Praise for The Data Driven Leader “We need to think differently about the role of HR in business, and effectively using data and analytics to drive your business and talent strategies is now an imperative If you’re trying to understand how you can use data and insights about your talent for real business results, this is the book to read. The Data-Driven Leader describes practical ways you can use data to talk about your company’s most valuable assets— your people.” —Kelly Palmer, Chief Learning & Talent Officer, Degreed “The most effective leaders today are leveraging the incredible power of People Analytics to maximize talent in the organization The Data Driven Leader is a practical guide for business leaders and human capital professionals to immediately make an impact with analytics Dearborn and Swanson not only showcase where People Analytics can make the greatest difference, they include graphs, charts, checklists and step-by-step examples which can be put to use immediately. This is a must have resource for any leader of people.” —Kevin Oakes, CEO, Institute of Corporate Productivity “Dearborn and Swanson are keenly tuned into the traditional/ stereotypical HR mindset and equally insightful about the data-based transformation needed to make more effective decisions and highperforming organizations Data driven leaders of all functions experience greater cross-functional success when they begin with facts and master the art of drawing meaning from those facts. Every aspect of the internal and external customer experience will benefit from using data analysis to prioritize the ‘why’ above the ‘blame’ when solving problems.” —Carol Goode, SVP & CHRO at Brocade “The Data Driven Leader offers an engaging parable that brings to life the value of analytically-based people decisions, and helpful guidance for leaders to enhance those decisions.” —Dr John W Bourdreu, professor and research director, Center for Effective Organizations and Marshall School of Business, University of Southern California “The Data Driven Leader is a terrific call to action for HR leaders who want—and need—to be business leaders Jenny Dearborn and David Swanson offer a thoughtful argument for why complex business and people challenges demand analytics-based solutions, and more importantly, they provide practical tools to make it happen The future of work and HR is becoming increasingly analytics-based and multidisciplinary in nature, with a value chain that is morphing from big data to better insights to business outcomes.” —Ian Ziskin, president, Exec Excel Group LLC and former chief HR officer, Northrop Grumman and Qwest Communications “Jenny and David bring to light the best practices for leveraging data to drive critical talent decisions within an organization. The concept of designing a data-driven people strategy is spot on. Human Resources teams are now on the hook for driving measurable business outcomes This book provides the blueprint—a must read for any HR leader.” —Michael Rochelle, chief strategy officer and principal HCM analyst, Brandon Hall Group “I love the story-telling approach to analytical lessons that Jenny and David offer Their knowledge of (and commitment to) people, systems and data converge here in a must-read book for anyone interested in the future of HR and Leadership Even the non-numbers oriented, like me.” —Bill John, president & CEO, Odyssey Teams, Inc “Effective leaders care about truth: the organizations that they really have, not the ones they think (or wish) they have Moving a company forward requires patient and accurate insights into what people are really doing This valuable book provides practical and clever tools for this analytical work, demystifying the application of data analytics to HR processes that serve the entire company.” —Dr Charles Galunic, Aviva chaired professor of Leadership, professor of Organization Behavior, INSEAD Business School, FRANCE “We live in a world where data overload and data integrity is questioned every day Rather than running away from it, we need to boldly step up Consequently, the ability to synthesize information, analyze data and offer compelling insights and recommendations is one of the most critical skills required by the workforce of today The HR function has the opportunity to lead and make a huge difference in this space The Data Driven Leader is an easy-to-read, insightful book that provides great ideas for practical application across many parts of a business.” —Karen Gaydon, SVP, CHRO and Corporate Marketing, Synaptics “HR teams have a unique opportunity to transform how work is done through analytics, helping teams be more engaged and productive The Data Driven Leader provides the real-life examples and practical tips you need to begin applying HR analytics that drive better people and business results.” —Robert J Milnor, head of Planning Analytics and Reporting, Corporate Organizational Capability, Chevron “One of the key questions for the future is the impact of artificial intelligence on human work Here’s a great contribution to that debate Jenny Dearborn and David Swanson, through engaging examples and in-depth analyses, show how data analytics can properly empower the workforce for the future.” —Chris Anderson, head of TED “You can’t build an organization where people want to show up if you don’t truly know your people and you can’t truly know your people if you don’t have a people analytics strategy in place Start by reading this book!… A truly valuable resource that will help business leaders make sense of what people analytics is, why it’s crucial, and how to go about building it into your organization A must read!… Jenny is one of the world’s top minds when it comes to data and people analytics She has created a valuable resource that every business leader needs to read If you want to build an organization that is prepared for the future of work then you need to based on people data Start by reading this book!” —Jacob Morgan, author of The Employee Experience Advantage (2017), The Future of Work (2014) and The Collaborative Organization (2012), speaker and futurist “The combination of story-telling, explaining concepts, and illuminating practical application is really quite compelling This is an increasingly important space—the industry has been on a journey to embrace the available depth of information and transform it into a tool to help all aspects of our businesses Jenny and David an amazing job illustrating how modern data analytics can be applied to the HR function, and how this creates a broader impact to a company—empowering HR as a strategic asset to the business They take a technology space that is relatively new and remove the mystery from it and make it understandable, tangible, and something that can be put into practice.” —Quentin Clark, software executive, advisor, investor, limited partner “One of the most profound changes in business today is the vast amount of people-related data we have to analyze This book will help HR and line managers understand their opportunity to apply analytics to many of the people decisions we make every day.” —Josh Bersin, industry analyst, principal and founder, Bersin by Deloitte “At the heart of The Data-Driven Leader is a simple yet powerful insight: that the best way to unleash the collective power of an organization is to unlock the full potential of individual employees working in concert By connecting data analytics with the rapidly evolving practice of human resources, Jenny Dearborn and David Swanson have created a guide to driving business value that every leader—and not just those in HR—should read.” —Mike Prokopeak, vice president and editor in chief, Human Capital Media, publishers of Chief Learning Officer, Talent Economy, and Workforce magazines “At the convergence of people and technology there exists a great opportunity for talent development leaders to shape the future of their organizations Data analytics are key to that effort, and in this book Jenny and David provide a practical guide for how to measure what really matters and use the information to transform business.” —Tony Bingham, CEO Association of Talent Development “Opinions are interesting but insight based on data are what leaders really need ‘What happened’ ‘Why did it happen?’ ‘What might happen?’ ‘What should we do?’ Answering these four questions effectively is what results driven leaders And more than ever, answering all four of them must be based on data and true insight; NOT just gut feel Organizations are often data rich and insight poor That state exists is because leaders often avoid the hard work of the hard work Having the discipline and skill to run an insight driven business is just that: darn hard work And the current expectation is that ‘Human Resource’ leaders (CHROs, CPOs, etc.) have the skill and tenacity to be data driven leaders in parallel to the same expectations for profit/loss leaders So how does a data driven Chief Human Resource leader act? Well in perfect timing to meet the needs of current ‘People Leaders’ everywhere, Jenny Dearborn and David Swanson have co-authored The Data Driven Leader The book’s protagonist is a newly minted CHRO, Pam Sharp, and she leads a company transformation through the most thoughtful application of data The authors a superb job of demonstrating how Pam Sharp and her HR team navigate the most profound business challenges with insight The narrative of the book gives us a story to embrace and hence specific examples to learn from Buy The Data Driven Leader and benefit from the rich narrative Pam Sharp takes you step by step through the world of insight development She shows that what’s in the way is the way? Follow Pam Sharp and we all might find the better route to becoming true data driven leaders!” —Lorne Rubis, chief evangelist, ATB Financial, Edmonton, CANADA “The Data Driven Leader is an essential guide for leaders who want to navigate complexity with brilliance and win in the new game of work This insight-packed book will show you how to ask the right questions, gather intelligence and enable your team to find the best answers.” —Liz Wiseman, New York Times bestselling author of Multipliers and Rookie Smarts, founder, The Wiseman Group “A good read for HR professionals planning to embark on analytics and don’t know where and how to start! I like how the book provided alternative perspectives to the performance indicators that HR departments are tracking currently and practical approaches to leveraging analytics to create solutions to add value to the business.” —Aileen Tan, Group CHRO, Singtel, SINGAPORE “Anecdotes or Analytics The Data Driven Leader gives voice to this choice that we all make daily in our decision-making process to recruit and retain, motivate and mentor, top talent that will be the best fit for each of our organizations Crisp, concise and compelling in her writing style, Jenny Dearborn delivers a book that—from cover-to-cover—is a must read for those of us determined to make a difference through the role of HR in our organizations.” —Carl Guardino, president & CEO, Silicon Valley Leadership Group Jenny Dearborn Dav i d S wa n s o n The DataDriven Leader A P o w e r f u l A pp r o a c h to Delivering Measurable B u s i n e s s Impa c t Through P e o p l e A n a ly t i c s Cover image: © John Lund /Getty Images Cover design: Wiley Copyright © 2018 by Jenny Dearborn 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, 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 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 the author shall be liable for damages arising herefrom For general information about our other products and services, 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 has been applied for and is on file with the Library of Congress ISBN 978-1-119-38220-1 (hbk) ISBN 978-1-119-38221-8 (ebk) ISBN 978-1-119-38222-5 (ebk) Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Index with experience in, 5–6 See also Big data; Data analytics stages; Exalted HR data analytics; HR (human resources); People analytics Data analytics capability guidelines: don’t rely solely on software, 195; education yourself on data analytics, 195; get the right expertise, 195; HR analytics centralized versus decentralized practice, 195–196; just it! to reap the benefits, 197; request recommendations but interview carefully, 195; selecting the best leadership as key, 193; staffing up, 193–195; trust and effective communications requirements, 196 Data analytics guidelines: be patient, 107; cast a wide net, 106–107; enlist key stakeholders early, 106; slow down to speed up, 106; start small and non-controversial, 105–106; understand the key players, 106 Data analytics journey: A: leadership development and succession planning, 208–210; B: learning management, 208, 211–213; C: performance management, 208, 214–216; D: talent acquisition, 208, 217–219; E: total rewards, 220–222; introduction to how to begin your, 207–208 Data analytics presentation guidelines: be prepared, 192; keep it simple, 191; prepare others, 192; speak the language of business, 192–193 Data analytics roadblocks: cultural constraints, 79; institutional constraints, 79; McKinsey Quarterly article (2015) on, 79–80 Data analytics stages: descriptive analytics, 52, 53, 55, 74, 75fig, 199–201; diagnostic analytics, 53, 54, 55, 74, 75fig, 201–203; predictive analytics, 53, 54–55, 74, 75fig, 203–205; prescriptive analytics, 53, 55, 75fig, 76, 205–207 See also Data analytics Data Driven: How Performance Analytics Delivers Extraordinary Sales Results (Dearborn), 44–45 Data scientists labor force, 194 Decision makers: benefits of using HR data analytics by, 20–21; how prescriptive analytics facilitates, 206–207; when data analytics are blocked by senior, 79 Decision making: CEB findings on lack of talent metrics to inform, 46; ensuring that Exalted change is combined with datadriven, 167; Pepperdine University’s use of data analytics for, 26, 28, 29; 235 Index prescriptive analytics facilitating good, 206–207 See also Problem solving Deloitte’s Global Human Capital Trends 2017, 77 Descriptive analytics: answering the “what happened?” or “why” question, 52, 55, 74, 200; description of the, 52; explanation and example of, 199–203; insight on common pitfalls of, 201; providing business insights into past trends, 53, 75fig Design thinking framework: combining feasibility, viability, and desirability, 30–32, 43; definition of, 30; Exalted’s adoption of a new approach using KPIs and, 41–46; on the hiring manager experience, 33–34, 35; on the job candidate experience, 31–32, 35; stepping into your customers’ shoes using the, 30, 42–43 Desirability (design thinking), 30–32, 43 Detractors, 143 Diagnostic analytics: answering “why did it happen?” question, 55, 74; description of, 54; explanation of and example of, 201–203; insights into using, 203; providing business insights into causes, 53, 75fig Drucker, Peter, 129 E Employee engagement: data showing financial benefits of, 179–180; Exalted’s Engagement Survey Feedback, 5, 122; research findings on benefits of, 143–144; sales rep attrition and levels of, 63–64, 88; as sales rep attrition ecosystem factor, 63–67, 88, 112–117, 149, 150, 184–188; sales reps retention connection to, 117–120, 122, 143–144; senior leadership disinterest in data on, 117, 139–140 See also Sales reps retention Employees: engagement survey feedback from, 5, 122; as Exalted’s ultimate customers, 14; improving the value chain by partnering with, 15–16; interaction analytics on behaviors of, 78 See also Sales reps (Exalted); Talent Employer brand: gathering data on improving Exalted’s, 64–65; high-quality candidates not accept job offers due to, 117; sales reps attrition factor of, 64, 65, 88; talent acquisition KPIs on, 218 Enablement: leadership development and succession planning, 208–210; Learning & Development asked for produce sheets on acquired products for, 141; learning 236 Index management supporting, 211–213; performance management supporting, 214–216; sales rep attribution ecosystem factor of, 60–61, 65, 112, 149, 150, 184–188; special coaching and training form of, 186, 188, 216; Stem Rep Attrition Key Analytics Initiative on development with, 152; what collected data indicates about attrition and lack of, 98–102 See also Learning & Development Engagement See Employee engagement Engagement survey Feedback (Exalted), 5, 122 Exalted board meeting: Anne’s support of Pam’s data during, 176, 182, 186, 191; Ashcroft’s challenge to the data presented by Pam, 177, 181–182, 183; Ashcroft’s resignation from the board, 189, 191; Bobby’s support of Pam’s data during, 176, 181, 186, 191; data linking engagement to financial benefits, 176–181; David’s support of Pam’s data and results during, 182–183, 191; introduction of new Enterprise Analytics department during, 183; presenting Talent Acquisition Initiative results to the, 188; sales rep attrition ecosystem data presented during, 183–188; summary of the, 190–191 Exalted Enterprise Analytics, 183 Exalted Enterprises: engagement survey feedback sent out by, 5, 122; gathering data on improving employer brand of, 64–65; improving their career portal, 34; Pam recruited as CHRO of, 2–6; talent challenge facing, 2–3; the value chain of, 14–16 See also Exalted strategic planning meetings Exalted HR: focusing on the value chain, 14–16, 19fig; Pam Sharp recruited as CHRO of, 2–6; presenting Global Shared Services (GSS) outcomes, 188; stopping The Blame Game and seeking solutions, 13, 17–19, 71, 136 See also Exalted HR strategic team; HR (human resources); Sharp, Pam (CHRO) Exalted HR data analytics: applying to sales rep attribution problem, 50–73; assessing and adjusting hiring process using, 30–46; Bobby’s confrontation with Pam over, 26, 27–29; Chloe brought in to share her expertise in, 50–73; dismal numbers of the, 6–8; engagement survey feedback, 5, 122; ensuring that change decision making is driven by, 167; finding and building allies for using, 237 Index 41–42, 168–170; identifying needed changes based on the, 112–144; initial steps of Pam’s team gathering, 84–108; initial strategic planning on how to improve the, 8–16, 17–18, 22fig; presenting results to the board, 176–193; sentiment analysis versus, 65, 117, 163, 171; Stem Rep Attrition Key Analytics Initiatives, 152–154 See also Data analytics Exalted HR strategic team: applying predictive analytics to improve sales rep performance, 148–173; discussion of the initial data findings on attribution, 87–108; identifying attrition ecosystem and needed changes, 112–144; initial steps of gathering data by the, 84–87; Pam on playing The Blame Game instead of solutions, 13–16, 17–18, 71, 136; presenting results to the board, 176–193; summarizing Pam’s three key ideas during initial meeting of, 22fig See also Exalted HR; Stem rep attrition plan Exalted senior leadership: Chloe’s findings on classic mistakes being made by, 138, 139–140; disinterest in employee engagement data by, 117–120, 122, 139–140; Engagement Survey Feedback sent out by, 5, 122; failure to connect retention to engagement by, 117–120, 122; getting their attention by presenting the business case, 140; on progress made with analytics discoveries, 148 See also Leadership Exalted strategic planning meetings: on applying data analytics to attribution problem, 50–73; on applying needed changes based on analytics, 112–144; on applying predictive analytics to performance, 148–172; for improving Exalted’s hiring process, 29–46; initial discussions with Pam’s team on their, 6–16; on initial findings of attrition data, 87–108; presenting results to the board, 176–193 See also Exalted Enterprises F Facebook, 64, 80 Feasibility (design thinking), 30–31, 43 Financial issues: data on Exalted’s low revenue, 7, 178; data showing engagement drives up margins, 179; data showing how engagement drives up revenue, 179–180; Exalted’s low margins, 7, 178; P&L (profit and loss), 5–6, 118, 121, 140; parallel decline of sales engagement, retention, and, 120, 143–144; 238 Index stock price, 7, 178–180 See also Compensation Friedman, Ted, 45–46 G Glassdoor, 64, 65, 166, 194 Global Human Capital Trends 2017 (Deloitte), 77 H Harvard Business Review, 45, 109, 118 Hasso Plattner School of Design Thinking (Stanford University), 42 Hiring managers: being wellorganized and resultsfocused, 34, 35; days to offer to low-rated candidates by, 114–115; design thinking to understand experience of, 33–34, 35; findings on ineffective job candidate screening by, 112–114; lowerrated candidates most likely to accept offers by, 116 See also Sales reps managers Hiring new reps: doing a retention risk analysis before, 152–154; providing training and tracking success for newly hired, 170–171; Stem Rep Attrition Key Analytics Initiative on, 152; Talent Acquisition Initiative for, 161– 165; using Exalted’s ideal sales rep DNA for, 153–154, 161, 163, 201–203, 206 Hiring process: Bobby’s perceptive of the sales reps, 56; Bobby’s preference for intuition and not data analytics for, 40, 172; data analytics applied to talent acquisition and, 217–219; design thinking framework applied to, 30–34, 35, 42–43; Exalted’s adoption of a new approach using design thinking/KPIs, 41–46; finding and building allies for data analytics, 41–42, 168–170; making it user-friendly and persuasive, 32, 35; making it well-organized and resultsfocused, 34, 35; as sales rep attrition ecosystem factor, 112–117, 149, 150, 184–188; Talent Acquisition Initiative to be implemented for the, 161–165; talent acquisition KPIs disconnect and poor outcomes of, 35–38, 40 See also Job candidates HR (human resources): building analytics capability guidelines for, 193–197; CEB findings on data on people processes kept by, 46; how to begin or improve analytics journey of your, 207–222; leadership development/succession planning data analytics used by, 208–210; learning management data analytics used by, 208, 211–213; the new era and opportunities 239 Index for, 17; people analytics used today by, 76–81; performance management data analytics used by, 208, 214–216; presenting data analytics results guidelines for, 191–193; recruiting and retaining talent role of, 2–3; talent acquisition data analytics used by, 217–219; total rewards data analytics used by, 220–222; trend of hiring people with P&L experience for, 5–6 See also Data analytics; Exalted HR; People analytics 112–113; hiring manager candidate rating, 114–117 Job candidates: days to offer for low-rated, 114–115; design thinking to understand experience of, 31–32, 35; hiring experience that is user-friendly and persuasive, 32–35, 218; how hiring managers rate quality of, 114–115; job offers not being accepted by highest-quality, 117; lower-rated as most likely to accept offer, 116; problem with screening, 112–113 See also Hiring process I IBM/HR.com poll (2015), 77 Ideal sales rep DNA: building on Trajectory’s ideal sales rep DNA, 59, 108; diagnostic analytics to identify the, 201– 203; hiring new reps based on Exalted’s, 153–154, 161, 163; prescriptive analytics to see how rep measures up to, 206; Trajectory’s data on KPIs impacting bookings data and, 56–58 The Innovator’s Dilemma (Christensen), 72 Input attrition variables, 202 J Job candidate screening: doing a retention risk analysis as part of the, 152–154; findings on ineffective Exalted’s, K KPIs (key performance indicators): data on Exalted’s sales rep attribution, 59–71; five steps between data analytics and identifying, 80–81; impacting bookings by Trajectory sales reps, 56–58; initial steps in adding to attrition factors list, 70; initial steps of Pam’s team gathering data on, 84–108; leadership development, 209–210; learning management, 212–213; need to focus efforts on the top sales drivers, 102–103; performance management, 215; prescriptive analytics to pinpoint rep’s deficiency in, 206; sales rep/KPI performance analysis 240 Index at Trajectory, 58; talent acquisition, 217–218; variable as another term for, 70, 202 See also Talent acquisition KPIs Kübler-Ross Change Curve, 142–143 L Leadership: as key to building data analytics capability, 193; leadership development and succession planning for, 208–210; underrepresented candidates for, 209 See also Exalted senior leadership Leadership development: data analytics applied to succession planning and, 208–209, 210; KPI comparison of efficiency and impact metrics, 209; underrepresented candidates for, 209 Learning & Development: asked to create product sheets on acquired products, 141; Bobby’s continued rejection of, 61, 99, 102; leadership development/succession planning by, 208–210; learning management by, 211–213; organizations that use completions as a success metric, 43–44; predictive analytics on at-risk producers failing to complete, 155, 158; special coaching and training through, 186, 188, 216; what collected data indicates about attrition and, 98–102 See also Enablement Learning management: comparison of KPIs for, 212; data analytics applied to, 211, 213 Lee, Martha (VP of HR Centers of Excellence): applying predictive analytics to improve sales rep performance, 148, 153, 157, 160, 166, 168; on demoralized employees, 8; on findings of initial attribution data, 87, 94, 98, 99, 102; on findings of the attribution data, 117, 122, 127, 130, 141; on improving Exalted’s hiring process, 32, 33–34, 38–39; on the sales attribution problem, 12, 13, 17–18; sales rep attrition data analytics contributions by, 51, 52, 54, 56, 61, 63, 70; strategic planning meeting with, 6–16, 17–18, 22fig Lewis, Michael, 109 LinkedIn, 64 Long, Marcus (VP of HR Business Partners): applying predicative analytics to sales rep performance, 148, 151, 152–153, 160, 161; on findings of attrition data, 117, 118, 122, 123, 126, 127, 130, 132, 134, 135–137, 138; on findings of initial attrition data, 84, 87, 90, 94, 102, 104; on improving Exalted’s hiring process, 31–32, 36–37, 241 Index 38, 40, 43; initial strategic planning meeting with, 6–16, 17–18, 22fig; on the sales attrition problem, 9–12, 16; sales rep attrition data analytics contributions by, 51, 54, 61–62, 63, 64, 67, 69, 71 Low producers (Exalted): failing to complete training courses, 153; predictions on at-risk, 156–158 See also Performers (Exalted); Top producers (Exalted) Margins: data showing engagement drives up, 179; Exalted’s low, 7, 178 McKinsey Quarterly, 79–80 Millennials job application preferences, 31, 32 Minority promotion rates, 209 Moneyball (film), 109 M O Mahal, Sameer (VP of HR Shared Services): applying predictive analytics to improve sales rep performance, 165–166, 168; board presentation on HR Global Shared Services’ success under, 188; on findings of initial attrition data, 87, 92, 94; on the findings of the attrition data, 117, 130; on improving Exalted’s hiring process, 31, 37–38, 40; on the sales attrition problem, 11, 12, 13, 17–18; sales rep attrition data analytics contributions by, 50, 51, 52, 53, 61, 62, 64, 69; strategic planning meeting with, 6–16, 17–18, 22fig; supporting Pam’s presentation to the board, 188 Managers See Hiring managers; Sales rep managers Oakland A’s, 109 “100 Best Places to Work” employer awards, 65 Operational reporting analytics, 76 Organizational network analysis (ONA), 78 Organizations: completions used as a success metric by L&D, 43–44; IBM/HR.com poll (2015) on business and talent strategies linked by, 77; importance of culture in, 129; roadblocks to the use of data analytics by, 79–80 See also Change Output attrition variables, 202 Overconfidence bias, 109 N New products See Products The New York Times’s “Age of Big Data,” 45 P P&L (profit and loss): serviceprofit chain, 118, 121, 140; 242 Index trend of hiring HR people with experience in, 5–6 People analytics: advanced analytics type of, 77; advanced reporting type of, 76–77; common roadblocks to effective use of, 79–80; good and bad news about, 78; operational reporting type of, 76; organizational network analysis (ONA) and interaction, 78; predictive analytics type of, 77 See also Data analytics; HR (human resources) Pepperdine University, 26, 27, 28, 29 Performance management: comparison of KPIs for, 215; data analytics applied to, 214–216 Performers (Exalted): activity and attrition of, 96; data on attrition rate of top performers versus, 90; defining top producers versus, 56; higher commissions than top producers, 95; unrealistic quotas driving attribution of, 94–98, 200 See also Low producers (Exalted); Top producers (Exalted) Porras, Jerry, 129 Porter, Michael, 18–19fig Predictive analytics: answering the “what might happen?” question, 55, 74; applied during sales rep lifecycle, 173fig; on at-risk high and low producers, 155–158; description, 54–55, 77; explanation and example of using, 203–205; improving sales rep performance through, 172; insights into using, 205; on managers at risk of losing reps, 159–160; Pam’s team starting process of applying, 148–172; providing business insights into predictions, 53, 75fig Prescriptive analytics: answering the “what should we do?” question, 55, 76; description of, 55; explanation and example of using, 205–206; to identify interventions for at-risk sales reps, 160–161; insights into using, 206–207; providing best course of action business insights, 53, 75fig Pricing: correlation between win rates and increasing, 125–126; data on attrition and impact of, 123; Exalted staying on course despite industry changes in, 123–124; impact on ability of sales reps to sell, 61–62; sales reps attrition ecosystem factor of, 123, 149, 150, 184–188 Problem solving: benefits of using HR data analytics for, 21, 169–170; understanding the problem first for effective, 130 See also Change management; Decision making 243 Index Rodriquez, Anne (chief marketing officer): introduction to the, 5; responding to data access request, 84–87, 104, 105; responding to findings of attrition data, 133–135, 136, 141–142; support of Pam’s presentation to the board by, 176, 182, 186, 191; on Thomas Ashcroft’s strategies as mentor to her, 85–86, 104, 133, 134, 135 Products: Bobby’s complaints about Exalted’s new, 131; challenges of changing Exalted’s, 127–128; data on Exalted’s low revenue from new, 7, 178; data showing how engagement increases revenue from new, 179–180; failure to use change management supporting new, 128, 140–141; Martha’s Learning & Development product sheets on new, 141; as sales reps attrition ecosystem factor, 126–127, 149, 150, 184–188 PwC data analytics survey, 20 S Q Quota attainment: data on unattainable top producer, 97, 200; data on unrealistic Exalted, 94–98; as key driver of sales reps attribution ecosystem, 94, 112, 149, 150, 184–188; predictive analytics on at-risk high producers and their, 155 R Retention risk analysis, 152–154 Retention See Sales reps retention Revenue: data on Exalted’s low, 7, 178; data showing how engagement drives up, 179–180; parallel decline of sales engagement, retention, and, 120, 143–144 Sales rep attrition: collecting data on, 59–71; correlation versus causation of, 92, 109–110; employer brand factor, 64–65, 88, 117; high rate of past, 7, 178; top producer versus performer, 56, 90, 94–98, 155–157, 200, 215; total rewards data analytics applied to decrease, 220–222 See also Sales rep attrition ecosystem; Stem rep attrition plan Sales rep attrition data: collecting and team discussion of the, 84–108; connecting the dots of the, 107–108; correlation versus causation issue of, 92, 109–110; determining which variables most impact Trajectory, 58; diagnosing the “why” of, 112–144; evidence on value of enabling development, 98–103; Exalted’s high 244 Index attrition rate, 7, 59–71; the four-cell matrix on data to collect, 66–68, 89; on KPIs impacting bookings at Trajectory, 56–58; need to focus efforts on the top drivers, 102–103; on performers, 56, 90, 94–98, 200; sales rep attrition ecosystem based on the, 93, 98, 104; on top producers, 56, 90, 94–98, 155–157, 200, 215; total sales rep bookings, 57; on unrealistic Exalted quotas, 94–98, 200 Sales rep attrition ecosystem: compensation, 94–96, 112, 128, 149, 150, 184–188; enablement (learning & development), 43–44, 60–61, 65, 98–102, 112, 149, 150, 184–188; engagement, 63–67, 88, 112–117, 149, 150, 184–188; ineffective hiring practices, 112–117, 149, 150, 184–188; managers, 61, 98–102, 112, 149, 150, 184– 188; pricing, 61–62, 123–126, 149, 150, 184–188; product, 126–127, 149, 150, 184–188; quota attainment, 94–98, 112, 149, 150, 184–188, 200 See also Sales rep attrition; Stem rep attrition plan Sales rep lifecycle, 173fig Sales rep managers: Bobby’s rejection of leadership development for, 61, 99, 102; comparing attrition of hired versus promoted, 101; comparing attrition of new and seasoned, 98–100; as factor in sales rep attrition ecosystem, 112, 149, 150, 184–188; need to provide enablement development to, 102–103, 112; performance management by, 214–216; predictive analytics on risk of losing reps, 155, 158, 159 See also Hiring managers Sales rep performance: applying predictive analytics to improve, 172; building on Trajectory data analytics to improve Exalted, 59, 108; data analytics applied to management of, 214–216; as factor in attrition, 60, 62, 65, 88; top producer versus performer, 56, 90, 94–98; total sales rep bookings, 57; Trajectory’s data on KPI impacting bookings, 56–58; unrealistic Exalted quotas measurement of, 94–98, 200 Sales rep retention: data showing engagement increases, 179–180; doing a retention risk analysis on, 152–154; employee engagement role in, 117–120, 122, 143–144; parallel decline of sales engagement and, 119; parallel decline of sales engagement, revenue, and, 120, 143–144; service-profit chain on, 118, 121, 140; Stem Rep Attrition Key Analytics Initiative on, 152 245 Index Sales reps (Exalted): Bobby’s perceptive of the problems in hiring, 56; Bobby’s preference for intuition when hiring, 40, 172; building on Trajectory data analytics to improve, 59, 108; complaining about high pricing, 61–62; findings on ineffective hiring of, 31–35, 112–117; ideal sales rep DNA of, 56–58, 59, 108, 153–154, 161, 163, 201–203, 206; introducing design thinking/KPIs approach to hiring, 30–46; lifecycle of, 173fig; performers, 56, 90, 94–98; talent acquisition KPIs disconnect with success metrics of, 35–38, 40; top producers data, 56, 90, 94–98 See also Cash, Bobby (chief sales officer); Employees Sales reps (Trajectory): building on ideal sales rep DNA to solve Exalted’s problems, 59, 108; determining which variables most impact, 58; KPI impacting bookings data at, 56–58; total sales rep bookings made by, 57 School of Information Studies (Syracuse University), 45 Selection bias, 201 Senior leadership See Exalted senior leadership Sentiment analysis, 65, 117, 163, 171 Service-profit chain, 118, 121, 140 Sharp, Pam (CHRO): applying data analytics to sales rep attribution problem, 50–73; asked by David to succeed him as CEO, 189–190; asked to head the change management task force, 167, 168; beginning strategic planning with her team, 6–16, 17–18, 22fig; Bobby’s confrontation over data analytics with, 26, 27–29; confidence in David’s support by, 71, 84, 137–138; on findings of attrition data, 118, 122, 126, 127–128, 130, 131, 132–133, 134, 135, 136–137, 139, 140, 142; on improving Exalted’s hiring process, 29–46; initial steps of gathering KPIs data and findings, 84–108; introducing frameworks for a new way of thinking, 29–40; leading her team to apply predictive analytics to sales rep performance, 148, 151, 155, 161, 164, 165, 168; leading her team to start using predictive analytics, 148, 151, 155, 161, 164, 165, 168; on not playing The Blame Game and focusing on solutions, 13–16, 17–18, 71, 136; Pepperdine University’s basketball team record of, 26, 27; presenting data analytics results to the board, 176–177, 181, 183–188, 246 Index 189, 190–191; recruited as Exalted’s CHRO, 2–6, 17; summary of the three key ideas by, 22fig See also Exalted HR Stakeholders: acquiring buy-in to change management from, 142; data analytics and support of key, 106; finding allies for data-driven change among the, 41–42, 168–170; Pam’s comment on their service to Exalted, 130 Stanford University’s Hasso Plattner School of Design Thinking, 42 Stanton, Jeffrey, 45 Status quo bias, 110 Stem Rep Attrition Key Analytics Initiatives, 152 Stem rep attrition plan: Bobby agrees to collaborate on the, 151, 153, 154–155; predictive data analytics on at-risk sales reps and hiring managers, 155–161; presenting results to the Exalted board, 176– 193; Stem Rep Attrition Key Analytics Initiatives, 152–154; Talent Acquisition Initiative, 161–165 See also Exalted HR strategic team; Sales rep attrition; Sales rep attrition ecosystem Stock price: data showing engagement benefits for, 179–180; Exalted’s low, 7, 178 Strategic planning See Exalted HR strategic team Success metrics: completions used by L&D professionals as, 43–44; lack of correlation between Exalted’s KPIs and, 36–37, 40; using outcomefocused KPIs to better match Exalted’s, 39–40, 41–44 Succession planning: data analytics applied to leadership development and, 208–209, 210; leadership development KPI comparison, 209; underrepresented candidates for, 209 Syracuse University’s School of Information Studies, 45 T Talent: Exalted’s loss of key, 2–3; Exalted’s strategic planning discussion on how to hire and retain, 8–16; IBM/HR.com poll (2015) on organizations linking business outcomes to strategies on, 77; importance of having the right people and, 3; Pam’s presentation to board on building a robust talent pipeline, 183–188; Pepperdine University’s use of data analytics for identifying, 26, 28, 29; Talent Acquisition Initiative to hire new sales rep, 161–165 See also Employees Talent acquisition data analytics: comparing efficiency and impact metrics, 218; how to apply to hiring process, 217, 219 247 Index Talent Acquisition Initiative (Exalted): for hiring new sales reps, 161–165; on an improved hiring process, 162; presenting to the board results of implementing, 188 Talent acquisition KPIs: comparing efficiency and impact metrics, 218; disconnect from hiring process, 35–36, 40; Exalted’s adoption of a new approach using design thinking and, 41–46; improving Exalted’s results using outcomefocused, 39–40, 41–44; lack of existing success metrics correlation to, 36–37, 40; poor outcomes of the usual hiring approach using, 37–38, 40 See also KPIs (key performance indicators) Top producers (Exalted): activity and attrition of, 96; data on attrition rate of performers versus, 90; data on unattainable quotes of, 97, 200; defining performers versus, 56; KPI’s for identification of, 215; lowest commissions and highest attribution of, 95; predictions about at-risk high or, 155–157; unrealistic quotas driving attribution of, 94–98, 200 See also Low producers (Exalted); Performers (Exalted) Total rewards professionals: description and functions of, 220; how analytics can be applied by, 221–222; total rewards KPI comparison, 221 Trajectory sales reps See Sales reps (Trajectory) U Underrepresented leadership candidates, 209 United Center (Chicago), 26, 190 Unstructured data: the fourcell matrix on, 66–68, 89; sentiment analysis using, 65, 117, 163, 171 U.S data scientists labor force, 194 V Value chain: Michael Porter’s, 18–19fig; partnering with employees to improve, 15–16; sketch of Exalted, 14 Variables: as another term for KPIs, 70, 202; Chloe’s definition of output and input attrition, 202–203; determining those that most impact Trajectory, attrition data, 58 Viability (design thinking), 30–31, 43 W Wheeler, Shep, 26, 28, 29 Women’s promotion rates, 209 Z Zenith Co., 123, 124, 126 248 WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... Degreed The most effective leaders today are leveraging the incredible power of People Analytics to maximize talent in the organization The Data Driven Leader is a practical guide for business leaders... about this trend That might quell some of the anxieties she The Data Driven Leader imagined they’d have about a sales executive becoming their leader and the CHRO Unfortunately, based on her first... Bersin by Deloitte “At the heart of The Data- Driven Leader is a simple yet powerful insight: that the best way to unleash the collective power of an organization is to unlock the full potential of