Advanced Analytics in Action
7. Where do you think this area is headed over the next few years? What opportunities are on the horizon?
As previously mentioned, there is going to be a clear evolution to focus on overall guest satisfaction improvement rather than just measurement.
Travelers are becoming increasingly savvy and demanding. The challenge will be for hotels to keep ahead of the curve by effectively leveraging guest intelligence to deliver remarkable experiences and drive higher guest satisfaction.
Decisions that impact guests are often made at the head office, but the guest satisfaction battle is won or lost on the property. It’s essential that the right people within the organization are getting the right information at the right time. New mobile apps, conceived for on-property use by hoteliers on the go, make it easier to manage guest feedback from anywhere and receive alerts in real time so hotel staff can act quickly to ensure that guests leave the hotel happy.
It is critical for hotel organizations to train, support, and engage their staff. It’s all about creating a cultural shift to evolve everyone in the organization to become more guest-centric. Brands that take advantage of technological advances to be more efficient and share information more (continued )
effectively will make it possible for hotel staff to free up more time to spend doing things that have a positive impact on guests.
There is also a clear opportunity for properties to exploit big data to outperform competitors by implementing the right strategy. Data becomes powerful when the analytics combine insight from a variety of sources and systems. It is critical that guest intelligence platforms are flexible and open and that they can integrate seamlessly with other systems and technologies. For example, hoteliers should demand that their guest intelligence data be able to integrate into their revenue management and customer relationship management (CRM) system to leverage the full potential of guest feedback in both optimizing pricing and building rich guest profiles.
Guest profile information gathered from satisfaction surveys is incredibly rich and valuable for CRM activities. Sophisitcated hotels will synchronize guest survey data with their CRM systems to be able to personalize promotional offers and the guest experience on the property.
MAking it WoRk—AnAlytics AnD technology
Analytics and technology go hand in hand. Obviously, most of the analytical methods I just described are designed to solve problems that are too complex and too large to calculate by hand. (Anyone who has learned to calculate a regression model by hand understands this fact intimately—if you haven’t had the pleasure of transform- ing X’Y matrixes by hand, consider yourself fortunate). The larger the data sets, the more complex the analytics, and the more processing power is required. Along with the technology innovations I described in Chapter 2, which allow for faster, more flexible, and more scal- able data storage, have come advances in the execution and delivery of analytics, making it possible to turn all of that complex data into meaningful, actionable insights. In this section, I describe innovations in the execution of analytics that provide better, more detailed an- swers faster, as well as innovations in solution delivery that make analytics more accessible for businesses. Finally, I close the chapter by describing the next frontier of analytics, streaming data, and real-time analytics.
innovations in executing Analytics
As I alluded to several times in previous discussions, emerging al- gorithms designed to handle big data will require increasingly large amounts of processing power to execute at the speed of business. Think of the definition of big data from Chapter 2: “exceeds an organization’s storage or compute capacity.” Big data is of no use unless you can turn it into insight. For that you need big analytics. Every piece of the analytics continuum. from reporting to advanced analytics, has been impacted by big data. In reporting, billions of rows of data need to be quickly rendered into a report form, and users want the flexibility to slice and dice, and drill down into the data according to their specific needs. Advanced analytics like forecasting and optimization require complex math executed by multiple passes through massive data sets.
Without changes to the technology infrastructure, analytic pro- cesses on big data sets will start to take longer and longer to execute—
even if you can capture and store that big data. Businesses today can’t be run by pushing the button and then waiting hours or days for an answer. Today’s advanced analytics need to be fast and they need to be accessible. This means more changes to the technology infrastructure.
Analytics companies like SAS have been developing new meth- ods for executing analytics more quickly. The following is a high-level description of some of these new methodologies, including why they provide an advantage. Once again, the intention is to provide enough detail to start conversations with IT counterparts (or understand what they are talking about), certainly not to become an expert. There is a ton of information out there if you want more detail.
1. Grid computing and parallel processing. Calculations are split across multiple central processing units (CPUs) to solve a bunch of smaller problems in parallel, as opposed to one big problem in sequence. Think about the difference between having to count an entire jar of M&Ms yourself as opposed to splitting the jar into 10 stacks and asking 10 of your friends to count and report back. To accomplish this, multiple CPUs are tied together, so the algorithms can access the resources of the entire bank of CPUs.
2. In-database processing. Most analytics programs lift data sets out of the database, execute the “math,” and then dump the data sets back in the database. The larger the data sets, the more time-consuming it is to move them around. In-database analyt- ics bring the math to the data. The analytics run in the database with the data, reducing the amount of time-consuming data movement.
3. In-memory processing. This capability is a bit harder to understand for nontechnical people, but it provides a crucial advantage for both reporting and analytics. Large sets of data are typically stored on the hard drive of a computer, which is the physical disk inside the computer (or server). It takes time to read the data off the physical disk space, and every pass through the data adds additional time (note that the math behind the analytics requires multiple passes through data). It is much faster to conduct analysis and build reports from the computer’s memory. Memory is becoming cheaper every day, so it is now possible to add enough memory to hold large data sets “in-memory,” speeding up the calculation of answers or rendering of reports.
To give you an idea of the scale of the impact, SAS applied these methodologies to render a summary report (with drill-down capabil- ity) from a billion rows of data in seconds. Large-scale optimizations, like risk calculations for major banks or price optimization for thou- sands of retail products across hundreds of stores, have gone from hours or days to minutes and seconds to calculate. As you can tell, the advantages are tremendous. Organizations can now run analytics on their entire data set, rather than a sample. It is possible to run more analyses more frequently, testing scenarios and refining results.
innovAtions in solution DeliveRy
Solution providers are innovating the way that technology is delivered as well, and this has an impact on solution decisions that hospitality and gaming companies make. While we are talking about technology innovations, it is worth defining available solution delivery options in order to understand the advantages and disadvantages of each.
Hoteliers are likely hearing a lot about solutions delivered via the cloud versus requiring the technology to be managed on-site or on-premise.
Cloud-based solutions are accessed via the Internet, the applications are hosted in a remote site, and the technology environment is managed by the vendor. On-premise solutions, on the other hand, require the company to invest in the hardware and IT resources to manage the solution and the technology environment themselves.
At a high level, there are two different types of cloud solutions:
1. Software-as-a-Service (SaaS). These cloud-based solutions are complete end-to-end software packages that are standard- ized for the entire user base. While they can be configured, generally, the software is the same for all companies that access it. The application sits on a layer above the individual users’
databases. Security is maintained between the users’ databases, but all databases access the same application layer. Salesforce .com is probably the best-known example of a SaaS applica- tion, but many hospitality solutions today are delivered SaaS (Avero’s Slingshot™ application and IDeaS Revenue Solutions suite of products are well-known examples).
2. Hosted. A hosted, or managed, solution is a proprietary solu- tion for each customer that is effectively the same as having an on-premise solution, except it is located in a remote data center and managed by the vendor. This enables a more proprietary, customer-by-customer application delivery, but without the organization having to own and manage the technology infra- structure or resources.
Four advantages of a cloud solution over an on-premise solution are:
1. Reduced burden on IT. The vendor assumes all responsibility for maintaining the environment, including regular mainte- nance, upgrades, data transfers, or managing memory and storage capacity.
2. Guaranteed service levels. Cloud providers guarantee a minimum up-time for the solution, and build in the mechanisms required to achieve this. You have the security of knowing the system will be available when you need it.
3. Implementation speed. Particularly for SaaS applications, the time to implementation is dramatically reduced for cloud solutions.
4. Security. Most cloud providers have extensive data encryp- tion and cybersecurity measures in place. In fact, some finan- cial services companies use hosted solutions to manage credit card fraud activity, and are able to meet all of their both inter- nal and external regulatory security measures to ensure that credit card numbers are not stolen, and the consumer’s privacy is respected.
Three advantages of an on-premise solution over a cloud solution are:
1. Control. An on-premise solution is 100% in the control of the company that uses it. The data flow in and out is controlled by the company, as is the system configuration. Many companies see their data as a strategic asset, and do not want it to be man- aged by a third party.
2. Regulatory issues. Some industries’ data is highly regulated (casinos, for example), and these regulations prohibit some data from ever leaving the facility where it is created. While the data centers for cloud solutions may have every protection mechanism in place, these companies are simply not allowed to leverage them. This is particularly an issue for customer data.
3. Cost. While the up-front cost of many on-premise solutions may be higher, the long-term costs can be significantly less. For hosted solutions, you can pay all of the up-front hardware and software costs plus an ongoing maintenance fee for the solution.
SaaS solutions are frequently subscription-based, which means they charge a regular fee while the software is being used. Over the life of the solution, these can be significantly more than up-front software costs for an on-premise solution.
Here are some of the differences between pure SaaS and a hosted solution:
■ The time-to-value of SaaS solutions, or any out-of-the-box solu- tion, can be significantly shorter than any proprietary option, even including custom-built hosted solutions. The implementation
process is standardized, so the solution can be up and running much faster. There generally are standardized training materials available, as well as extensive support mechanisms for users at every stage of the life cycle.
■ SaaS solutions are multitenant, meaning that all users share a common application layer (although each company’s data is kept separate). This means that upgrades happen simultane- ously and automatically for all users. They are included in the monthly subscription fee, so do not require additional services fees. This means you are always using the latest version of the software package.
■ SaaS applications are standardized to an industry or an applica- tion, so they do not offer a lot of room to customize to meet unique or highly specific customer requirements. Some com- panies have unique requirements that would make a propri- etary solution highly valuable (for example, a unique operating constraint like the capacity of a kids’ club or a minimum oc- cupancy requirement driven by other on-site services). These unique requirements are difficult to accommodate in an SaaS application, and if they are incorporated, they become generally available to any user of the application. A proprietary hosted (or proprietary on-premise) solution could incorporate any unique business processes or operating constraints, turning the solution itself, not just the application of the results, into a competitive advantage.
■ SaaS applications include specific user interfaces and work- flows. There is some flexibility, but in general, users must be trained on and conform to the methodology as laid out by the application. When a company decides to use one of these ap- plications, the change management can be extensive, and the opportunity for company-specific business processes is either reduced or must be developed within the context of the appli- cation. For a company brand new to such a system, this can be an advantage, as business processes are baked into the solution implementation, so there is no need to invent new ones. For a more mature company that desires to upgrade their capabilities, this can be burdensome and restrictive.
Case study: advaNtage oF PRoPRIetaRy systems, wyNdham destINatIoN NetwoRk
In my group at Wyndham Destination Network, all of our analytical systems and solutions are built internally, and we have invested in the people and the technology to do that. This is an important strategic decision for us. The main driver for this decision was that the pricing problems in vacation rental and time-share exchange were different enough from “traditional” revenue management problems that no commercially available solution fit. Once the company invested in the resources to build the pricing solutions, it was natural to keep innovating internally on any new problems that come along, rather than working with vendors.
This creates several advantages for us, particularly in such a complex environment. First, all of the solutions are purpose built for the specific problem. While we can, and do, use what we learned from previous projects, we can also adapt to the specific operating conditions of the business unit we are working with. Second, our development cycles are astoundingly fast (from a former vendor’s perspective), because we only have to solve that exact client’s problem, not make the solution flexible enough to handle a multitude of different flavors of the problem. We can add value very quickly, which makes our stakeholders more willing to work with us on current and future projects.
“Given the option, I prefer to build solutions internally,” said Jeremy TerBush, Senior Vice President of Global Analytics, Wyndham Destination Network. “We know our business better than anyone else, and we know what will drive value. That level of control over the development gives us the flexibility to design the right solution for that very specific scenario, and to be very nimble as our understanding of the problem improves.
It also provides our team the most interesting work and helps them continue to grow within the business. Anything from building a report to automating a process to building an entire pricing system is core to our analytical strategy and has been instrumental in our success. This required investment in people and technology, and a commitment from senior leadership to continue to support that investment, and it has more than paid off for us.”
C ASE S Tu D y
ReAl tiMe AnD stReAMing
The move to digital has given hospitality companies a platform to in- teract with guests in real time, or near real time, as guests are browsing the web or using an app. This same technology facilitates personal- ized recommendations in context during face-to-face interactions.
Real-time decisioning is now being used across hospitality and travel.
Many interactions are based on business rules, such as “if the guest clicks here, show them this banner ad.” The competitive advantage will come when analytics are infused into the real time interactions, to predict the next best offer that will cause the guest to take the action you desire in that moment. Real-time engines typically work by understanding the context of the interaction, and looking up a score or analytic result that is matched to an offer or communication sent back to the guest in the moment. The action that the guest takes as a result of the interaction is recorded in the database, so that future actions are improved. These real-time interactions can take place through the website, kiosks, mobile apps, or even face to face at the front desk or concierge—any place where the guest can be identified through a device that is integrated with the real-time decision engine.
The next big innovation in real-time analytics is streaming analyt- ics, or analytics on the edge. In this application, the data is analyzed before it even hits the database. It is analyzed, in fact, as it is being created. This dramatically speeds up time to results. You’ve probably heard the phrase “the Internet of Things.” This is the concept that de- vices are connected to the Internet and are generating volumes of data about their condition and the environment around them. Think about manufacturing lines that have interconnected machines with parts that wear out, or a car that is a giant system of interrelated compo- nents that need to sense not only their operation, but the conditions of the road and the driver. In hospitality, we will soon have room service trays that can tell us when they are set outside the door, or lighting and HVAC systems that capture guest preferences and provide opportunities to improve energy efficiency, even faucets that can tell us when they are leaking. All of these individual components are cre- ating and reporting data.
Analytics that can watch this data as it streams by and instantly make decisions will be the difference between detecting a pump failure before it happens and the next oil spill. These types of analytics are just emerging, and are best suited for very large, very fast-moving data sets like machine sensors, website tracking, or data usage for mobile devices.
You will hear a lot more about this issue as the Internet of Things goes mainstream, so it is important to be aware of the opportunity. I speak more about this in subsequent chapters.
conclusion
The term “analytics” is used to describe a wide variety of techniques ranging from traditional business intelligence applications to advanced predictive modeling and optimization. While an analytic hospitality executive, or an aspiring analytic hospitality executive, does not neces- sarily have to be able to develop and execute these modeling techniques, they do need to work closely with the people who do. Therefore, it is very important to have a basic understanding of what they are and how they can add value to your business.
In this chapter, I talked about some common advanced analytic techniques, including statistical modeling, forecasting, predictive mod- eling, data mining, and optimization. I also talked about innovations in the execution of analytics that make it possible to execute these complex techniques on larger and larger data sets. You should be pre- pared to have better conversations with analysts and IT to help guide an analytics strategy for your organization. In the rest of the book, I’ll go functional area by functional area in the hospitality and gaming enterprise to describe how analytics are commonly and best applied within these areas. In combination with the overview I provided here, you should gain from the following chapters a good understanding of where the potential exists in your organization to leverage these techniques, and how to make the right investment in data, analytics, technology, and people.
ADDitionAl ResouRces
■ Thomas H. Davenport, Jeanne G. Harris, and Robert Morrison, Analytics at Work:
Smarter Decisions, Better Results (Boston: Harvard Business Review Press, 2007).