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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Serie: SAS Institute Inc
- Sprache: Englisch
Targeted analytics to address the unique opportunities in hospitality and gaming The Analytic Hospitality Executive helps decision makers understand big data and how it can drive value in the industry. Written by a leading business analytics expert who specializes in hospitality and travel, this book draws a direct link between big data and hospitality, and shows you how to incorporate analytics into your strategic management initiative. You'll learn which data types are critical, how to identify productive data sources, and how to integrate analytics into multiple business processes to create an overall analytic culture that turns information into insight. The discussion includes the tools and tips that help make it happen, and points you toward the specific places in your business that could benefit from advanced analytics. The hospitality and gaming industry has unique needs and opportunities, and this book's targeted guidance provides a roadmap to big data benefits. Like most industries, the hospitality and gaming industry is experiencing a rapid increase in data volume, variety, and velocity. This book shows you how to corral this growing current, and channel it into productive avenues that drive better business. * Understand big data and analytics * Incorporate analytics into existing business processes * Identify the most valuable data sources * Create a strategic analytic culture that drives value Although the industry is just beginning to recognize the value of big data, it's important to get up to speed quickly or risk losing out on benefits that could drive business to greater heights. The Analytic Hospitality Executive provides a targeted game plan from an expert on the inside, so you can start making your data work for you.
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Veröffentlichungsjahr: 2016
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The Analytic Hospitality Executive
Implementing Data Analytics in Hotels and Casinos
Kelly A. McGuire, PhD
Cover image: © Devaev Dmitriy/iStock.com
Cover design: Wiley
Copyright © 2017 by SAS Institute, Inc. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
Portions of this book have appeared in the author's previous book, Hotel Pricing in a Social World: Driving Value in the Digital Economy.
No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions.
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Library of Congress Cataloging-in-Publication Data:
Names: McGuire, Kelly Ann, author. Title: The analytic hospitality executive : implementing data analytics in hotels and casinos / Kelly A. McGuire, PhD. Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2017] | Series: Wiley and SAS business series | Includes bibliographical references and index. Identifiers: LCCN 2016024813 (print) | LCCN 2016026828 (ebook) | ISBN 978-1-119-12998-1 (hardback) | ISBN 978-1-119-22493-8 (ePDF) | ISBN 978-1-119-22492-1 (ePub) | ISBN 978-1-119-16230-8 (oBook) Subjects: LCSH: Hospitality industry—Management—Decision making. | Hospitality industry—Statistical methods. | Big data. | BISAC: BUSINESS & ECONOMICS / Industries / Hospitality, Travel & Tourism. Classification: LCC TX911.3.M27 M36 2017 (print) | LCC TX911.3.M27 (ebook) | DDC 647.94068—dc23 LC record available at https://lccn.loc.gov/2016024813
To my favorite analytic hospitality executives
CONTENTS
Foreword
Acknowledgments
About the Author
Chapter 1 Building a Strategic Analytic Culture in Hospitality and Gaming
Strategic Analytic Culture
Moving Ahead and Staying Ahead with Prescriptive Decision Making
Making It Happen
Getting Started
How This Book Can Help
Notes
Chapter 2 Data Management for Hospitality and Gaming
Data Management Challenge and Opportunity
Data Storage
Data Integration
Data Quality
Measuring the Benefits of Data Management
Responsible Use of Data
Conclusion
Additional Resources
Notes
Chapter 3 Data Visualization
Why Are Visualizations So Important?
Visualization Technology
Data Handling
Visualization Types
Creating Powerful Visualizations
Conclusion
Additional Resources
Notes
Chapter 4 From Reactive to Proactive Decision Making: Advanced Analytics in Action
Reactive to Proactive Decision Making
Statistical Analysis
Forecasting
Predictive Analytics
Optimization
Machine Learning
Text Analytics
Making It Work—Analytics and Technology
Innovations in Solution Delivery
Real Time and Streaming
Conclusion
Additional Resources
Notes
Chapter 5 Analytics for Operations
Operations
Operations Data
Advanced Analytics for Operations
Workforce Planning and Optimization
Queues
The Impact of Queue Configuration
Managing Consumer Perceptions of the Wait
Benchmarking Operations Analytics Capabilities
Technology and People Investments
Conclusion
Additional Resources
Notes
Chapter 6 Analytics for Marketing
Marketing Data
Advanced Analytics for Marketing
Digital Intelligence
Benchmarking Marketing Analytics Capabilities
Technology and People Investments
Conclusion
Additional Resources
Notes
Chapter 7 Analytics for Sales
Sales Data
Advanced Analytics for Sales
Statistical Analysis
The Changing Landscape of Sales
Benchmarking Sales Analytics
Conclusion
Note
Chapter 8 Analytics for Revenue Management
Revenue Management: A History Lesson
Then Things Changed . . .
Revenue Management Data
Revenue Management Analytics
Benchmarking Revenue Management Analytics Capabilities
Technology and People Investments
Conclusion
Additional Resources
Notes
Chapter 9 Analytics for Performance Analysis
Data for Performance Analysis
Advanced Analytics for Performance Analysis
Benchmarking Performance Analytics Capabilities
Technology and People Investments
Conclusion
Additional Resources
Notes
Chapter 10 Analytics for Gaming
Gaming Data
Advanced Analytics for Gaming
Casino Floor Revenue Optimization
Fraud and Anti–Money Laundering
Benchmarking Gaming Analytics Capabilities
Technology and People Investments
Conclusion
Additional Resources
Notes
Chapter 11 Pulling It All Together: Building an Analytical Organization
Getting Started: Well-Defined, Small Projects for Maximum Impact
Organizing Your Analytics Department
The Build versus Buy Decision
Integrated Decision Making
Conclusion
Additional Resources
Notes
Appendix 1 Case Study from Infor: Analytics Opportunities in Operations
Appendix 2 Case Study from IDeaS: Meetings and Events Revenue Management
Introduction
Evaluating Groups
Metrics
Other Factors Influencing Success
Summary
Appendix 3 Why Dynamic?
Appendix 4 Chapter Questions
References
Index
EULA
List of Tables
Chapter 5
Table 5.1
Chapter 6
Table 6.1
Chapter 8
Table 8.1
Chapter 10
Table 10.1
List of Illustrations
Chapter 1
Figure 1.1
Strategic Analytic Culture Framework
Figure 1.2
A Phased Approach
Chapter 2
Figure 2.1
Data Management Challenge and Opportunity
Figure 2.2
Data Management Framework
Figure 2.3
Structured Data
Figure 2.4
Text Reviews—How Do You Structure These?
Figure 2.5
Benefits of Data Management
Chapter 3
Figure 3.1
Simple, But Compelling, Visualizations
Figure 3.2
Grouped Bar Chart
Figure 3.3
Stacked Bar Chart
Figure 3.4
Histogram
Figure 3.5
Line Chart
Figure 3.6
Pie Chart
Figure 3.7
Scatter Plot
Figure 3.8
“Heatmap incito” by I, Colinmcfarlane
Figure 3.9
Sankey Diagram
Figure 3.10
Traditional Pie Chart
Figure 3.11
Stacked Bar Chart
Figure 3.12
Flashy Effects
Figure 3.13
Simple Bar Chart for Clearer Relationships
Figure 3.14
Demand Management for Hotels
Figure 3.15
Technology and Distribution Landscape Ideal
Figure 3.16
Technology and Distribution Landscape Actual—Hotel Picasso
Chapter 4
Figure 4.1
Analytics Continuum from
Competing on Analytics
Figure 4.2
Regression Output Here height is used to predict weight. You can see from the chart on the top, the
p
-value (<.0001) of height as a predictor variable is significant. The chart shows the actual observations of height and weight (circles), and the line is the set of weight predictions that the model would make at each height value.
Figure 4.3
Forecasting Output Circles are the actual observations; the line is the forecasted value, predicted into the next four years.
Figure 4.4
Predictive Modeling Output This is the incremental impact of a marketing treatment. The two shadings represent the control and predicted impact of the treatment, respectively.
Figure 4.5
Optimization Output The optimization recommends the right staffing levels and indicates where there will be staffing shortages, which will cause waits. To set the constraints, analysts enter the hourly wage for employees and how many hours they can work. The model minimizes expense.
Figure 4.6
Text Mining Output Shown here in black and white, the bars and text would in actual use have sentiment indicated as neutral (blue), positive (green), and negative (red).
Chapter 5
Figure 5.1
Operations Must Balance Cost and Guest Experience
Figure 5.2
Stages of the service processWhen you plan to conduct a time and motion study, generally the first step is to map out the stages of the service process that you want to time, such as those shown in this diagram. It is useful to note which are in control of the operation (light gray) and which are in control of the guest (dark gray).
Figure 5.3
Labor Scheduling Process
Figure 5.4
The Ubiquitous Waiting Line
Figure 5.5
Multiline, Multiserver System
Figure 5.6
Single-Line Multiserver System
Figure 5.7
Take a Number, or Virtual Queue
Chapter 6
Figure 6.1
Is loyalty the holy grail for hotels and casinos, or is it an expensive “me too”?
Figure 6.2
Identifying Opportunities to Use Social Data
Figure 6.3
Summarized Clickstream Data for Tracking Website and Campaign performance It is useful to understand aggregated activities on the website, but it cannot be disaggregated to track individual customer actions.
Figure 6.4
Click Data at the Customer Level This table becomes short and wide instead of tall and thin, but stores detailed activity associated with an individual instead of an activity.
Figure 6.5
The Roadmap to Guest-Centric Marketing for Hotels and Casinos
Chapter 8
Figure 8.1
Evolution of Airline Yielding
Figure 8.2
Airline Fare Screen
Figure 8.3
Evolving Scope of Revenue Management Activities
Chapter 9
Figure 9.1
STR Reporting Example
Chapter 10
Figure 10.1
Increasing Casino Floor Revenue
Figure 10.2
Performance Decline
Figure 10.3
Return to Regular Performance Levels
Chapter 11
Figure 11.1
Typical Guest Journey
Figure 11.2
Everyone Has a Role in Personalization
Figure 11.3
Phased Approach
Appendix 3
Figure A.1
Seasonal Demand
Figure A.2
Low-Demand Periods
Figure A.3
Forecasting an Increase in Demand
Figure A.4
Coming Back from a Decline in Performance
Figure A.5
Should We Get Rid of This Game?
Figure A.6
And Keep This One?
Figure A.7
The Impact of Moving a Top Performer
Figure A.8
And Then Moving the Top Performer Back
Figure A.9
Impact of Change in Multipliers
Figure A.10
Adding a Mystery Progressive
Figure A.11
Change Didn’t Help This Game
Figure A.12
Change Didn’t Help This Game
Figure A.13
A Sign Post for Analytics
Guide
Cover
Table of Contents
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Foreword
Data, it has often been claimed over the past several years, is the new oil. I’m not convinced this is entirely true, but there are some curious similarities. Just as oil slumbered as an unappreciated resource until the late nineteenth century and then awakened wholesale changes to the world economy, data in many ways has the potential to do the same. But in contrast to oil that sat beneath the earth for thousands of years relatively undetected, data is flooding all around us in seemingly unmanageable variety and volume. Data is everywhere, but perplexingly the more we have of it, the more it becomes increasingly difficult to harness and exploit.
This is particularly true in the hospitality industry where our culture has been historically high touch and low tech. Yet, every hour of every day hotels, restaurants, and casinos generate millions of data points as customers interact with reservation systems, loyalty programs, credit card exchanges, point of sale systems, and simply check in and out of hotels. Although the traditional success of most hospitality companies has largely been due to their ability to use customer service, facilities, and location as differentiators, this is no longer enough.
Today, our service-driven industry has become extremely competitive in almost every way conceivable. For small and large companies alike, there has never been a time with more focus on performance—financial performance, stock price, customer loyalty, market share, you name it. The competitive landscape has quickly transitioned to finding a way to best use data to drive strategy and performance.
As a hospitality industry executive and consultant for almost 30 years, I have witnessed this transition firsthand and I can appreciate what a challenging journey it has been and continues to be for many of us. Although I have enjoyed some success over the years helping to drive the adoption of data-driven decision making and performance enhancement during my time with Pricewaterhousecoopers, Host Hotels & Resorts, and now Hilton Worldwide, I really wish Kelly McGuire could have helped me out and published The Analytic Hospitality Executive 25 years earlier. As you will soon realize when reading her book, Kelly serves up a brilliant recipe for understanding all of the key principals in a readable and business-faced format. If you are thinking about becoming a better analytic hospitality executive, then this is your guidebook.
I first met Kelly when I joined the advisory board for the Center for Hospitality Research at Cornell University’s School of Hotel Administration. We are both alumni; she with a master’s degree and a PhD, and I with an undergrad degree many years earlier. What impressed me about Kelly when we first met was that I immediately recognized her as an “hotelier.” Not solely an academic mind, she had that rare combination of technical intelligence matched with a keen appreciation for the business of hospitality. It’s actually easy to see how she came up with The Analytic Hospitality Executive because that is who she is. I know her to be an analytics evangelist who is passionate about helping the hospitality and travel industries realize the value of data-driven decision making.
In this book, Kelly McGuire masterfully articulates the keys for successfully building a strategic analytical culture in your hospitality organization. She will emphasize the absolute necessity for senior executive–level buy-in and support. Additionally, she will stress the need for an organizational commitment to fact-based decision making and the allocation of the right business resources. Not just dollars allocated to technology, but the dedication of the business to transition to an actionable data-driven decision-making process. The days of devoting 80 to 90 percent of resources to data collection and validation need to come to an end.
There is no message that resonates more strongly from Kelly’s book than that it’s all about the data. If you learn nothing else from this book and the real-life stories depicted within, please take one word of advice from those of us who have walked the path. Start with the data.
As Kelly explains in this book, data is often not the sexy part of analytics. The potentially rich data trapped in fragmented legacy systems like those prevalent in the hospitality industry are plagued with challenges. The possible solutions often lack clear ownership and funding as other priorities jump to the front of the line. In my view, this is always shortsighted as getting the data right is perhaps the most important building block for success.
Much like my golf game, it’s always more appealing to find a shortcut. Hard work and practice are no fun for most of us. Every year there is new driver technology that promises to let us all hit it right down the middle and 50 yards further. Why take lessons and practice when you can just buy new technology? Of course that strategy continues to disappoint in lowering my handicap.
Similarly, many executives are often too eager to embrace the popular new technology and the vernacular of the day. Lately, big data seems to be the magic term that gets everyone excited. As Kelly will explain, today’s big data is tomorrow’s small data. It’s not just science; there is a lot of art as well. Being too quick to buy a shortcut solution and rush to fancy dashboards without focusing on the underlying data and organizational alignment almost always lead to failure.
In my experience, and as Kelly describes in this book, data is the key to the successful creation of a strategic analytical culture. It’s the business taking ownership and demanding a “single source of truth.” It’s the commitment to establishing a common business language and what Kelly describes as a sound and sustainable data management strategy.
In this amazing book, Kelly McGuire will provide a tool kit to help all of us navigate the path to a strategic analytical culture in our organizations. She understands the challenges hospitality companies are facing in these highly competitive times. Strategically leveraging data has never been more important. We all need to be better analytic hospitality executives. In that regard, this book is essential.
Dexter E. Wood, Jr.
SVP, Global Head, Business & Investment Analysis
Hilton Worldwide
#hotelieforlife
Acknowledgments
The experience of writing this book was very different from writing the first one. Of course, changing jobs and moving right in the middle of the process definitely influenced that. Having this project did add a little bit of stress, but it also helped me through the transition. It was a constant that reminded me of my passions and interests, as I was figuring out what to do with my extra furniture or trying to find a new dry cleaner. Of course, now that it’s ready to be published, I suddenly have fewer excuses for not unpacking those last few boxes. . . . As with my first effort, the best thing about the process was that it gave me an excuse to reach out and reconnect with people who inspire me, and who I so very much enjoy speaking with. There is a fantastic community of dedicated analytic hospitality executives out there, and I am humbled and privileged to be a part of it.
I must start out by once again thanking the team at SAS that helped me through this book so soon after the first. My development editor, Brenna Leath, and my marketing support, Cindy Puryear, in particular, have made this process both easy and fun. Thanks for being responsive, even after I left the fold.
I also want to thank my previous boss, Tom Roehm, for pushing me to do this. One book wasn’t going to be enough; I had to write two to prove, actually, I can’t really remember what I was trying to prove . . . but I’m glad I felt I had to. I must thank my new boss, Jeremy TerBush, for being open to letting me see this through, for his genuine excitement about the project, and for how much fun we have had and will continue to have making a difference for the business, for our stakeholders, and for the careers of the individuals on our team. I have admired Jeremy’s dedication, leadership, and achievements from afar for many years. It is an honor to be a part of his team.
A special thanks goes to Dexter Wood, for sharing his experience and his perspective through this process. Conversations with Dex inspired a lot of the thinking that went into the book. He pushed me to challenge the material and myself, and it is much appreciated. Thank you for authoring the foreword and the case study in Chapter 2, but more important for believing in the value of data analytics, for believing in this project, and for believing in me. And speaking of Big Red Analytic Hospitality Executives, I very much appreciate the genuine enthusiasm and passion that Dave Roberts has for analytics and for revenue management. He has been a great inspiration and a great advocate. Thank you, Dave, for your tireless pursuit of the importance of analytics in hospitality! I also appreciate the support and inspiration from Ted Teng, a consummate #hotelieforlife, whose dedication to advancing the industry and the people in it has been an inspiration to us all.
Many people generously gave their time to this project, and it is much appreciated. My partner in crime, Kristin Rohlfs; my other partner in crime, Natalie Osborn; and Alex Dietz, Anne Buff, and Analise Polsky lent me their expertise as technical editors, and the book is much better for it. Dave Roberts, Jeremy TerBush, David Koch, Bernard Ellis, David Turnbull, R. J. Friedlander, Natalie Osborn, Paul van Meerendonk, Kate Kiesling, Fanie Swanepoel, and Andy Swenson took time from their very busy schedules to lend their expertise to lengthy case studies. Michael Smith and Kate Keisling took a panicked phone call at short notice when I realized I was out of my depth. I also very much appreciate the inspiration provided by the analytic hospitality executives who let me quote them, learn from them, and be inspired by them.
Speaking of analytic hospitality executives, two more of my favorites should be personally recognized for their support of me and my efforts. Thank you, Mark Lomanno and Tom Buoy, for sharing your critical and thoughtful perspectives with me and letting me run with them, for your passion for the industry, and for the time you have spent making me and others better at what we do. I also appreciate the encouragement and advice from Gary Cokins, another prolific SAS author, and from Michele Sarkisian, whose passion for all things hospitality is both remarkable and contagious.
I highly value my relationship with the global team at HSMAI, who have been great advocates for education and the advancement of the hospitality industry, and great supporters of me as well. I must thank Juli Jones in particular, who works so hard and is so good at keeping the community together, and of course, Bob Gilbert, who is such a great advocate for our industry.
I am fortunate to have good friends and family who have been with me through this process: In particular, Alex Failmezger and Adam Sternberg, for providing moral support and feedback even through their job changes. My brother, Sean, who told me that my first book “was not a terrible read.” And, of course, my parents, who have supported me through every crazy decision that got me to this point. If anyone is looking for a nontraditional hospitality analytics candidate, my mother is now quite well read and, I think, available—if you offer the right travel benefits.
I learned so much while I was at SAS. This book would not be what it is without that experience. I miss my colleagues and teammates very much. I thought of you often as I was finishing this book. I also want to thank my new team at Wyndham for being so welcoming, so much fun, and, well, so just plain excellent at what you do! Every analytic hospitality executive should be so lucky to have a team like you!
I was extremely humbled by the response to my first book. It is an honor to be a part of this community and to contribute to moving it forward. It has been such a pleasure to present the original research that Breffni Noone and I have worked on to the community and talk through those complicated issues with you. It has been a joy to hear your reactions to the blog that I coauthored with Natalie Osborn, and it has been just genuine fun to stand up in front of you to challenge our thinking and try to make us better. The biggest thank you goes to all of you who have read my work, shared it with your colleagues, assigned it to your students, and talked to me about it. Keep up the great work. We will get there, together.
About the Author
Kelly A. McGuire, PhD is senior vice president, revenue management and direct marketing for MGM Resorts International, where she is responsible for driving profitable room revenue for MGM Resorts International’s Las Vegas resorts. Prior to this role, she was Vice President, Advanced Analytics for Wyndham Destination Network. She led a team of data scientists and developers who build custom analytic solutions for Wyndham Vacation Rental’s companies and the RCI time-share exchange. Prior to joining Wyndham, she led SAS’s Hospitality and Travel Global Practice, setting the global analytics strategy for these industries, and supporting engagements around the world.
CHAPTER 1Building a Strategic Analytic Culture in Hospitality and Gaming
I believe in intuitions and inspirations. . . . I sometimes feel that I am right. I do not know that I am.
—Albert Einstein
Hospitality executives struggle to find the balance between delivering a guest experience that fosters loyalty and repeat business, and delivering on their revenue and profit responsibilities to stakeholders, shareholders, or franchisees. If you invest too much in the guest experience, you could impact profits, but if you focus on too many cost-cutting measures to drive profits, you can negatively impact the guest experience.
Decisions made in one department of a hotel can have impacts across the organization. For example, without a good understanding of food cost, a marketing program providing restaurant discounts could affect profitability. Without understanding check-in and checkout patterns, a labor-savings initiative might create long lines at the front desk, impacting the guest experience. Today, your service mistakes are broadcast through social channels and review sites as they happen. The competition is no longer just the hotel next door, but it is also third-party distribution channels and alternative lodging providers like AirBnB, all waiting in the wings to win your guests from you. On top of all that, recent merger and acquisition activity is creating scale never before seen in this industry, and global economic conditions continue to be unstable.
When the stakes are this high, you need something to help shore up that balance between delivering an excellent guest experience and meeting profit obligations. Analytics can be that thing. Tarandeep Singh, Senior Director, Revenue Performance and Analytics, Asia, Middle East, and Africa says, “Analytics is like GPS—it helps you be on track, and even pings you when you go off.” Fostering a culture of fact-based decision making ensures that the organization can find the right direction, understand the trade-offs, hedge against risk, know the next best action, and stand the best chance to be competitive in an increasingly crowded marketplace.
Einstein reminds us in his quote at the beginning of this chapter that there is still room for intuition and inspiration in this vision. Your intuition can be backed up by the data, getting you closer to “knowing” you are right. Inspiration for the right action can come from what the numbers tell you. Intuition and inspiration are even more powerful when paired with curiosity and questioning. David Schmitt, former director of Interactive Marketing Operations and Analytics for IHG, says in his blog, “The questions from the business are our North Star, the guidance and direction that provide clarity to analytics efforts.”1
The goal is to cultivate a culture of asking good questions and letting the data provide the answers. There are so many examples today of companies who have successfully, and sometimes famously, derived insight from their data assets through analytics, which helped to create a huge competitive advantage or some remarkable innovation. This could be you. Let’s talk about the characteristics of a strategic analytic culture first, and then I will tell you how this book can help you to build a strategic analytic culture in your own organization and set yourself up for success through analytics.
Strategic Analytic Culture
So, what does a strategic analytic culture (SAC) look like? Figure 1.1 outlines the interrelated components of a SAC.
Figure 1.1 Strategic Analytic Culture Framework
A strategic analytic culture starts and ends with executive management commitment. This level of support is required to make the necessary investments in people, process, and technology, as well as to ensure the alignment among departments that is critical to enterprise-level thinking.
The executive management team uses analytics to set business strategy. Rather than being guided by individual intuition or aspiration, the data and analytics offer a fact-based pathway toward the strategy, which is based on market conditions, customer characteristics, and the company’s operating circumstances.
The foundation of any analytics program is an organization-wide commitment to data management. Data management programs include:
Data governance to provide data definitions and guidelines for storing and accessing information
Data integration to ensure that data from disparate systems is matched and consolidated
Data quality programs to ensure data is cleansed before being used in analytics
Data storage infrastructure that facilitates access for analytics and reporting
An all-encompassing data management strategy facilitates enterprise use of analytics. Most organizations have isolated pockets of analytic capability, whether it be in revenue management, marketing, or finance. Enterprise use of analytics brings these siloed departments together, ensuring that decision making is not done in isolation.
Mark Lomanno, partner and senior advisor for Kalabri Labs, in an interview in the blog The Analytic Hospitality Executive, said that the role of analytics is becoming increasingly centralized in hospitality. “Traditionally the role of analytics has been more in the financial metrics measurement category, to some degree in the operations category, and in the marketing category; however, in the future all those will come together,” Mark said. He predicted that over time, online hotel reviews and comments in social media will replace traditional guest satisfaction measures as the primary gauge of customer satisfaction, and that companies will be able to start predicting occupancy and rates by the quality and nature of the hotel’s consumer comments and reviews. “This will force operations and marketing to work very closely together to react very quickly to what the consumer is saying,” Mark said.
Mark’s prediction points to the need to break down silos, improve communication, and synchronize decision making. When the entire enterprise is aligned around analytics, it creates a culture of fact-based decision making. You’ve probably heard the saying “In God we trust, all others must bring data.”2 Companies with a SAC back up all of their decisions with data and analytics, rather than instinct and internal influence. This doesn’t mean that you stifle creativity. It means that creative thought is supported by an analysis to back up conclusions or reinforce decision making. In fact, strategic use of analytics can help organizations become more creative and more agile when it uncovers insights that were not apparent on the surface.
Ted Teng, President and CEO at The Leading Hotels of the World provided this perspective in a video interview for SAS and the Cornell Center for Hospitality Research: “We are an industry of emotional decisions. We badly need analytics and good data for us to make the right decisions.” Ted explained that the hospitality market has completely changed and industry operators can no longer rely on how they did things 20 years ago. “There’s a lot of talk about big data out there. I am happy with just small data—some data—that allows us to make better decisions that are based on facts rather than based on our emotions.”
Where is your organization in this cycle? Are you getting stuck at executive commitment? Perhaps it’s been too difficult to build a data management infrastructure? Is analytic competency still residing in pockets across the organization? This book is designed to help you achieve the SAC vision from the ground up, or from the top down if you are fortunate enough to have that kind of power and influence!
Moving Ahead and Staying Ahead with Prescriptive Decision Making3
Most hospitality organizations today recognize the need for data-driven decision making, and they are making strides in that direction, or at least planning for it. In marketing, managers want to understand the customer better to improve targeting and value calculations. Operations knows that demand forecasting can support better staffing and ordering decisions, and finance recognizes that performance analysis drives opportunities for efficiencies and strategic growth. As organizations embrace data, analytics, and visualizations, they evolve from “gut-feel” reactive decision makers to more proactive, forward-looking decision makers.
I believe that hotels and casinos are at a turning point in data and analytics. Most hospitality companies have implemented some level of data management and business intelligence, or at least are on the path. Many hotels and casinos have made investments in predictive analytics solutions for revenue management or marketing. All organizations have at least some desire to provide access to the right information at the right time to the right resources to make the right decisions. If organizations successfully build out their data and analytic infrastructures, they will be part of the way there. If they are able to successfully leverage the analytic results across their organizations, they will get ahead and stay ahead.
Analytic solutions are simply decision support tools. They must be used by managers who have the experience to interpret the results and take the appropriate actions. Revenue management systems, for example, drive revenue because the revenue manager can interpret the price and availability recommendations and implement them as part of a broader pricing strategy. The jobs of the revenue management system and the revenue manager are not the same. A hotel cannot simply hook up the revenue management recommendations to the selling system and walk away. At the same time, a revenue manager can’t process the millions of pieces of information required to understand market opportunity by hand. However, a great revenue management system managed by a business-savvy revenue manager is a winning combination.
An executive from a large hotel brand told me that one of the driving factors for their business analytics investments is to get better information into the hands of their senior executives faster. “Imagine how much more effective smart and charismatic leaders would be in an investment negotiation or even an internal meeting if they had instant access to performance metrics, to support whatever questions they happen to get asked,” he told me. “We have great, highly experienced leadership, they are doing a good job today, but I’m sure they could drive much more revenue with better information at their fingertips the moment they need it.” It’s not that the information doesn’t exist, or that there aren’t standard sets of reports available. The difference is in the flexibility of the data structure and speed of access to the information. To be able to access information in the right format at the speed of a business conversation, no matter what is needed at the time, is beyond the technical capabilities of most organizations today.
Once again, these systems are not supposed to replace the experience and ability of a top-performing executive, but rather, they should provide information to better interpret a situation, respond more quickly to a question, reinforce or demonstrate a point, convince an investor, or make a key business decision faster. This should be the goal not only at the senior leadership level, but also replicated throughout the organization. It will take the right decision support tools, backed by credible data and advanced analytics, and it will also take the right person in the role of interpreter and decision maker.
This is why I argue that we are at a turning point in hospitality and gaming. We are moving through the chain of analytic maturity, perhaps at different rates organization by organization or department by department within organizations. We are getting to the point where we will need a different type of business analyst and a different type of manager to move ahead and stay ahead. As the needs of the business change, the skill sets and competencies of analysts and managers in analytical roles will need to change, as will the organizational structures, incentive plans, and scope of responsibilities.
The evolution of the scope of decision making in hospitality can be thought of in three stages, based on the ability to access and analyze data. As I mentioned previously, different departments in the organization may be at different stages, but the goal is to evolve everyone to the final stage.4
Descriptive.
At the first stage of analytic evolution, it is the best that organizations can do to develop and interpret historical reports. This is the descriptive phase. The organization could know that occupancy ran about 80% last month, or that 40% of reservations book in the week before arrival. Past revenue is tracked to identify historical trends. Decisions are based on this historical snapshot, which primarily involves reacting (i.e., putting out fires). Reports come from disparate systems, often are built in Excel, and pass through multiple hands before being finalized. Creating these reports is time consuming and prone to mistakes. Still, the business at least has some visibility into operating conditions and can report performance to executives—even if it takes a couple of days (or months) to pull together the information. As organizations evolve through this phase, they start to look at building out enterprise data warehouses and investing in business intelligence tools to improve the speed and accuracy of reporting. As more information gets into the hands of decision makers, they are able to react faster. For example, alerts are set up around key metrics so that managers can be made aware when they drop below, or rise above, certain critical levels.
Predictive.
In the next state of analytical evolution, organizations begin to deploy advanced analytic techniques that allow them to anticipate trends and take advantage of opportunities. They start to apply forecasting, predictive modeling, and optimization algorithms to existing data, typically either in marketing with predictive modeling on patron data, or in revenue management using forecasting and optimization to set pricing. These models produce results like occupancy will be 80% next month, the marketing campaign will result in a 2% lift, or revenue is expected to trend down for the next several months. Organizations then prepare themselves to manage through these now expected events. They can be more proactive in their approaches, setting up the right staffing levels to meet expected demand, adjusting price to take advantage of peak periods, or deploying marketing campaigns at the right time to get the best forecasted responses.
Prescriptive.
The final stage of analytic evolution is all about “what are we going to do about it?” In this phase, organizations are heavily supported by techniques like optimization, which provides the best possible answer given all business constraints, or simulation, a “what-if” technique in which a complex scenario with multiple moving parts is modeled so that parameters and options can be tested to determine the impact on key outcomes. For example, marketing optimization might give you the best possible set of contact lists for all of your promotions that will provide the highest response rate, but still respect budgets and patron contact preferences. Simulation lets you test the impact of a particular pricing strategy on demand and revenue generation, or the lift associated with spending a little more on a marketing campaign.
Advanced analytic techniques like forecasting, predictive modeling, optimization, and simulation are valuable because they provide a vision into the future or a decision point to consider, but the true mark of a prescriptive organization is that analysts and managers have the business acumen to both ask and answer the question “what are we going to do about it?” It’s fine to know that occupancy was 80% and it will be 90% next month. However, the true prescriptive manager can use that information, with their knowledge of the market and the operations, to build a plan to get to 95%. The skill set associated with this manager is different than the skills required in the descriptive or predictive phase, but clearly it is one that can move the organization forward—replicating the instincts, charisma, and acumen of the executive I described previously across all functional areas.
Making It Happen
For many organizations, this evolution in decision making will happen first in individual departments. The goal is to move the entire organization toward prescriptive decision making, supported by data and analytics. Success in a small area can become the inspiration that facilitates broad growth of analytical capabilities.
The point is that knowing what happened and what will happen is no longer enough. We need to build a culture of “what are we going to do about it?” in which the whole team uses the organization’s data and analytics to make fact-based decisions that move the organization forward.
Focus Areas for a Strategic Analytic Culture5
Moving your organization toward a strategic analytic culture requires more than just investments in analytic technology. Building a SAC starts with people, process, organization, and technology, in three focus areas within your organization.
Business analytics skills and resources
Data environment and infrastructure
Internal analytic processes
Focus Area 1: Business Analytics Skills and Resources
Find the right balance of resources. Building a strategic analytic culture is not simply hiring a bunch of analytic modelers and letting them play with your data, but rather striking the balance between analytic rigor and business application. Your best revenue managers understand their markets and their business, sometimes even better than they understand the forecasting and optimization algorithms underlying the revenue management system. And that’s okay. It is their ability to interpret the analytic results and apply them to their markets that makes them successful. Think about how to achieve this business acumen supported by analytic rigor across the organization.
To accomplish this, organizations may need to move to a structure where the advanced, predictive analytic models are created and managed by a central team of trained and experienced analysts, who work closely with counterparts in the business. The analyst’s role is to build the model with the guidance of and questions from the business, and then the business interprets the results through their experience and business acumen. When there is a shortage of analytical talent, this structure ensures analytic rigor is maintained, but also puts power in the hands of decision makers to access the right information when they need it to move the business forward. It releases the requirement that managers be highly analytical, but requires them to be analytical enough to interpret the numbers and savvy enough to read market conditions. In other words, it allows them to become prescriptive managers. I provide more detail about organizing an analytics department in Chapter 11.
Make analytics more approachable. Analytical skills are in short supply. In fact, in the United States it is estimated that demand for deep analytical resources will be 50% higher than supply by 2018.6 Organizations will need to figure out a way to make analytics more approachable. Highly visual, wizard-driven tools enable nontechnical users to explore and share “aha moments” without having to be PhD statisticians. They say a picture is worth a thousand words, and that’s true in analytics as well. Graphics are accessible and easy for executives to consume quickly. This ease of access will help to foster the commitment to fact-based decision making. Enabling business users to create and share insights will further the mission of enterprise use of analytics, while simultaneously freeing the limited supply of analytical resources to focus on the more rigorous analysis. In Chapter 3, I talk about visual analytics applications that can help move the organization to approachable analytics and self-service data visualization.
Focus Area 2: Information Environment and Infrastructure
Without a strong foundation of reliable and accurate data, analytic results will be suspect, and buy-in becomes impossible. You can spend all meeting, every meeting arguing about whether revenue per available room should include the out-of-service rooms, or instead spend the time making strategic decisions about price position relative to the competitive set. A sound data management strategy gets you on the road to analytic success, and away from the need to confirm and reconfirm the data. Here’s how to establish the foundation for a commitment to data management:
Establish a data governance discipline.
As data and analytics become centralized, data governance ensures consistency in data definitions, data integration guidelines, and data access rules. This is crucial to establishing a “single version of the truth” in results and reporting, as well as to building a sustainable process for continuing to advance organizational data acquisition.
Upgrade your data architecture.
In order to effectively leverage the insights trapped in today’s fast moving, diverse volumes of data, you need a modern data infrastructure that can support enterprise-class analytics and dynamic visualizations.
Bridge the gap between IT and the business.
A strong partnership between IT and the business must be built to ensure that the infrastructure described previously facilitates exploration and fact-based decision making. A key new resource to add to the organization could be the “translator” between IT and the business—someone who understands how to interpret the business requirements into an IT context, and vice versa.
Capitalize on advanced analytics, not reporting.
Any SAC relies on forward-looking analysis to stay ahead of trends and proactively identify opportunities. This requires moving from descriptive analytics that simply illustrate where you are today, to the use of predictive analytics like forecasting and optimization, which can identify what could happen and help you determine the best possible response in advance.
Chapter 2 of this book will demystify data management so that you can work with your peers and IT to establish a strong, credible data platform as the foundation of your analytics efforts.
Focus Area 3: Internal Processes
Enterprise use of analytics is not as simple as “everyone log in and go.” With limited personnel and technology resources, organizations will need processes in place to ensure access to critical analytical or IT resources. Then, the organization can better identify, prioritize, and address analytical requirements—whether it be deploying a new retention model or investing in a new analytical tool.
Manage analytics as an ongoing process, not a one-off project. Internal processes must be designed around sustainable, long-term analytic performance throughout the analytics life cycle. You will need to think not just about developing models, but deploying them, embedding them into a business process, and monitoring and improving them over time.
Facilitate collaboration. Traditionally, hospitality, like so many other industries, has operated with siloed departments. To facilitate collaboration, the silos that prevent collaboration must be removed. Technology may be the glue that binds departments together, but true collaboration will require realigning incentives, changing organizational structures, and breaking down barriers. Resources across the organization should be empowered and given incentives to act in the best interests of the enterprise, not just their departments.
This is not an insignificant effort in most organizations. Collaboration across the enterprise is not possible without at least one active and influential ally at the top of the organization who is able to drive change. Frequently, a grassroots effort from one department stalls out when that department is unable to gain momentum and get executives’ attention. Chapter 11 describes in further detail how analytic hospitality executives can turn their grassroots efforts into an enterprise-wide initiative.
You can talk all you want about the analytics cycle, the importance of integrating data, the value of advanced analytics, but I think the most important element in any analytics program is intent. What does the business want to get out of the analysis? What do they think is the measure of success? It is easy to make assumptions during an analysis and end up delivering something that the business didn’t expect, doesn’t want, or can’t use. Take time to clearly define the intent with the business before starting any analytics project, and you will be set up for success.
—Vivienne Tan, Vice President, Information Technology,Resorts World Sentosa
Getting Started
So, how do you get started? Read the rest of this book, obviously! In all seriousness though, building a strategic analytic culture is a journey that should be accomplished in phases. I talk again, and in more detail, about this phased approach in Chapter 11, but here is a summary to set some context (also see Figure 1.2).
Figure 1.2 A Phased Approach
Establish.
The first phase is where you implement the enabling analytic technologies, create processes, and place people within key departments. Here it is most important to ensure that you have solid processes to build on, well-trained people, and the right technology to support current operations as well as future growth.
Integrate.
Next, you begin to integrate data and analytics across a few key departments. Get a cross-functional team together to define metrics and identify opportunities, then start providing analysts with manual access to new data sources. Let them get comfortable with the data, so they fully understand how it will impact results and decisions.
Optimize.
As analysts become comfortable, it’s time to automate. Data can be incorporated into models and results operationalized. Since the analysts are already familiar with the data, they’ll be more likely to understand and accept new results and new decisions.
Innovate.
When your automated processes become ingrained in organizational decision making, you’ve built a platform for innovation. Sometimes, innovation is simply adding a new data source or a new analytic technique. Other times, it may require starting from the beginning with the establish phase. Either way, you’ve got a process in place for ensuring success.
You’ll need organizational buy-in to embark on this journey, and that isn’t always easy to achieve. Find a project that is easy to complete and highly visible. Perhaps you start with one small initiative that is a pet project of a visible executive. It can also be helpful to find a project that bridges the gaps between two siloed pockets of analytic capability, since those departments are already comfortable with their own data. Leverage the entire cycle from data governance to automating analytics so that you can set up repeatable processes. Start small, and win big, but don’t lose site of the ultimate goal—developing a high performance organization built on a solid foundation of data management and advanced analytics.
The most immediate and important executive action is to start asking for proof. Force your teams to defend any recommendations with data. Find out if there are additional data sources or analytical tools that would help them to make better decisions, and make that happen. Encourage collaboration across departmental boundaries. As your success grows, you’ll find your peers recognizing the momentum and wanting to get on board themselves!
How This Book Can Help
In the rest of this book, I provide you with information and strategies to help you identify opportunities within your organization to start on the path to a strategic analytic culture—or to help you cross the finish line if you are nearly there already! This book is intended to provide hospitality executives with the information they need to make the right decisions about analytics strategy, people, and technology, to survive and thrive in today’s highly competitive market.
The foundation of a strategic analytic culture is data. Chapter 2 helps to demystify big data and describes the tools and processes available to manage it. I talk about the importance of establishing a common business language and how to set a data governance process in place that will make and keep you successful. I give you strategies for identifying data sources that could provide value to the organization, and talk about how to access, integrate, cleanse, and store that data.
Chapter 3 describes why visualizations are “worth a thousand words.” Everyone wants to be able to communicate more effectively, particularly to leadership and stakeholders. In this chapter, I discuss how to create powerful visualizations that get your point across without complicating the message. I describe the technology enablers, provide tips for creating powerful visualizations, and give examples of visualizations.
Everyone seems to be talking about analytics these days, and many companies throw that term around to describe practically any use of data. In Chapter 4, I discuss the difference between descriptive and
