The analytic hospitality executive: Implementing data analytics in hotels and casinos41472

The Analytic Hospitality Executive Wiley & SAS Business Series The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions Titles in the Wiley & SAS Business Series include: Analytics in a Big Data World: The Essential Guide to Data Science and its Applications by Bart Baesens Bank Fraud: Using Technology to Combat Losses by Revathi Subramanian Big Data Analytics: Turning Big Data into Big Money by Frank Ohlhorst Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics by Evan Stubbs Business Analytics for Customer Intelligence by Gert Laursen Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure by Michael Gendron Business Intelligence and the Cloud: Strategic Implementation Guide by Michael S Gendron Business Transformation: A Roadmap for Maximizing Organizational Insights by Aiman Zeid Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media by Frank Leistner Data-Driven Healthcare: How Analytics and BI are Transforming the Industry by Laura Madsen Delivering Business Analytics: Practical Guidelines for Best Practice by Evan Stubbs Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition by Charles Chase Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain by Robert A Davis Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments by Gene Pease, Barbara Beresford, and Lew Walker The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models by Keith Holdaway Health Analytics: Gaining the Insights to Transform Health Care by Jason Burke Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World by Carlos Andre Reis Pinheiro and Fiona McNeill Hotel Pricing in a Social World: Driving Value in the Digital Economy by Kelly A McGuire Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset by Gene Pease, Boyce Byerly, and Jac Fitz-enz Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education by Jamie McQuiggan and Armistead Sapp Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet by Mark Brown Predictive Analytics for Human Resources by Jac Fitz-enz and John Mattox II Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance by Lawrence Maisel and Gary Cokins Retail Analytics: The Secret Weapon by Emmett Cox Social Network Analysis in Telecommunications by Carlos Andre Reis Pinheiro Statistical Thinking: Improving Business Performance, second edition, by Roger W Hoerl and Ronald D Snee Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics by Bill Franks Too Big to Ignore: The Business Case for Big Data by Phil Simon The Value of Business Analytics: Identifying the Path to Profitability by Evan Stubbs The Visual Organization: Data Visualization, Big Data, and the Quest for Better Decisions by Phil Simon Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean Win with Advanced Business Analytics: Creating Business Value from Your Data by Jean Paul Isson and Jesse Harriott For more information on any of the above titles, please visit www wiley.com 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 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and services or for technical support, please contact our Customer Care Department within the United States at (800) 7622974, outside the United States at (317) 572-3993 or fax (317) 572-4002 Wiley publishes in a variety of print and electronic formats and by print-on-demand Some material included with standard print versions of this book may not be included in e-books or in print-on-demand If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http:// booksupport.wiley.com For more information about Wiley products, visit www.wiley.com 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 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To my favorite analytic hospitality executives I n d e x   ◂  division of labor, with multiline, multiserver system for queues, 148 DORM (director of revenue management), 250 Drucker, Peter, 135 duration, 128–130, 142 dynamic nature, 368–376 E e-books, 184 e-commerce marketing, 184 econometric methods, of forecasting, 93–94 EDWs (enterprise data warehouses), 37–39 effect size estimate, 87 efficiency metric, 239 Ellis, Bernard, 162–163, 343–348 e-mail direct marketing, 184 emerging data sources for gaming, 280–281 for marketing, 174–177 for operations, 133–134 for performance analysis, 262–263 for revenue management, 230–231 for sales, 209 enrichment, of data, 42 enterprise data warehouses (EDWs), 37–39 enterprise goals, versus departmental goals, 189 enterprise optimization (EO), 163, 344–345 395 entity, forecast, as consideration in forecasting, 142 EO (enterprise optimization), 163, 344–345 equitable waits, versus unfair waits, 154 establish phase, 13–14, 14f, 335, 335f estimation, 87–88 ETL (extraction, transformation, and loading), 33, 34 events revenue management, 215–218, 355–356 Excel, 268 excellence, demonstrating, 315 executive management commitment, executive sponsors, cultivating, 315 exogenous factors, 92, 93 expert forecasting, 264–265 explained waits, versus unexplained waits, 153–154 extraction, transformation, and loading (ETL), 33, 34 F Facebook, 176–177 fact-based decision making, culture of, fairness, with single-line, multiserver system for queues, 149 federated analytics, 319–320 fences, 225 Few, Stephen “Save the Pie for Dessert”, 65 396  ▸   I n d e x Financial Crimes Enforcement Network (FinCEN), 295 finite waits, versus uncertain waits, 153 first touch channel, 190 fixed costs casino revenue optimization and, 286 revenue management and, 222 flash drives, 184 flexibility in business analytics, 159–160 for data handling tools, 58–59 of data storage, 29 of data warehouses, 34–35 of Hadoop, 32 food and beverage minimums, as a sales factor, 206 forecasting about, 91–92 accuracy of, 94–96, 121n5 considerations for, 92 for gaming, 284, 301–302 illustrated, 91f increases in demand, 369f in labor scheduling process, 137, 141–143, 141f for marketing, 180 methods of, 92–94 for operations, 136–137 for performance analysis, 264–266, 270 for revenue management, 234–235 for sales, 210–211 technology and, 159, 160 formatting, of data visualizations, 68 forward-looking demand data, for revenue management, 230 fraud, gaming and, 294–298 Friedlander, R.J., 106–111 function space, revenue management and, 239, 241–243 functional analytics, 318 functionality, of visualization tools, 59–60 future, preparing for the, 338–339 fuzzy matching, 41–42 G gaming about, 276–278 analytics for, 276–305, 281–285 benchmarking analytics capabilities, 299–300 case study, 291–294, 292f, 293f casino floor revenue optimization, 285–291 data for, 278–281, 280f data management for, 20–52 data visualization for, 301 emerging data sources for, 280–281 forecasting for, 284, 301–302 fraud and anti–money laundering, 294–298 optimization for, 285 people for, 300–301, 302 I n d e x   ◂  predictive modeling for, 284 revenue management and, 245–247 technology for, 300–302 gaming floor, operations, marketing and, 333–334 generalized cleansing, of data, 40–41 Gladwell, Malcom Outliers: The Story of Success, 266 golf course, revenue management and, 239 Google Analytics, 183, 191–192, 196 Green, Cindy Estis Demystifying Distribution: Distribution Channel Analysis for Hotels, 253–254 grid computing, 112 group sales, 207–209 group waits, versus solo waits, 154 grouped bar charts, 62–63, 62f groups, evaluating, 351–359 Grover, Suneel, 188, 190 guest experience, as an operations challenge, 126–127, 126f guest intimacy, cultivating, 330 guest journey, 328f, 329–331 guest profile building, 169–173 marketing and, 196 guest reviews, 176 guest surveys, 132–133 guest-centric marketing, 195, 195f 397 guest rooms, as a baseline for revenue management, 354–355 guests, tying digital data to, 188 gut instinct, relying on, 338 H Hadoop platform, 32–33, 268 Harrah’s Entertainment, 172 Harris, Jeanne Competing on Analytics, 337 heat maps, 66–67, 66f, 291 high-rollers, 172 Hilton Worldwide, 25–27, 200–201, 265–266 histograms, 63, 63f historical information, data federation/virtualization and, 37 Ho, Jeannette Hotel Pricing in a Social World, 236, 238, 332 Hold-out sample, 121n5 hospitality building a strategic analytic culture in, 1–17 data management for, 20–52 hosted applications/solutions, 114 Hotel Pricing in a Social World (McGuire and Ho), 236, 238, 332 hotels revenue management and, 226–227, 236–243 typical input data for, 229 398  ▸   I n d e x hub-and-spoke model, 225 hypothesis testing (yes/no questions), 86–87, 178, 263–264 I IBM, 200–201 IDeaS, 207, 251–252, 350–365 IHG, 271–272 impact, prioritizing, 314–315 implementation speed, with cloud solutions, 115 incentive alignment, 364 in-database processing, 113 inferential statistics, 85–86 influencer marketing, 184 Infor, 162–163, 343–348 information architecture, upgrading your, 11 information environment and infrastructure, as a focus area of strategic analytic culture, 11–12 information management programs, in-memory processing, 113 innovate phase, 14, 14f, 335f, 336 in-process waits, versus preprocess waits, 153 “Insights and Innovations for Hospitality” webinar, 158 integrate phase, 14, 14f, 335–336, 335f integrated data, for digital intelligence, 186–189, 186f, 187f integrated decision making, 327–329 intention mining, 100 internal processes, as a focus area of strategic analytic culture, 12–13 Internet of Things, 134 IT bridging gap between business and, 12 burden of, with cloud solutions, 114 J JHM Hotels, 219 jockeying, with multiline, multiserver system for queues, 149 Jurys Inn, 109 K Kalabri Labs, 253 key performance indicators (KPIs), 134–135, 178 Kiesling, Kate, 207, 215–218, 350–365 Kimes, Sherri, 243 Kimpton KarmaTM, 170 knowledge, 364–365 Koch, David, 291–294, 301, 368 KPIs (key performance indicators), 134–135, 178 L labeling, in data visualizations, 68 labor scheduling forecasting, 137 planning, 140–141, 141f process of, 141–145, 141f I n d e x   ◂  large-scale optimizations, 113 Las Vegas, 276 Las Vegas Sands, 265, 298 last touch channel, 190 Laws of Service, 152, 166n3 lead time, as a baseline for revenue management, 356–357 Leading Hotels of the World, 200 learning and development, as an opportunity of consolidating analytics, 321 leg, of travel, 225 length of stay controls, at hotels, 226 line charts, 64, 64f line cutting, with single-line, multiserver system for queues, 149 line switching, with multiline, multiserver system for queues, 149 linear attribution channel, 190 location data, 133–134, 177 location-based data collection, 197–198 location-based technology, 161 Lock, John, 341n4 logistic regression, 97, 98 Lomanno, Mark, 4–5 Demystifying Distribution: Distribution Channel Analysis for Hotels, 253–254 Loveman, Gary, 265 low-demand periods, 369f loyalty programs, 169–173 399 M machine learning, 103–104 See also data mining MAD (mean absolute deviation), 94 magnitude, showing changes in, 62 Maister, David, 166n3 The Psychology of Waiting Lines, 152 managed solution, 114 MAPE (mean absolute percentage error), 95 Marina Bay Sands, 283 marketing about, 168–169 advanced analytics, 177–182 analytics by business goals, 182, 182f analytics for, 168–201 benchmarking analytics capabilities, 191–192 campaign, 184 case study for, 192–195, 195f data for, 169–177 digital, 183–184 digital intelligence, 183–191 e-commerce, 184 forecasting and, 180 guest-centric, 195, 195f influencer, 184 management of, for analytical organizations, 331–332 operations, gaming floor and, 333–334 optimization, 181–182 people and, 196, 198–199 400  ▸   I n d e x marketing (continued) performance of campaigns, 173–174 predictive modeling, 180–181 social media, 184 statistical analysis for, 178–180 technology and, 196–198 marketing optimization technology marketing and, 198 Marriott, 158 Marriott International, 321–324 matching data, 41–42 McDonald’s, 157–158 McGuire, Kelly A Hotel Pricing in a Social World, 236, 238, 332 mean absolute deviation (MAD), 94 mean absolute percentage error (MAPE), 95 meeting space, as a sales factor, 206 meetings revenue management, 215–218 metrics about, 359 monitoring, 330 profit per available space/time (ProPRE), 362–364 profit per occupied space/time (ProPOST), 361–362 utilization, 359–361 Mezrich, Ben Bringing Down the House, 296 MicroStrategy, 56 Minard, Charles, 67, 67f minimum bet variable, 287 Minnock, Bill, 166n4 mitigating risks, 42–43, 43f moving average, 93 multicollinearity, 50 multiline, multiserver system, for queues, 148–149, 149f multitenant nature, of SaaS solutions, 116 N naïve approaches, to forecasting, 92–93 natural language, 104 net rates, for revenue management, 230 network effect, 225 networking, importance of, 315 noisy data, 86 Noone, Breffni, 170, 174–175, 230 number of seats variable, 287–288 O Obama, Barack, 59 objective, as an element of optimization, 101 Occam’s razor, 49–50 occupancy metric, 360 occupied wait time, versus unoccupied wait time, 152 offline data, 185 OLAP cubes, 58 on-premise solutions, 114–115 open-source database, 33 open tables variable, 287 I n d e x   ◂  operations about, 124–128, 126f analytics for, 124–166, 134–140, 140f benchmarking capabilities, 156–158 case study, 162–163, 166n4, 244–245, 343–348 challenges of, 126–127, 126f customer satisfaction, 132–133 data for, 128–134 emerging data sources, 133–134 forecasting and, 136–137 importance of time in, 128–132 management of, for analytical organizations, 332–333 managing consumer perceptions, 152–155 marketing, gaming floor and, 333–334 optimization and, 139 people and, 158 predictive modeling and, 138–139 queues, 146–148 statistical analysis and, 135–136 technology and, 158–161 workforce planning and optimization, 140–145, 141f optimal table mix, 244–245 optimization about, 100–103, 102f enabled, 189 for gaming, 285 401 for marketing, 181–182 for operations, 139 for performance analysis, 267 for revenue management, 235–236 for sales, 211–212 slot floor, 289–291 workforce, 140–145, 141f optimize phase, 14, 14f, 335f, 336 Osborn, Natalie, 191, 192–195, 195f outliers, affecting data, 92 Outliers: The Story of Success (Gladwell), 266 overbooking, at hotels, 226–227 overfitting, 50–51 P pace, 260 parallel processing, 112 parsing, of data, 41 Patriot Act (2001), 295 patron theoretical loss slots, 282 patron theoretical loss table, 282 patron value optimization, 245–247 people for gaming, 300–301, 302 for marketing, 196, 198–199 for operations, 158 for performance analysis, 269, 270–271 for revenue management, 250–251 technology and, 161–162 402  ▸   I n d e x performance decline in, 370f of marketing campaigns, 173–174 measuring, 239–241 performance analysis about, 258–259 for analytical organizations, 334 analytics for, 258–274 benchmarking analytics capabilities, 267–269 case study for, 271–272 data for, 259–263 emerging data sources for, 262–263 forecasting for, 264–266 optimization for, 267 people for, 269, 270–271 predictive modeling for, 266–267 statistical analysis for, 263–264 technology for, 269–270 perishable product, casino revenue optimization and, 285 personalization, 189, 327–329, 329f phased approach, 334–337, 335f photolicitation, 176 pie charts, 64–65, 65f, 69, 69f Pinnacle Entertainment case study, 37–39 Planet Money (podcast), 304 planning in labor scheduling process, 141f, 143–144 workforce, 140–145, 141f player theo, 282 point of sale (POS) data, 40, 130, 351 Polsky, Analise, 329–331 POS (point of sale) data, 40, 130, 351 position based channel, 190 predictive analytics about, 96, 121n4 cluster analysis, 97, 99 data mining, 99–100 illustrated, 97f logistic regression, 97, 98 predictive modeling for gaming, 284 for marketing, 180–181 for operations, 138–139 for performance analysis, 266–267 for sales, 211 predictive stage, of analytic evolution, preprocess waits, versus inprocess waits, 153 prescriptive decision making, 5–9, 339 prescriptive stage, of analytic evolution, 8–9 price constraint, 255n1 price optimization See revenue management price sensitivity of demand, 237–238, 237f price transparency, 228, 230 price-able demand, 237 privacy, with single-line, multiserver system for queues, 149 I n d e x   ◂  proactive decision making, reactive decision making to, 83–84, 84f processing power, of data warehouses, 34–35 profiling, data, 40 profit per available space/time (ProPAST), 362–364 profit per occupied space/time (ProPOST), 361–362 progressives, 305n1 project efficiencies, as an opportunity of consolidating analytics, 321–322 ProPAST (profit per available space/time), 362–364 proprietary data management, 188 The Psychology of Waiting Lines (Maister), 152 purpose, of data visualizations, 68 p-value, 86–87, 90, 97, 120n2 Q qualitative forecasting, 92 quantitative forecasting, 92 questions, chapter, 377–383 queue configuration as a feature of a queuing system, 147 impact of, 148–152 queue discipline, as a feature of a queuing system, 147 queues, operations and, 146–148 queuing theory, 138–139 403 R radio frequency identification (RFID) chips, 289 reactive decision making, to proactive decision making, 83–84, 84f real-time analytics, 118–119 real-time decisioning, marketing and, 197–198 real-time technology, 161 REG procedure, 89f regression, 88–91, 89f, 120n3, 121n4, 178, 233 regulatory issues, with onpremise solutions, 115 relational database, 31–32 reputation management about, 107 marketing and, 196 for revenue management, 230, 250 technology and, 159 request for proposals (RFPs), 213, 354 resources, sales, 214–215 response rates, 173 restaurants, revenue management and, 239, 241 return on investment (ROI), 42–43, 43f revenue management about, 222–223 for analytical organizations, 331–332, 332–333 analytics for, 222–255, 231–243 benchmarking analytics capabilities, 247–249 404  ▸   I n d e x revenue management (continued) case study for, 244–245, 251–252, 350–365 casinos, 245–247 data for, 229–231 emerging data sources for, 230–231 evolving, 227–229 forecasting for, 234–235 history of, 223–227, 224f hotels and, 226–227, 236–238, 239–243 illustrated, 232f optimization for, 235–236 outside of rooms, 239–243 patron value optimization, 245–247 people and, 250–251 statistical analysis for, 233–234 technology for, 249–250 revenue optimization, 285–289, 286f See also revenue management revenue per available room (RevPAR), 239–240, 253–254, 259–260, 350 revenue per available seat-hour (RevPASH), 241 revenue per available timebased inventory (RevPATI), 239–240 ReviewPro, 106–111 reviews, guest, 176 RevPAR (revenue per available room), 239–240, 253–254, 259–260, 350 RevPASH (revenue per available seat-hour), 241 RevPATI (revenue per available time-based inventory), 239–240 RFID (radio frequency identification) chips, 289 RFPs (request for proposals), 213, 354 risks, mitigating, 42–43, 43f Roberts, Dave, 136, 321–324 Rohlfs, Kristin, 174–175 ROI (return on investment), 42–43, 43f rooms, as a sales factor, 206 Roquefort, Agnes, 75 S SaaS (Software-as-a-Service), 114 SAC (strategic analytic culture) See strategic analytic culture (SAC) sales about, 204–205 advanced analytics for, 210 analytics for, 204–219 benchmarking analytics, 214–215 case study for, 215–218 changing landscape of, 212–214 data for, 205–209 emerging data sources for, 209 forecasting and, 210–211 optimization and, 211–212 predictive modeling and, 211 I n d e x   ◂  resources, 214–215 statistical analysis for, 210–212 technology, 214–215 Sankey diagrams, 67, 67f SAS® Forecast Server, 95 SAS® Visual Analytics, 56 “Save the Pie for Dessert” (Few), 65 scalability of data storage, 29 of Hadoop, 32 scatter plots, 65, 66f scheduling, in labor scheduling process, 141f, 144–145 Schmidt, David, 3, 266, 271–272 scoring, 97, 181 search data, for revenue management, 231 search engine marketing (SEM), 183 search engine optimization (SEO), 183 seasonal demand, 368f seasonality as a baseline for revenue management, 357–358 of data, 92 security, with cloud solutions, 115 segmentable demand, casino revenue optimization and, 286 segmentation, 189 segmentation analysis, 180–181, 284 SEM (search engine marketing), 183 405 sentiment analysis, as a category of text analytics, 106 SEO (search engine optimization), 183 service levels with cloud solutions, 114 with multiline, multiserver system for queues, 148 service process, as a feature of a queuing system, 147 shoulder periods, 226 siloes, acting in, 338 simulation analysis, 138, 160–161, 284 Singapore, 277, 280, 283 Singh, Tarandeep, single line data collection insert, 187 single-line, multiserver system, for queues, 149, 150f slot floor optimization, 289–291 Smith, Adam, 341n4 Smith, Michael, 219 Smith Travel Research (STR), 260, 261f SnapShot, 70–78 social data as an emerging data source for marketing, 174–177, 175f for gaming, 280–281 social media marketing, 184 social media optimization, 184 Software-as-a-Service (SaaS), 114 solo waits, versus group waits, 154 space allocation, revenue management and, 355 406  ▸   I n d e x sparse data, 92 spas, revenue management and, 239 special events, affecting data, 92 speed of data handling tools, 58 of data storage, 29 of data warehouses, 34 of Hadoop, 32–33 spend pattern, as a baseline for revenue management, 358–359 spreadsheets, compared to data visualization, 55–56 stacked bar charts, 63, 63f, 68–69, 69f staffing efficiencies, as an opportunity of consolidating analytics, 321 standard deviation, 85–86, 120n1 standardization of data, 41 of SaaS solutions, 116 standards of behavior, implementing, 331 statistical analysis about, 84–85, 120n2, 121n4 correlation, 87 estimation, 87–88 hypothesis testing (yes/no questions), 86–87 inferential statistics, 85–86 for marketing, 178–180 operations and, 135–136 for performance analysis, 263–264 regression, 88–91, 89f for revenue management, 233–234 for sales, 210–212 statistical modeling, gaming and, 291 storytelling, 331 STR (Smith Travel Research), 260, 261f strategic analytic culture (SAC) about, 3–5, 3f building in hospitality, 1–17 focus areas of, 10–13 strategic thinking, 340 streaming analytics, 118–119 structure, 364–365 structured data defined, 30 illustrated, 30f structuring, 296 style, of data visualizations, 68 success, factors influencing, 364–365 Swanepoel, Fanie, 283 Swenson, Andy, 37–39 T table game revenue management, 286–289 Tableau, 56, 75 take a number system, for queues, 150, 150f targeting, 189 technology about, 158 advanced analytics and, 111–113 I n d e x   ◂  advanced analytics platform, 160 advanced forecasting, 160 business intelligence, 159 flexible business analytics, 159–160 forecasting, 159 for gaming, 300–302 location-based/real-time, 161 for marketing, 196–198 for operations, 158–161 for performance analysis, 269–270 reputation management, 159 for revenue management, 249–250 for sales, 214–215 simulation analysis, 160–161 time and motion study, 160 workforce planning and optimization, 160 technology and distribution, for hotels, 72, 73f, 74f Teng, Ted, TerBush, Jeremy, 117, 272, 312–315, 341n2 text analytics, 104–106, 106–111 text data, as an emerging data source for marketing, 174–177 text mining, as a category of text analytics, 106 theoretical win, 282 third-party vendors, 213 407 Thomas, Gwen “The DGI Data Governance Framework”, 23–24 throughput, 130 time casino revenue optimization and, 286 as consideration in forecasting, 142 importance of in operations, 128–132 with single-line, multiserver system for queues, 149 time and motion study, 130–132, 131f, 160 time decay channel, 190 time series methods, of forecasting, 93 time-perishable inventory, revenue management and, 222 time-to-value, of SaaS solutions, 115–116 time-variable demand, revenue management and, 222 Total RewardsTM program (Harrah’s Entertainment), 172, 278–279 tracking signal, 95 transactional systems, data federation/virtualization and, 36 Travel Click, 46, 248 TripAdvisor, 176 Turnbull, David, 70–78 “24/7 Focus Group”, 132–133 Twitter, 176 408  ▸   I n d e x U uncertain waits, versus finite waits, 153 uncertainty, 138–139 unconstrained demand, 136, 233 unexplained waits, versus explained waits, 153–154 unfair waits, versus equitable waits, 154 unintended consequences, 341n4 unoccupied wait time, versus occupied wait time, 152 unqualified demand, 236 unqualified transient demand, 228 unstructured data, 28–29, 30 usability, of visualization tools, 60–61 user interfaces, of SaaS solutions, 116 utilization, as a metric, 359–361 V value of service, wait time and, 154 Van Meerkendonk, Paul, 251–252 variability, 138–139 variety of data, 28–29 of data handling tools, 58 velocity of data, 28–29 of game variable, 288 virtual queues, 150, 150f volatility, of data, 92 volume, of data, 28–29 W Waldorf Astoria, 265 Watson, 200 WayBlazer, 200 web data, as an emerging data source for marketing, 174 whales, 172 win or loss rate, calculating, 282 Wood, Dexter E., Jr., 25–27, 49 workforce planning and optimization, 140–145, 141f, 160 Wyndham Destination Network (WDN), 117, 312–315 Y yes/no questions (hypothesis testing), 86–87, 178, 263–264 yield management See revenue management WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... 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... 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... 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