The Profitable Path to Personalization for Hotels

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Machine Learning

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

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

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

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

Text Analytics

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

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

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

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

Digital intelligence

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

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

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

For example:

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

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

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

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

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

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

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

Pricing and Revenue Management

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

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

Marketing and Customer Loyalty

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

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

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


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

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

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

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

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

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

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

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

Analytics Explained

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

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

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

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

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

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

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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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