were suffering. We were called in to build some capacity or revenue management strategies to reduce wait times and increase throughput. In our initial meetings we brainstormed the idea of running an optimal table mix analysis – an optimization algorithm that matches table sizes to the party size distribution, reducing the number of empty seats. We needed to collect and analyze party size distribution, run the optimization algorithm and then scenario test to make sure the mix would stand up against the variability in the distribution.   When we returned a few weeks later to review initial findings, the manager pulled me aside. He told me he liked the table mix idea, figured he had too many four tops on the floor, and went ahead and replaced them with a bunch of tables of two. He told me he was doing about 36 more covers per hour, and that he felt like satisfaction was increasing. The actual analysis revealed that he was off by a few tables here or there, but the point was that he was able to take advantage of the opportunity to increase throughput and reduce wait times before the “perfect” answer came, just by using data gained by his own observation of the operations.

Before you bite off big data, you are better off working with what you have, driving value and using that to set the path forward.

Organizations need to think through how they can use their existing data to drive value today as they configure and grow the database. Testing and learning on what you have will help to inform future data acquisition and discovery plans, and help to prioritize future actions. You may discover that the data point you thought was crucial is just not that important. You will certainly uncover new sources or directions for the analysis and future data collection.

One of the initiatives I have been hearing a lot about through my travels is the opportunity to use free wifi sign up to do location based marketing. Providing this service to guests or patrons, in exchange for being able to do push marketing does provide an opportunity to interact, and potentially drive revenue. Retail, airports and casino integrated resorts seem most interested in this right now. At first my reaction was “well, what do you really know about that mobile user anyway? How useful can this limited data really be?” As I thought it through, I realized that the limited data actually represents a huge opportunity to get creative, to test and learn and to execute against a very contained problem, which has huge implications when more information is added. There is no point in waiting around for the profile to be complete and accurate – you can only get so much information from any one patron.  Taking each of these interactions together as a whole, however, using the opportunity to quickly test and learn, provides a big opportunity to drive value.

A recent study from our partners at the Cornell Center for Hospitality Research, “The Mobile Revolution is Here, Are You Ready?” conducted by Heather Linton and Rob Kwortnik, investigated traveler preferences for interacting with hotel companies through their smartphones. They found that most guests prefer to use their smart phones to automate routine tasks like checking in, ordering room service or contacting staff. If there is a problem, they prefer to talk to a person. Hotels are taking on many technology driven initiatives to reduce friction through the guest journey, like mobile check-in, tablet ordering for room service, and mobile phones to unlock doors. Many of these initiatives are operationally focused, and organizations do not appear to be thinking through what further insights could be gained from the data collected from them. Are there hidden opportunities for insight? The data from any of these service oriented initiatives, limited though it may be, could potentially add value to a predictive analysis, provide insights about the behavior of guest segments, and help you design service processes that increase guest engagement and drive guest value.

Think creatively about what you would do if you only had an identifying number (mobile number) and knew the location of the person holding that device at this moment. The mobile number itself provides a clue to where the guest is from – but that’s really about it. What kinds of offers or promotions could you send to them, keeping in mind that would be able to collect what they responded to and know whether that same mobile number returned to your property?   If they were having dinner in the upscale restaurant, maybe promote an after dinner cocktail in your lounge. Spent more than three minutes in front of the retail shop, a coupon for 20% off their purchase, good only for an hour from now. This is basic, there are probably some clever offers you can build that will get the wifi user to reveal some additional useful information about themselves that could be extended to others that exhibit the same behavior.   You will certainly not get it right every time, or even most of the time, but you can learn over time based on the redemption of the offers across the customer base.

The wifi vision involves some technology investment, and it is a big data problem because it involves real time decisioning, but I use it as an example of how even a small amount of data about guest behavior can provide valuable insight. A next step could be to tie back to guest profiles, so their location and promotional response enriches what you know about them, and their profile provides opportunities for more targeted promotions.

Many hotel companies are making investments in building a robust guest database to support more targeted marketing and facilitate personalization initiatives. You should make an effort to continue to gather more detailed information about your guests to enrich your guest database, but while you are doing that, why not work with a small select set of data to test out some options? Pick a dataset that you are confident is clean and credible, and work with the team to figure out what insight can be gained from it. Can it demonstrate the value of some more advanced segmentation analysis? Can you use it to test out the attractiveness of certain channels or certain promotions?

Given how competitive the environment is becoming and how fast technology is moving, hotels cannot afford to wait until everything is perfect before moving on to the next step. Applying advanced analytics to the data you have today can provide enough insight and value to justify moving forward with the data you want to collect for tomorrow. Avoid getting stuck on the idea that you must have robust big data sets before you can take any action.

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Real-time marketing for Casinos

 

The gaming business moves fast. Casinos serve a multitude of entertainment options to thousands of patrons 24 hours a day, a pace that results in a myriad of interaction points with their patrons. Competition in this service industry is fierce. If patrons at a casino do not feel that they are being offered something that they want, it is all too easy for them to find another entertainment option for their hard-earned dollars. Patron expectations are high. Casino companies have a lot at stake when they entice patrons through reinvestment in the form of free play, free meals, and even free accommodations. Making high-quality real-time decisions during each patron interaction is critical to the success of a casino.

However, managing interactions among patrons in real time comes with its challenges. Patrons today do not act in typical ways; therefore, it is difficult for a rules-only approach to be successful. Some patrons are there for gaming, some for shows and entertainment, some for dining or nightlife, some for spas, and maybe even some for golf. If a casino lacks a comprehensive understanding of their patrons’ needs and preferences, actions taken with patrons can fall flat.

When identifying the actions to take with a patron while he or she is in the casino experience, casinos need to manage the delicate balance between ensuring that the offer is attractive to the patron and maintaining profitability for the casino. Showering patrons with free food and drink, hotel rooms, show tickets, or even cash in a bid to maintain their loyalty can easily backfire. They can result in a direct impact on the bottom line, or patrons can start to feel that these treatments are meaningless to them. Predictive analytics can supply the much needed context to the patron experience. When coupled with real-time decision capabilities, a casino can truly enhance and personalize the interactions that they have with their patrons.

Real-time decisions are decisions that are made at a customer’s point of experience, using data captured from customer interactions as they occur, along with historical information and analytics output. Real-time marketing involves adding context to the channel through which the casino is interfacing with a patron. Context can be defined as the circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood and assessed. Therefore, for a channel to have context, it must provide facts that describe the patron’s current situation. Here is an example.

John Smith is a very high-value, high-frequency patron at a particular casino; however, he has not visited the casino in the last six months. John inserts his casinos rewards card into a slot machine on the casino’s gaming floor. The casino can then gather John’s preferences, his previous gaming history, his theoretical worth, his predicted lifetime value, and the identity of John’s casino host. Using the information, an alert can be sent to the host to suggest that he or she go and greet John, welcome him back to the property, and provide him with an invitation to an exclusive poker tournament.

Data about the patron experience can be used to determine an appropriate offer in real-time.

Data about the patron experience can be used to determine an appropriate offer in real-time.

This is an illustration of real-time decisions in action, or real-time marketing. Real-time marketing, or the ability to provide context to a patron’s interactions, enables you to provide relevant, insightful offers, recommendations, advice, and even service operation actions when they are needed the most. In the case of John Smith, the offer of a seat at a poker tournament is extremely relevant to him, based on his previous behavior and preferences.

High-value casino customers like John Smith are very accustomed to receiving preferential treatment based on their casino activity. In the larger gaming jurisdictions such as Las Vegas and Macau, it is not uncommon for a customer to have rewards accounts established at multiple casinos. Casino patrons value the high-touch service they receive once they reach preferential reward status with their preferred casino company. However, an unhappy patron only has to present his or her current player’s card at a different casino and he or she is very likely to be granted the same status with the competitor. Therefore, making high-quality, real-time decisions during each patron interaction while the patron is still in the casinos is critical to the success of a casino.

To execute real-time decisions, you need real-time data on patron interactions, historical data on your patrons and their preferences, historical information and predictive analytics scores, and a real-time decision engine such as SAS® Real-Time Decision Manager.

Real-time data is the flow of data captured from patron interactions as they occur. By using various technologies such as websites, kiosks, slot machines, iBeacons, and smart phones to track and interact with its patrons, a casino can collect data from consumers both explicitly (via forms and purchases) and implicitly (via web sessions and geo-location). When these systems are connected to networks, the data can be shared in real time with other systems within the casino. As a result, casino organizations can have access to a new level of detail about patrons as they interact. Any insights gained about the patron can be used in real time to create a response while the patron is still interacting. These offers can be more relevant because they can be targeted based on the current interest and status of the patron.

Real-time data lets casino operators know what the customer is doing now and lets them target their customers with offers when they are most likely to respond positively. Historical data gives the business users the ability to develop predictive models to help determine the likelihood that a customer will act a certain way, such a booking a hotel room or redeeming a direct mail offer. Combining real-time data, historical information, and analytic results into real-time decisions enables a casino operator to know which patrons will take particular actions based on the most up-to-date information and deliver decisions and recommendations that optimize every patron interaction to improve revenue, growth and retention.

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Guest-centric hotel marketing: Identifying the drivers of guest value.

In a previous post, I discussed how hospitality marketers can gain a complete picture of their guests and understand guest behavior with analytics. In this post, I will explore what you can do once you have a complete picture of your guests.

Today’s operating environment presents a set of challenges to hotel marketers. The availability of data from loyalty programs, combined with increasingly diverse on-property offerings, provides the opportunity to know your guests at a deeper level. It is not enough to know who your high-rate paying guests are, since your best guests may be spreading their spending across all the revenue-generating outlets at your property. These guests expect you to know who they are and what they like. They do not want to be blanketed with irrelevant offers, but will respond when you personalize interactions. Historical information gives a limited picture of guest value, but when augmented with predictive analytics, can provide a powerful tool to predict behavior. If you know what your guests are likely to do before they do it, you can take steps to encourage or discourage behaviors. All of these challenges are opportunities to “surprise and delight” your guests, while increasing the economic value of your guest base.

Careful analysis of your guest’s behavior can uncover the main drivers of guest value. Your most valuable guests will dictate the elements of your offering that drive their spending. Is it the monthly restaurant events, the spa offerings or, in the case of a casino, a complimentary room offer? Each of these elements drives purchase behavior, but not all of them may encourage a guest to spend more, and spend more profitably. As these drivers are identified, your operation can adjust to make them more accessible and/or more profitable. Should you have a restaurant event next month? Should you reserve some tables at the high-end steakhouse during peak dining hours for your most valuable patrons? Should you extend the spa or fitness center’s operating hours? Do you need more package options? Are there enough rooms of a certain type? Once the value drivers are established, forecasting will help predict the impact on revenue and profitability far into the future, in plenty of time to take corrective action.

Luxury hotel room

Luxury elements in guest rooms could be one of the drivers of guest value.

Using predictive analytics you can identify the elements of your service that drive guest value and track these over time. Spa promotions, golf offers, food and beverage specials or luxury elements in hotel rooms could all be drivers of guest value. Once the drivers are identified, you can project how they will affect profitability. If responses to offers are not tracking as expected, you can send an additional offer. If luxury elements in the rooms cause additional housekeeping effort, you can invest in tools to reduce these expenses. In addition to looking historically at these drivers, you can forecast how the drivers will affect future revenues and profitability. Having forward looking insight into the business allows you to proactively monitor these impacts using powerful predictive analytics, and take corrective actions before your business is affected.

Once you identify your most valuable guests, understand their activities and behaviors and identify the drivers of value, marketing automation tools such as SAS® Marketing Automation can help you create targeted promotions designed with your guests in mind. Using analytics with marketing automation, you are now able to send the right offer to the right guest at the right time. Automation reduces the labor involved with creating and executing a promotion, and personalization ensures that your guests receive the right offer for them. Marketing budgets are reduced at the same time that responses increase. Why send out an offer to your entire database when you only need a specific number of responses? In addition, guest satisfaction increases because you are not blanketing your guests with junk mail, and the offers they do receive are still available when they call to redeem them. The answers to what your guests want are sitting in your data, and predictive analytics is the key to finding these insights.

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Do I know you? Personalization without a guest profile.

In a noisy distribution environment, hotels are in significant danger of becoming commoditized. Differentiating themselves not only from the competition, but from the third party distributors, will be crucial to maintaining a competitive edge, or even just maintaining profitability. In an effort to connect better with guests, most hotel companies are taking on initiatives to improve the guest experience, whether through personalization efforts or giving guests more control over their experience through mobile check-in, mobile keys or choice of rooms.

To support this effort, hotels have been talking about getting that 360 degree view of the guest – gathering as much information as possible about needs, preferences and behaviors across all of their interactions with the guest. This information can be leveraged through the guest journey to provide more relevant offers, improve communications and increase engagement.   Loyalty programs help hotels to gather this data – effectively rewarding guests for being willing to provide information about themselves. Hotels also try to gather and match any personal information provided at reservation or checking – emails, phones address, and tie that back to a person or a stay. Natalie and I have talked in this blog about the importance of that 360 degree view and the benefits infusing analytics through the guest journey to support personalization efforts.

The vision of stitching together fragments of data collected across the guest journey is challenging enough when you have a profile to work from, but most hotels only “know” a small fraction of their guests. Most of the traffic to the website, or even guests in the hotel at a given night, are not included in the hotel’s guest database. So, if you don’t know the majority of your guests, how can you implement personalization initiatives?

Interacting with an unknown guest is all about using what you can see about them (no matter how little) and analytically comparing that to what happened with other guests who looked or behaved like that. By tracking their search behavior and clicks, a picture begins to form of what they might be looking for, and potentially who they are. Using this information, compared to the activities of “known” guests, the website can start proactively surfacing relevant content, understanding options for “next best offer”, tracking the visitor’s reaction and then incorporating that back into the decision of what to surface next.   The IP address can be recorded, and the next time the unknown guests comes back, you can use what you learned from their previous search behavior.

Some of the actions may be based on business rules (if the visitor searches for resort properties, show beach pictures in the sidebars), others on analytic results (predictive modeling shows a 20% increase in booking likelihood when guests who followed a certain path were shown this specific piece of content).   It doesn’t have to be that complicated, really. Just use what you can see to help the guest cut through the “clutter” of all of the many option you have available to find exactly what they are looking for.

The results of these interactions will not be as accurate as when you have a richer profile of the guest. At least you can start testing and learning, and trying to be a little better than a generic web experience. You will get better over time with that specific visitor, and with your customer base in general.

Remember that underlying any offers you surface or paths you direct both known and unknown guests through on the website, are certain key tenants. You’ve probably heard these from me before, but they are worth repeating:

  • Offers should be profitable, driving business where it’s needed and protecting the firm from revenue dilution. Revenue management forecast and price recommendations should be incorporated into the offer delivery on the website and through other channels. Customer value should be carefully understood so that you are not over-incentivizing guests.
  • Make it easy to do business with you. In the process of intelligently surfacing content and improving the guest website experience, don’t forget that your whole goal is to convert. If the guest can’t find the “book now” button in the middle of all of your beautiful, search engine optimized content, they will go elsewhere.   Throughout the entire search experience, it should be crystal clear to the guest what they have to do to make a booking. The OTAs do it, hotels should too.
  • Profiles and business rules can be dangerous. As you are building a guest profile, remember that demographic information alone does not form a complete picture of who the guest is. Behavior and context are also crucial pieces of information that fill out the picture. Simply using demographics and applying business rules will result in bad decisions. Think about the business traveler who this time is taking the family on vacation. Their needs and preferences will be dramatically different. Think about the variety that could exist within a segment made up of young professionals from the Northeast who tend to stay in city center hotels.
  • Don’t cross the line to creepy. Just because you can collect information about your guests (mining their Twitter page, stalking them on Facebook), doesn’t mean you should or you should use it directly. The winning companies will be able to properly operationalize the information they collect or derive to improve the guest experience. It is going to be really easy to get this wrong. Companies already are. For every piece of data you collect or insight you derive, be sure to ask yourself what you would use it for. If you don’t have a good answer, don’t waste your time.

Differentiation in the noisy digital space is becoming increasingly difficult. Guests can come to you through a variety of paths, and for a plethora of different reasons. Using the digital crumbs they drop along the way can provide opportunities to improve their experience, drive their loyalty and repeat business, and improve the hotel’s revenue and profits – no matter how much or little you already know about them.

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How is innovation shaping the hospitality industry?

Innovation within hospitality drives awareness, service delivery, guest engagement, and brand differentiation. SAS asked a panel of experts to comment on how innovation is shaping the hospitality industry.

 

According to many of our experts, analytics is at the heart of innovation. Learn more in this white paper on building a strategic analytic culture for the hospitality industry.

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Hospitality Industry Challenges in 2015: Our panel of experts weigh in

From the pressures of a highly competitive marketplace to changing economic conditions, to the evolution of the distribution network - the challenges facing the hospitality are many and varied. In this video, SAS asked a panel of experts to share their views on the issues that will challenge the hospitality industry in 2015 and beyond.

 

How do these compare with the challenges that you are facing in 2015? Here's a list of 15 actions to take in 2015 for hospitality executives.

 

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The 360-degree view: Gaining a complete picture of your guests

For hotel companies, it is challenging to find new ways to differentiate in an ever evolving marketplace. There is a lot of talk in our industry about the increasing numbers of third party channels and distributors to have entered the marketplace, and how that impacts the hotel company’s core business. Every one of these players is competing for your guests. However, most hotels possess an advantage over their competition that is often sitting forgotten in their databases - the detailed transaction data that they have on their guests.

To stand out from the competition, hospitality companies need to match guests with highly targeted offers that demonstrate an understanding of both guest preferences and value. To ensure these offers strike the right financial balance to drive profits, hospitality companies should base their campaigns on a true understanding of guest worth, both today and in the future. A 360-degree view of the guest is essential to achieving this vision. But what is a 360-degree view, and how do hoteliers achieve it?

Today’s hotel guest is likely to participate in a variety of activities across the property, including dining, meetings, spa, even golf and shopping if available. To gain a 360-degree view of your guests, these activities need to be captured and linked back to the guest. Gathering this information from disparate operating systems presents a technical challenge for many hotel companies, as do the analytic techniques that extract guest behavioral insight.

Hotel and resort companies are gathering the information needed to know their guests, but often have difficulty determining how to access and use the data. And once the data is gathered together, how can hotels companies ensure the quality of the data? Duplicate guest profiles and duplicate transactions are common in property management systems as well as customer relationship management systems. When you add the other systems such as point of sale, restaurant reservations, spa or golf systems, the problem can grow exponentially. That’s why a thorough data management approach, including data integration and data quality is essential to an accurate picture of your guest’s behavior. When you have access to relevant, accurate information about your guests’ actual behavior you can use that information to make informed business decisions with clarity and confidence.

Once you have a 360-degree view of your guests you have many more opportunities to leverage analytics. Using advanced segmentation strategies and predictive analytics, micro-groups of guests with similar preferences and purchase behaviors can be identified. Perhaps you want to look across all of your guest 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 spa operations such as opening hours or even a renovation will directly impact the revenue of the restaurant. Additional data on guest behavior enables you to group guests by more than just their room revenue, but rather on all of their activities across your estate. Micro-segmentation allows you to be much more laser-focused in your marketing and service efforts, and your guests will feel that you really understand them.

Access to information about your guests at a detailed level allows you to match your service offerings and your marketing efforts to the set of customers that responds best to it. You do not have to be all things to all guests, but you can define your core offering based on your most valuable guests, and work on attracting other similar customers. New guests exhibiting similar behavior to your most valuable guests can be identified and nurtured, so you do not have to build an extensive guest history before you are able to identify and serve them. Behavioral indicators signal an opportunity for hotel companies to intervene to encourage or discourage expected behaviors. For example, churn models predict when an individual guest is at risk for leaving, so you can take appropriate action before he or she defects.

Your guests are also interacting with you via websites, social media channels and mobile aps from when they first start to do research on your property, to well after they have departed. Adding online behavior data, which includes not just what was purchased, but also what was viewed by your guest during the purchase process can help your understanding how to cross-sell and upsell your service offerings. If a guest was reviewing spa open hours during their room reservation purchase, would that same guest respond well to an offer for a spa appointment? In many cases the offers do not need to include a discount to make them effective.

Redefining “valuable” and nurturing these relationships allows you to be more proactive, creative and focused in your marketing and service offerings. The hotel that can demonstrate a personal knowledge of their guests, and drive revenue while generating an attractive bottom line, will rise above the rest. Your guests will ultimately reward your attention with increased loyalty and spending.

How are you consolidating information about your guests? Do you currently have a 360-degree view, or single view of guests available to you? How are you deploying this in both your marketing and service operations efforts?

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The Cornell School of Hospitality Administration: What will 2015 bring?

We asked our partners at the Cornell Center for Hospitality Research to poll the research faculty at the Hotel School to understand their guidance about what to expect in 2015. We were also able to get a preview of what the faculty will be working on in terms of research this year. I know the Center for Hospitality Research is always interested to know what the industry is worried about or wants to investigate, so I hope you will reach out with your questions, comments, and interesting business problems!

What should the hospitality industry expect in 2015?

Bill Carroll, Clinical Professor, Services Marketing, believes that the economy and technology will be big drivers for hospitality in 2015. He says: “Technology will drive change in the ways consumers engage travel and hospitality suppliers. Increasingly consumers will find travel planning, execution, and savoring more digital, mobile and seamless. This will be driven primarily by consumer use of mobile devices, increasingly functionality of apps; and increasing use of big data management. As important, these trends will financially favor intermediacies over suppliers.”

Bill is an economist, so we asked for his predictions in that area as well. He predicts that North America will grow, while China will shrink slightly. In the balance, it will even out, but expect more activity from NA, and a bit less from China.

From a human capital perspective, Mike Sturman, Professor, Associate Dean for Faculty Development, Academic Director of The Center for Hospitality Research and The Kenneth and Marjorie Blanchard Professor of Human Resources, reminds us of labor legislation that will impact our industry “A huge issue for the hospitality industry in 2015 will be employee wages, and in particular the wages of minimum wage or near-minimum wage workers. As of January 1st, 21 states and Washington DC saw increases to the minimum wage. Eleven states have increases scheduled for the future, and 16 states have minimum wage linked to cost of living or similar indexed increases. The effect of these wage increases will be particularly salient for the restaurant industry, but we will also likely see related increases in pay across much of the hospitality industry, as even works earning somewhat more than the minimum wage are likely to see increases in their pay related to minimum wage changes. This year will also see continued battles between employer and labor groups on this issue, as the fight over minimum wage and living wage changes are only going to continue.”

Tony Simons, Associate Professor Management & Organizational Behavior, agrees “In the US and elsewhere, there is growing awareness of, and the actuality of, extremes of income and wealth inequality. Along with that is, in the US, an awareness that the economic recovery has largely left lower-wage workers behind, and that there is a growing pool of working poor.”

To net it out for our analytic hospitality executives, the maintaining the delicate balance between labor costs and service levels is only going to get more challenging in the next year. If you don’t have the right forecasting and optimization to understand demand patterns and match that to needed labor, you’ll throw your organization out of balance.

Dave Sherwyn, John and Melissa Ceriale Professor of Hospitality Human Resources, Academic Director of the Cornell Institute for Hospitality Labor and Employment Relations and Stephen H. Weiss Presidential Fellow, mentioned the NLRB is seeking to change the test to determine if a franchisor is a joint employer from the control test to the Industrial Realities test. The NLRB could be the tip of the iceberg – the DOL and the EEOC could follow. If this comes to be, franchisors will either: (1) relinquish substantial control over their brand; or (2) be responsible for employment related liabilities.

Soberingly, Jack Corgel, Robert C. Baker Professor of Real Estate and Director of Graduate Studies for the Baker Program in Real Estate, reminds us that terror attacks on major US cities can become a real possibility, given recent activity.

How do you think the hospitality industry needs to be prepared to address these trends?

Bill suggests that “In a word, the hospitality industry must be “resilient”. Fortunately, the major changes driving change in 2015 are foreseeable – technology and economic growth. So, forward thinking is straightforward for impacts that have high probability of occurring. Simultaneously, a focus on the strategic core of service experience provision is critical. Finally, organizations should insure that the core of their service experiences are made "resilient" to foreseeable and non-foreseeable impacts.”

(As an aside, I love the advice about resilience. Technology is changing nearly faster than we can keep up, true, but the best advice is to keep focused on the core service experience, yet with an eye to “the art of the possible”)

To address the salary gap, Tony Simons suggests the following “These trends will manifest in increasing pressure to raise salaries at the lower end of the wage scale. If we, as an industry, are to pay our workers more, then we must seek ways to enhance service and productivity to offset increasing costs. In my opinion, the highest-leverage point at which to engage that mission is leadership practice, from CEOs all the way down to supervisors. It is well established that workers do more, better, are more creative, and are more generous to guests when they are engaged, when they care about the company, and when they feel like the company cares about them. “

Jack Corgel says hotel management needs to be prepared for the worst at two levels – an attack on the hotel property and an attack in the city market. Hoteliers will need to increase expenditures on security, increase cash reserves, update property emergency and shut down plans.

Dave Sherwyn feels the industry needs to address the issue of the franchisor as joint employer on all fronts: legally, lobbying, and operationalizing. “In other words, the industry needs either stop this progression or figure out what franchising will look like under the industrial realities test. Will franchisors accept employment liability, exercise more control, and incur more costs? If so, will they be able to pass the costs on the franchisees or the consumer? If not, will franchisors give up brand control? If so, what will that do the brand? Franchisors need to thinking of this now, even though it could be years before the issues is resolved.”

What research projects are you working on in 2015?

  • Tony Simon: I am studying how the trust and credibility processes differ across cultures and countries. How leaders who are seen to consistently deliver on their word increase employee engagement through trust and also through improved communication of standards. How the effective implementation of ethical leadership initiatives, diversity initiatives, and other leadership approaches often depends on the personal credibility of the leader as a critical make-or-break factor. To better understand how to develop these trust- and credibility-based leadership skills, I am engaged in a six-year project to help train, coach, and document the leadership skills and their effects at the police department, fire department, and government bureaucracies in five English cities.
  • Jamie Perry, Assistant Professor and Rachel Etess Green '98 and Jason Green Faculty Fellow, Human Resources: Currently, I am working on two projects that will benefit the hospitality industry. The first project is a meta-analytic review of 40 years of research on diversity training. In this project, my colleagues and I examine the effects of diversity training over time and across characteristics of training context, design, and participants. The second project is a field study of healthcare teams that examines how social differences (e.g. power and status) impact the teams’ cognitive, motivational, and affective states, and performance outcomes. Both of these research projects are important to the hospitality industry because current workforces are composed of individuals with diverse abilities, cultural backgrounds, and work styles that may impact how work is performed. By understanding and leveraging the differences, organizations can not only reap the performance benefits of diversity, but will also be able to retain top talent.
  • Rohit Verma, Singapore Tourism Board Distinguished Professor in Asian Hospitality Management, Service Operations Management, is working on two projects: Text Analytics: Leveraging On-Line Text Reviews in Managing Hotel Operations and Understanding Technology-Based Innovations in Upscale and Luxury Hotels
  • Jack Corgel is working on a project called: Sell Hotels Now – How to Decide!

From all reports, the industry will continue to move fast, with pressures from all sides both internally and externally. It seems that it is becoming more important than ever to have the right data and analytics strategy in place to anticipate these trends and identify opportunities. Be assured that we at the Analytic Hospitality Executive will be here to help in 2015!!

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