Hotel pricing in a social world: Driving value in the digital economy41484

Hotel Pricing in a Social World 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: Agile by Design: An Implementation Guide to Analytic Lifecycle Management by Rachel Alt-Simmons 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 S 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 Economic and Business Forecasting: Analyzing and Interpreting Econometric Results by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business by David Thomas and Mike Barlow Financial Institution Advantage & the Optimization of Information Processing by Sean C Keenan Financial Risk Management: Applications in Market, Credit, Asset and Liability Management and Firmwide Risk by Jimmy Skoglund and Wei Chen Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications by Robert Rowan Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection by Bart Baesens, Veronique Van Vlasselaer, and Wouter Verbeke 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 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 Mobile Learning: A Handbook for Developers, Educators, and Learners by Scott McQuiggan, Lucy Kosturko, Jamie McQuiggan, and Jennifer Sabourin 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 Understanding the Predictive Analytics Lifecycle by Al Cordoba Unleashing Your Inner Leader: An Executive Coach Tells All by Vickie Bevenour Using Big Data Analytics: Turning Big Data into Big Money by Jared Dean 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 Visual Six Sigma, Second Edition: Making Data Analysis Lean by Ian Cox, Marie Gaudard, Philip Ramsey, Mia Stephens, and Leo Wright 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 Hotel Pricing in a Social World Driving Value in the Digital Economy Kelly A McGuire, PhD Cover images: Abstract background © Getty Images / Studio-Pro; Social network concept © mattjeacock / iStock.com Cover design: Wiley Copyright © 2016 by SAS All rights reserved Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada 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 Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose No warranty may be created or extended by sales representatives or written sales materials The advice and strategies contained herein may not be suitable for your situation You should consult with a professional where appropriate Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, 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: McGuire, Kelly Ann   Hotel pricing in a social world : driving value in the digital economy / Kelly A McGuire, PhD    pages cm — (The Wiley & SAS business series)   Includes bibliographical references and index   ISBN 978-1-119-12996-7 (cloth); ISBN 978-1-119-19241-1 (epdf); ISBN 978-1-119-19240-4 (epub); ISBN 978-1-119-16228-5 (obook)   1.  Hotels—Rates.  2.  Online social networks—Economic aspects   3.  Electronic commerce.  4.  Revenue management.  I.  Title   TX911.3.R3M35 2016   910.46068—dc23 2015033007 Printed in the United States of America 10  9  8  7  6  5  4  3  2  To the Girl Geek Gang, for the support, the inspiration, and the memories! 316  ▸   R e f e r e n c e s & Sons www.sas.com/store/books/categories/business‐leadership/ big‐data‐data‐mining‐and‐machine‐learning‐value‐creation‐for‐ business‐leaders‐and‐practitioners/prodBK_66081_en.html Dietz, Alex 2013 “Demystifying Price Optimization: A Revenue Manager’s Guide.” SAS white paper www.sas.com/en_us/whitepapers/demystifying‐price‐optimization‐revenue‐managers‐ guide‐106156.html Dietz, Alex 2013 “Can Big Data Help Revenue Management?” Analytic Hospitality Executive blog http://blogs.sas.com/content/hospitality/ 2014/10/08/can‐big‐data‐help‐revenue‐management Dietz, A., N Osborn, and T Sanli 2012 “Are You Awash in the Sea of Competitive Price Intelligence? Let Analytics Be Your Life Raft.” Paper 379–2012, SAS Global Forum 2012 Proceedings http://support sas.com/resources/papers/proceedings12/379–2012.pdf Dorman, James 2012 “What Is Personalization.” Quora http://www quora.com/What‐is‐the‐definition‐of‐personalization Duggan, M., N B Ellison, C Lampe, A Lenhart, and M Madden 2015 “Social Media Update 2014.” Pew Research Center Dull, Tamara “Hadoop in a Data Environment: A Non‐Geek’s Big Data Playbook.” SAS white paper www.sas.com/en_us/offers/15q1/ non‐geeks‐big‐data‐playbook‐106947.html Enz, Cathy, and Linda Canina 2010 “Competitive Pricing in European Hotels.” Advances in Hospitality and Leisure 6:3–25 Enz, Cathy, Linda Canina, and Breffni Noone 2012 “Strategic Revenue Management and the Role of Competitive Price Shifting.” Cornell Hospitality Report 12(6) Enz, Cathy, Linda Canina, and Mark Lomanno 2009 “Competitive Hotel Pricing in Uncertain Times.” Cornell Hospitality Report 9(10) Garlick, Rick 2009, November “What if You Reduced Your Hotel Room Rates and Nobody Noticed?” Hospitality Net www.hospitality net.org/news//4044480.html Green, Cindy Estis, and Mark Lomanno 2012 “Distribution Channel Analysis: A Guide for Hotels.” HSMAI Foundation Hanks, R B., R P Noland, and R G Cross 1992 “Discounting in the Hotel Industry: A New Approach.” Cornell Hotel and Restaurant Administration Quarterly 33(3): 40–45 R e f e r e n c e s   ◂  317 HSMAI CRME Certification: www.hsmai.org/career/content.cfm? ItemNumber=4863 Hotel Revenue Management (course), eCornell, www.ecornell.com/ certificates/hospitality‐and‐foodservice‐management/hotel‐revenue‐ management HSMAI salary report: www.hsmai.org/incentivespracticeresearch Huff, Darrell 1954 How to Lie with Statistics New York: Penguin Mathematics IHG Trends Report 2014 “Creating ‘Moments of Trust,’ the Key to Building Successful Brand Relationships in the Kinship Economy.” http://library.the‐group.net/ihg/client_upload/file/2014_moments_ of_trust_report.pdf Kahneman, D., J L Knetch, and R H Thaler 1986 “Fairness and the Assumption of Economics.” Journal of Business 59:S285–S300 Kahneman, Daniel, and Amos Tversky 1979 “Prospect Theory: An Analysis of Decision under Risk.” Econometrica 47: 263–291 Kaplan, A M., and M Haenlein 2010 “Users of the World, Unite! The Challenges and Opportunities of Social Media.” Business Horizons 53(1): 59–63 Kimes, S E 1989 “Yield Management: A Tool for Capacity‐Constrained Service Firms.” Journal of Operations Management 8(4): 348–363 Kimes, S E 2010 “The Future of Hotel Revenue Management.” Cornell Hospitality Report 10(14) Kimes, S E., and K A McGuire 2001, December “Function‐Space RM: A Case Study from Singapore.” Cornell Hotel and Restaurant Administration Quarterly 33–46 Kimes, S E., and R B Chase 1998 “The Strategic Levers of Yield Management.” Journal of Service Research 1(2): 156–166 Kimes, S E., R B Chase, S Choi, P Y Lee, and E N Ngonzi 1998 “Restaurant RM: Applying Yield Management to the Restaurant Industry.” Cornell Hotel and Restaurant Administration Quarterly 39(3): 32–39 Kimes, S E., and B M Noone 2002 “Perceived Fairness of Yield Management.” Cornell Hotel and Restaurant Administration Quarterly 43(1): 21–31 Kimes, S E., and Lee W Schruben 2002 “Golf Course RM: A Study of Tee‐times.” Journal of Revenue and Pricing Management 1(2): 111–120 318  ▸   R e f e r e n c e s Kimes, S E., and S Singh 2009 “Spa Revenue Management.” [Electronic version] Cornell Hospitality Quarterly 50(1): 82–95 Kimes, S E., and G M Thompson 2004 “Restaurant RM at Chevys: Determining the Best Table Mix.” Decision Sciences 35(3): 371– 392 Koushik, D., J Higbie, and C Eister 2012 “Retail Price Optimization at IHG.” Interfaces 41(1): 45–57 Kumar, T Krishna 1975 “Multicollinearity in Regression Analysis.” Review of Economics and Statistics 57(3): 365–366 JSTOR 1923925 Laney, D 2001 “3D Data Management: Controlling Data Volume, Velocity, and Variety.” META Group http://blogs.gartner.com/ doug‐laney/files/2012/01/ad949‐3D‐Data‐Management‐Controlling‐Data‐ Volume‐Velocity‐and‐Variety.pdf Marn, Michael V., and Robert L Rosiello 1992, September “Managing Price, Gaining Profit.” Harvard Business Review McGuire, Kelly 2012 “Customer Lifetime Value Analytics—The Holy Grail for Hotels and Casinos.” http://blogs.sas.com/content/hospitality/ 2012/02/29/customer‐lifetime‐value‐analytics‐the‐holy‐grail‐for‐ hotels‐and‐casinos McGuire, Kelly 2014, June “The Path to Personalization—The Vision Part 1.” www.hospitalityupgrade.com/_magazine/Magazine Articles/The‐Path‐to‐Personalization‐The‐Vision.asp McGuire, Kelly 2014, October “Hotel Pricing in a Social World: The Unmanaged Business Traveler.” http://blogs.sas.com/content/ hospitality/2014/10/06/chrs_pricingsocial McGuire, Kelly, and Suneel Grover 2014, July “The Path to Personalization Part 2: Digital Intelligence for Hospitality.” www.hospitalityupgrade com/techTalk/Articles/The‐Path‐to‐Personalization‐Part‐Two‐ Digital‐Intelligence‐for‐Hospitality Metters, R., C Queenan, M Ferguson, L Harrison, J Higbie, S Ward, and A Duggasani 2008 “The ‘Killer Application’ of Revenue Management: Harrah’s Cherokee Casino & Hotel.” Interfaces 38(3): 161–175 Naipaul, S., and H G Parsa 2001, February “Menu Price Endings That Communicate Value and Quality.” Cornell Hospitality Quarterly 42(1): 26–37 R e f e r e n c e s   ◂  319 Noone, B., S Kimes, and L Renaghan 2003 “Integrating Customer Relationship Management and Revenue Management: A Hotel Perspective.” Journal of Revenue and Pricing Management 2(1): 7–21 Noone, B., and K McGuire 2013a “Effects of Price and User‐ Generated Content on Consumers’ Pre‐Purchase Evaluations of Variably Priced Services.” Journal of Hospitality and Tourism Research doi:10.1177/1096348012461551 Noone B., and K McGuire 2013b “Pricing in a Social World: The Influence of Non‐Price Information on Hotel Choice.” Journal of Revenue and Pricing Management 12:385–401 Noone, B., and K McGuire 2015 “Impact of Attitudinal Loyalty on Road Warriors’ Use of Price and Consumer Reviews in Hotel Choice.” Working Paper Submitted to Journal of Revenue and Pricing Management Noone, Breffni, and Stephani Robson 2014 “Using Eye Tracking to Obtain a Deeper Understanding of What Drives Online Hotel Choice.” Cornell Center for Hospitality Research Reports 14(18) Noone, B., K McGuire, and K Rohlfs 2011 “Social Media Meets Hotel Revenue Management: Opportunities, Issues and Unanswered Questions.” Journal of Revenue and Pricing Management 10:293–305 Oosten, Maarten 2013 “Strategic Pricing: The Secret Sauce of Executives.” SAS white paper www.sas.com/content/dam/SAS/en_us/doc/ whitepaper1/strategic‐pricing‐secret‐sauce‐executives‐106561.pdf Ott, R., and M Longnecker 2001 An Introduction to Statistical Methods and Data Analysis, 5th ed Duxbury: Wadsworth Pan, Y., and J Q Zhang 2011 “Born Unequal: A Study of the Helpfulness of User‐Generated Product Reviews.” Journal of Retailing 87(4): 598–612 Park, D H., J Lee, and I Han 2007 “The Effect of On‐Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement.” International Journal of Electronic Commerce 11(4): 125–148 Phillips, R 2005 Pricing and Revenue Optimization Stanford: Stanford University Press Pinchuk, S 2007 “A System Profit Optimization.” Journal of Revenue and Pricing Management 7:106–109 Polsky, Analise 2014 “Data Visualization: Considerations for Visualization Deployment.” SAS white paper www.sas.com/en_us/white- 320  ▸   R e f e r e n c e s papers/iia‐data‐visualization‐7‐considerations‐for‐deployment‐ 106892.html Robson, Stephani, and Breffni Noone 2014 “Show Me What You See and Tell Me What You Think: Using Eye Tracking for Hospitality Research.” Cornell Center for Hospitality Research Report 14(17) Skiff.com The Future of Guest Experience http://products.skift.com/ ebook/the‐future‐of‐guest‐experience Smith, B C., J F Leimkuhler, and R M Darrow 1992 “Yield Management at American Airlines.” Interfaces 22:8–31 Sorenson, Arne 2014, March “State of Hospitality: Ask the Next Gen Traveler.” Linked‐In Pulse https://www.linkedin.com/pulse/ 20140325110517‐239587237‐state‐of‐hospitality‐ask‐the‐next‐ gen‐traveler?trk=mp‐reader‐card Talluri, Kalyan T., and Garrett J van Ryzin 2005 The Theory and Practice of Revenue Management (International Series in Operations Research & Management Science) New York: Springer Taylor, Wayne J., and Sheryl E Kimes 2010 “How Hotel Guests Perceive the Fairness of Differential Room Pricing.” Cornell Hospitality Report 10 (2) Tetko, I V., D J Livingstone, and A I Luik 1995 “Neural Network Studies Comparison of Overfitting and Overtraining.” Journal of Chemical Information and Computer Sciences 35(5): 826–833 doi:10.1021/ci00027a006 Thaler, R H 1985 “Mental Accounting and Consumer Choice.” Marketing Science 4(3): 199–214 Vinod, B 2004 “Unlocking the Value of Revenue Management in the Hotel Industry.” Journal of Revenue and Pricing Management 3(2): 178–190 Weinstein, Jeff 2014 “HOTELS Interview: Nassetta Reflects on Hilton’s IPO, Part 2.” HOTELS Magazine http://www.hotelsmag.com/ Industry/News/Details/48361 Worgull, Samantha 2014, September “Take a ‘Case Study’ Approach with RM Talent.” Hotel News Now www.hotelnewsnow.com/ Article/14399/Take‐a‐case‐study‐approach‐with‐RM‐talent Yang, S., S E Kimes, and M M Sessarego 2009 “$ or Dollars?: Effects of Menu Price Formats on Customer Price Purchases.” Cornell Hospitality Report 9(8) Index A ADR, versus market share, 215–219 agent selling experience, on path to personalization, 243–244 Airbnb, 274 airline industry, yield management in, 20–22, 21f airline model, algorithm, 27 Amazon, 204 analytics backing up strategy with, 207 big data and, 74–76 innovations in executing, 56–58 marketing and, 136 revenue management and skills in, 256 Anderson, Calvin, 259, 274 anticipation, 275–276 apples to apples comparisons 115–116 Ashford Hospitality Trust, 259 AST (Automated Sales Tips), 244 attribute levels for business travelers, 102–105, 104f for leisure travelers, 96–97, 97f value assessments and, 96–98, 97f attributes, importance of for business travelers, 102–103 Automated Sales Tips (AST), 244 B BAR (best available rate), 45n2 Bartnick, Fabian, 149–150, 275–276, 281 base price by room type, 45n2 basics, remembering the, 278 best available rate (BA), 45n2 Bezos, Jeff, 204 bias, 75 bias-variance trade-off, 75 big analytics, big data and, 61–63 big data about, 48–50 analytics and, 74–76 big analytics and, 61–63 data visualization and, 63–67 innovations in executing analytics, 56–58 innovations in solution delivery, 58–61 resources on, 77 responsible use of, 67–76 role of in revenue management science, 78–82 storing unstructured data, 52–56 booking process, simplifying, 241 Boston Consulting Groups matrix, 208 brand, pricing and, 195–203 brand class, 201–203 British Airways, 239 business strategies, pricing to support, 203–205 business travelers assessing overall value, 105–108 attribute levels and value assessments, 103–105, 104f importance of attributes for, 102–103 loyalty and demographics, 101–109, 102f, 103f, 104f price and user-generated content for, 100–101, 101f buying behavior, 86–87 321 322  ▸   I n d e x C campaign management marketing optimization and, 132f, 133 revenue management and, 132, 132f Canina, Linda, 210, 211 career paths, in revenue management, 261–264 casino revenue management, 138 chief revenue officer (CRO), 262, 276–277 choice modeling, 96 Citadelle/Wisdom of the Sands (Exupéry), 279 citizen, 198–199, 211–212, 217 cloud-based solutions, 58–60 communication skills importance of, 272–273 revenue management and, 256 competitive price effects, 31–34 competitive pricing business rules for, 33f danger of business rule-driven, 33f confidence, having, 202 consilience, 79 consistency, importance of, 195–203 constraints, as element of optimization problems, 27 consumers, how they choose, 87–101 continuous price optimization, 29 control, as an advantage of onpremise solutions, 59 core product, hiding price of, 203 Cornett, Janelle, 248, 273 cost as an advantage of on-premise solutions, 60 of Hadoop, 54 Crenshaw, Craig, 216–217 CRO (chief revenue officer), 262, 276–277 cross-channel experience, evaluating, 240 cross-sell/up-sell, 136, 137 Cullen, Kathleen, 280–282 customer analytics marketing optimization and, 132f, 133 revenue management and, 132, 132f customer choice modeling, 237–238 customer segments, 276–277 customer value, incorporating into revenue management, 138–140 D data about, 50–51 evaluating, 71–74 importance of, 274–275 marketing and, 136 sources for, 69–71, 113–115 data mining, 77 data normalization, 232 data visualization, big data and, 63–67 data-mindedness, revenue management and, 257 de Jong, Lennert, 199, 211–212 Dean, Sloan, 259 decision variables, as element of optimization problems, 27 demand generation of, 273–275 price sensitivity of, 26t demographics about, 95–96, 95f of business travelers, 101–109, 102f, 103f, 104f value assessments, 95–96, 95f descriptive phase, 255, 255f Dietz, Alex, 67 digital data, tying to guests, 232 digital intelligence, 229–232 discipline, elevating, 273 discounts, compared with surcharges, 202 disruptors, importance of, 274–275 I n d e x   ◂  distribution channel management, 181–188 dual entitlement, principle of, 197–198 Duetto, 153 dynamic packages, creating, 137 dynamic pricing, 29 E ecommerce, revolution of, education importance of, 272, 278 revenue management and, 257–258 effective pricing, 10–11 Enz, Cathy, 210, 211 EpicMix, 244 establish phase, of technology implementation, 141, 141f evaluating data, 71–74 evangelizing, 278 Evolving Dynamics (HSMAI), 11 execution, 228–229 Expedia, 5, 19f Exupéry, Antoine de Saint Citadelle/Wisdom of the Sands, 279 F Facebook, statistics on usage of, 4–5 Fairmont Hotels and Resorts, 259 familiarity, 201–203 Fegan, Neal, 167–168, 177, 259 fences, 20 fixed capacity, 157 flexibility, of Hadoop, 55 focused, staying, 271 forecasting about, 41–45 analytically, 230 Forlie, Siv, 265–266 formal process, following a, 165–166 FRHI Hotels and Resorts, 177–178 function space revenue management performance metric, 161–164 future, planning your, 279 323 G genuineness, 229 global impacts, importance of, 274–275 goals, of total hotel revenue management (THRM), 172–175 grid computing, 57 guaranteed service levels, as advantage of cloud solutions, 59 guest journey, 225–229, 226f guest-centric revenue management, 169–172, 235–237 Gulrajani, Linda, 176–177, 271 H Hadoop platform, 54–55 Happel, Joerg, 38 Hendler, Rom, 262 High fixed costs, 157 Hilton Midtown, 259 Hilton Worldwide, 263 Hirko, Rhett, 271 hosted solutions, 59 hotels price and demand relationship in, 31f price optimization for, 29–36 HSMAI’s Certified Revenue Management Executive program, 11 hub-and-spoke model, 20 Hughes, Rich, 78–80 I IDeaS Reputation Influenced Pricing Module, 118, 153 IHG (Intercontinental Hotels Group), 39–40 images, 111 implementation speed, as advantage of cloud solutions, 59 in-database processing, 57 information, 201–203 in-memory processing, 57–58 324  ▸   I n d e x innovate phase, of technology implementation, 141f, 142 input data, typical to hotels, 48–49 integrate phase, of technology implementation, 141, 141f integrated data, for digital intelligence, 231–232 integrated marketing, revenue management and, 130–134, 132f intelligent demand management, 133, 133f Intercontinental Hotels Group (IHG), 39–40, 223 K Kayak, Kimes, Sherri, 158–159, 201–203, 256–259, 266–267 Kimpton Hotels, 263 L Las Vegas Sands, 142–143, 262 leg, 20 leisure travelers attribute levels and value assessments, 96–97, 97f demographics of, 95–96, 95f value perceptions of, 98–99 lifetime value calculations, 136 likelihood to respond, 136 linked rates, 35 lodging performance, 109–111, 210–211 Loews Hotels & Resorts, 118–119 Lomanno, Mark, 210–211 long-term, versus short-term, 211–212 Lotte.com, 243 low-hanging fruit, 166–167 low-variable costs, 157 loyalty, of business travelers, 101–109, 102f, 103f, 104f loyalty program, 241 M machine learning, 77 managed solutions, 59 market basket analysis, 136 market share, versus ADR, 215–219 marketing about, 128–130 achieving the vision, 140–142, 141f analytics and, 136 cautions about, 143–145 data and, 136 integrating revenue management with, 137–138 Las Vegas Sands, 142–143 revenue management and, 128–146 marketing optimization campaign management and, 132f, 133 customer analytics and, 132f, 133 marketplace, changing, 6–7 metadata, 79 Metasearch, microsegmentation, 136 MicroStrategy, 64 mobile presence, leveraging your, 240 money, price optimization and, 36–39 multicollinearity, 75–76, 77 multitenant nature, of SaaS solutions, 60 N Nair, Hari, 186–188 Nassetta, Chris, 223 negative reviews, power of, 94–99 negotiation skills, revenue management and, 257 network effect, 22 next best offer, 137 “Next Gen Traveler,” 223 Noone, Breffni, 88, 210 I n d e x   ◂  O objective, as element of optimization problems, 27 Occam’s razor, 74 Oliveira, Ivan, 41–45 online travel agents (OTAs), 7, 182 Onyx Hospitality Group, 121–123 operational constraints, aligning pricing with, 267–270 operations, involving, 167–168 opportunity, unknown guests as an, 227–228 optimal table mix analysis, 163–164 optimization, 27–28 See also price optimization optimize phase, of technology implementation, 141, 141f Orbitz, organizational structure, 280–282, 280f organizations, for revenue management, 264–277 OTAs (online travel agents), 7, 182 outcomes, monitoring, 166 overall value, assessing, 105–108 overbooking, overfitting, 75–76, 77 P parallel processing, 57 Payea, Brian, 111, 123–125 peers, staying connected to, 278 perceived quality, 89–90, 89f perceived value, 90–92, 90f, 92f performance drivers of, 165 establishing baselines of, 165 ratings and, 109 of websites, 240 performance metrics about, 159–164 function space revenue management, 161–164 restaurant revenue management, 160–161 325 personalization about, 222–224 advice for revenue managers, 246–247 digital intelligence, 229–231 getting started with, 239–242 guest-centric revenue management and, 235–237 integrated data for digital intelligence, 231–232 path to, 241–242, 242–245 profiling versus tracking behavior, 245–246 revenue management and, 233–239 of a vision, 224–229 Phillips, Robert Pricing and Revenue Optimization, 11–12 predicting, 230 predictive analysis, 230 predictive phase, 255, 255f prescriptive phase, 255, 255f price and demand relationship, in hotels, 31f price and pricing about, 86–87 aligning with operational constraints, 267–270 benefits of strategic pricing, 209–213 brand and, 195–203 as a consumer consideration, 87–101 impact on revenue management of, 93 leveraging revenue management in, 205–206 as a strategic tool, 191–215 strategy considerations for, 194–203 study on, 88–94, 100–101, 101f to support business strategies, 203–205 tips on being strategic in, 206–209 326  ▸   I n d e x price elasticity, calculating, 30–31 price endogeneity, 30–31 price fairness, 196–203 price optimization about, 18–19, 24–27 compared with revenue management, 36t defined, 29 for hotels, 29–36 money, 36–39 optimization, 27–28 yield management in airline industry, 20–22, 21f price sensitivity of demand, 26t variance in, 37 price transparency, 18 price-able demand, 25, 34–36 price-demand relationship, 37 Pricing and Revenue Optimization (Phillips), 11–12 profiling, versus tracking behavior, 245–246 profit optimization See total hotel revenue management (THRM) promotion demand, forecasting,137 proprietary data management, 232 prospect theory, 200–201 publicizing successes, 168–169 Q Qantas, 239 quality impact on revenue management of, 93 perceived, 90–92, 90f, 92f of reviews, 111 study on, 88–94 R Rainmaker, 153 rate fences, building logical, 202 rate spectrum, analyzing, 207–208 ratings as a consumer consideration, 87–101 performance and, 109 Red Roof Inns, 212–213 reduced burden on IT, as advantage of cloud solutions, 59 reference price, 199–200, 202–203 regulatory issues, as advantages of on-premise solutions, 59–60 relational database, 52, 54 reputation impact on revenue management of, 93–94 at Loews Hotels & Resorts, 118–119 revenue management systems and, 113–119 restaurant revenue management performance metric, 160–161 revenue leaders, 10, 270–277 revenue management about, 128–130 achieving the vision, 140–142, 141f in airline industry, 20–22, 21f campaign management and, 132, 132f career paths in, 261–264 cautions about, 143–145 challenges in, compared with price optimization, 36t customer analytics and, 132, 132f developing strategies for, 165 evolution of, 7–10 evolving scope of, 8f framework for new applications of, 156–169 framework of, 70f future of, 251–279 guest-centric, 169–172 impact on, 93–94 incorporating customer value into, 138–140 I n d e x   ◂  integrated marketing and, 130–134, 132f integrating with marketing, 137–138 Las Vegas Sands, 142–143 leveraging, 205–206 limitations to, 134–136 at Loews Hotels & Resorts, 118–119 marketing and, 128–146 organizations for, 264–277 personalization and, 233–239 perspectives of, 41–45, 280–282 perspectives on, 78–82, 120–125, 147–150, 215–219, 247–249 process of, 41–45, 42f, 43f reputation and systems of, 113–119 research on, 256–259 value of, 11 revenue managers advice for, 246–247 profiles of, 254–261 Revenue Per Available Room (RevPAR), 159 Revenue Per Available Time-based Inventory Unit (RevPATI), 159 revenue strategy See total hotel revenue management (THRM) review sites, reviews as a consumer consideration, 87–101 number of, 110–111 power of, 93 quality of, 111 responses to, 109–110 RevPAR (Revenue Per Available Room), 159 RevPATI (Revenue Per Available Time-based Inventory Unit), 159 Roberts, Ann, 225–226 roles and responsibilities evolution of, 271 expanding, 275 327 S SaaS (Software-as-a-Service), 58, 60–61 sales, 147–150 Sanli, Tugrul, 80–81 SAS® Visual Analytics, 64 scalability, of Hadoop, 54 security, as advantage of cloud solutions, 59 segmentable markets, 157 selling strategies, building around system recommendations, 208–209 service experience, on path to personalization, 244–245 Shannon, Claude, 79 Sharma, Chinmi, 182–186 Sheraton Hotels, 263 short-term, versus long-term, 211–212 Silcock, Chris, 263 Silkunas, Leigh, 263 Singh, Tarandeep, 274–275 single line data collection insert, 231 “single version of the truth” database, 132–133, 132f social network analysis, 136 social web, evolution of, Software-as-a-Service (SaaS), 58, 60–61 Solomons, Richard, 223 solution delivery, innovations in, 58–61 Sorenson, Arne, 223 sources, for data, 113–115 Southwest, 194–195 speed, of Hadoop, 55 standardization, of SaaS solutions, 60–61 strategic pricing, benefits of, 209–213 “Strategic RM and the Role of Competitive Price Shifting” study, 210 strategic tool, pricing as a, 191–215 328  ▸   I n d e x strategic usage, versus tactical usage, 116 structured data, 52, 53f successes, publicizing, 168–169 surcharges, compared with discounts, 202 system recommendations, building selling strategies around, 208– 209 T tableau, 64 tactical usage, versus strategic usage, 116 talent shortage, 11, 259–261 Talluri, Kalyan, 75 The Theory and Practice of Revenue Management, 11–12 technology big data and innovations in, 51 evaluating offerings in, 240 evolution of, importance of, 274–275 revenue management and skills in, 257 Telenor, 243 The Theory and Practice of Revenue Management (Talluri and van Rysin), 11–12 THRM See total hotel revenue management (THRM) time perishable inventory, 157 time-to-value, of SaaS solutions, 60 time-variable demand, 157 total guest value, incorporating in revenue management, 137 total hotel revenue management (THRM) about, 152–155 beyond rooms, 155–169 components of, 175–178 goals of, 172–175 guest-centric revenue management, 169–172 total revenue performance See total hotel revenue management (THRM) tracking behavior, versus profiling, 245–246 training, 229 transparency, importance of, 203 Travel Click, 69 trends, 23 TripAdvisor, 5, 123–125 TripAdvisor Rank, 77, 110, 120–125 Trivago, Tune Hotel Group, 149–150 U UGC See user-generated content (UGC) unknown guests, as an opportunity, 227–228 unqualified demand, 25 unqualified transient demand, 23 unstructured data, storing, 52–56 user interfaces, in SaaS solutions, 61 user-entered data, unreliable nature of, 72 user-generated content (UGC) about, 86–87 lodging performance and, 109–111 role of, 93 study on, 88–94, 100–101, 101f V Vail Resorts, 244 value as a consumer consideration, 88 perceived, 90–92, 90f, 92f of revenue management, 11 value assessments attribute levels and, 96–98, 97f for business travelers, 103–105, 104f for leisure travelers, 96–97, 97f value perceptions of leisure travelers, 98–99 study on, 88–94 I n d e x   ◂  van Rysin, Garrett, 75 The Theory and Practice of Revenue Management, 11–12 variance error, 75 variety, big data and, 50 velocity, big data and, 50 vendor partners, evaluating, 240 Verizon, 194 visions achieving, 140–142, 141f personalization of, 224–229 visualizations, 63–67, 65f, 232 volume, big data and, 50 W Walmart, 194 webpage interactions, on path to personalization, 243 websites, performance of, 240 Wendy’s, 194 “what-if-ing,” 44 “Why Discounting Doesn’t Work” study, 210–211 Wiersman, Timothy, 212–213 Wolf, Stefan, 121–123, 218, 277 workflows, in SaaS solutions, 61 X Xuereb, Monica, 118 Y Yeoh, Melinda, 275 yield management See revenue management yieldable demand, 34 “yielding,” 18 Young, Nicole, 216 329 WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... 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 McGuire Human Capital Analytics:... evolutions are also impacting the way that data can be visualized and consumed New data visualization software puts the power to consolidate disparate data into a single source, and visualize that data... helping particularly SAS’s hospitality and gaming clients realize the value from big data and advanced analytics initiatives, to build a culture of fact‐based decision making Internally at SAS,