Follow us: #LINKUS13. Talent Analytics: Big Data Benchmarks, Big Picture Answers

Follow us: #LINKUS13 Talent Analytics: Big Data Benchmarks, Big Picture Answers

Follow us: #LINKUS13 Presenters Jeff Facteau, Ph.D. VP, Professional Services, SHL Michael Griffin Executive Director, HR Practice, CEB Michael Blair Manager Recruitment Technology & Assessments, Sprint Mark Ludwick, Ph.D. Director, Selection & Assessment, TWC 2

Follow us: #LINKUS13 How H1N1 Helped Big Data Go Viral Jeff Facteau, Ph.D. VP, Professional Services, SHL 3

Introduction

Introduction What is big data? Massive Multidimensional Asset rather than bi-product Descriptive and predictive What is the value of big data? Insight driven decisions, actions, strategies

Introduction What is analytics? A discipline focused on: Extracting insight from data assets Informing business decisions and strategies Talent Analytics Specialty within HR organizations Integration of diverse data sources / systems Addresses key questions about talent relevant to the business

Session Objectives Learn how leading organizations are assembling their data and building talent analytics capability Understand how vast, differentiated data sources are being used to answer strategic talent-related questions Become familiar with the actionable insights that have been realized from talent analytics Understand how talent analytics helps to identify needs as well as inform solutions about an organization s talent management practices

Follow us: #LINKUS13 The Analytics Era: Transforming HR s Impact Michael Griffin Executive Director, HR Practice, CEB 8

An explosion of talent data Administrative and Compliance Data Talent Management Data Social Technology and Behavioral Data Volume of Data Employee Demographics Payroll Data Compliance Tracking Hire Dates Performance Evaluations Employee Engagement Survey Results Talent Assessment Results Employee Demographics Payroll Data Compliance Tracking Hire Dates Real Time Behavioral Data (e.g., e-mail habits) Passive Candidate Employment and Personal Preferences Past Experiences, Skills, Languages Professional and Social Networks Performance Evaluations Employee Engagement Survey Results Talent Assessment Results Employee Demographics Payroll Data Compliance Tracking Hire Dates 1980 2013 9

Sophistication alone is insufficient Beyond basic levels of analytic sophistication, additional investments focused only on improving technology and methodology sophistication yield minimal additional benefits. High While a basic level of analytic sophistication is necessary for impact further investments in sophistication alone yield low additional benefits. Talent Outcomes Include: Employee performance Quality of hire Employee engagement Employee retention Leadership bench strength Talent Outcomes Organizational average n = 108. Low Analytic Sophistication High 10

Three challenges to improving impact HR faces three key challenges to improving Analytic Impact. 1 2 3 Criticality Where Should I Focus Talent Analytics? Few Business Leaders Believe HR Analytics Focuses on the Right Business Questions Percentage of Business Leaders Capability How Do I Upskill My HR Function? Most HR Leaders Believe HR Staff Capabilities is a Barrier to Improving HR Analytics Percentage of Senior HR Leaders Credibility How Can I Increase Credibility of HR Data? Few Business Leaders Trust Talent Data and Insights from HR Percentage of Business Leaders Who Trust Talent Data 17% Agree 80% Agree 18% Agree n = 9,528. Source: CEB, CEB Global Labor Market Survey, 2013. n = 108. Source: CEB, CEB Corporate Leadership Council Analytic Survey, 2013 n = 9,528. Source: CEB, CEB Global Labor Market Survey, 2013. 11

The Analytics Era: Transforming HR s impact on the business 1 Criticality 2 Capability 3 Credibility Prioritize Critical Business Questions Apply Business Judgment to Data Science Drive End-User Ownership of Talent Analytics Prioritize the most scalable opportunities for business impact, rather than simply fulfilling on-demand data requests Reset goals for talent analytics to focus staff on business judgment Provide implications of decisions, don t prescribe solutions 12

Follow us: #LINKUS13 Big data in the real world Michael Blair Manager Recruitment Technology & Assessments, Sprint 13

Big Data in the Real World Integrated Talent Management Feeding the Need for Talent Analytics

An industry leading wireless company FORTUNE 100 company Annual revenues $35.3B in 2012 Serve more than 55 million customers Strong prepaid brand portfolio with Virgin Mobile USA, Boost Mobile, Assurance Wireless 4G LTE in more than 60 markets with more launching regularly* Nationwide 3G voice and data network National push-to-talk network, providing instant communications coast-to-coast Global IP network with reach to 165 countries Priority #1: Improve the customer experience Priority #2: Strengthen the brand Priority #3: Generate cash * See Sprint.com/network for details. 15

What is ITM?

Integrated Talent Management SPRINT JOB PROFILE Position Summary Position Work Details Education / Certification Previous Experience General Competencies General Skills Talent Acquisition External Talent Pools Internal Talent Pools Sprint Integrated Talent Management Conceptual Model My JOB PROFILE Work Details Key Competencies Key Skills Function/Organization Compensation - Job Family Definitions - Compensation Opportunities - Set Global Job Profile - Match Job Req s to Talent Profiles - Create Internal Talent Pools New Hires; Job Reclassifications Competency & Job Management JOB PROFILE Initial Profile Data on Hire Express Interest TALENT PROFILE Workforce Planning & Analytics Career Development - State Goals - Map to Desired Jobs - Evaluate Talent Gaps - Set Development Plans - Express Interest - Network w/hiring Managers Based on competencies Learning & Development Learning & Development Based on competencies Review & Revise Update Key Competencies, Achievements, Dev Plans Updated by Employee Expose attributes based on opt-in Social Media - Set Specific Development Plans based on Performance / Competency Gaps Performance Management - Review & Update Current Job Definitions & Key Competencies - Review Recognition - Evaluate Performance to Key Competencies Profile Management MY PROFILE Previous Employment Current job responsibilities Previous job responsibilities Education & Certification Languages Awards & Recognition Achievements Competencies Skills Learning Activities Development Plans Career Goals Personal Interests & Hobbies Groups - Expertise Location - Social Recognition - Group Connections 17

Retail Talent Analytics Sprint SHL/Corporate Executive Board March, 2013

Retail Talent Analytics Data-driven talent insights that improve business performance Uses data from multiple sources Unique applicants and hires between December 2009 and August 2012 > Applica(on data > Pre- hire assessment results > HR data > Engagement survey results > Job performance data (KPIs) 19

Talent Questions Recruitment: Do we identify and select high-quality candidates? Are there certain markets with higher-potential candidates? Where is Sprint finding top candidates? Are referral candidates higher quality? Performance What are non-assessment drivers of performance? Retention What are non-assessment drivers of retention?

High volume of candidates allows us to be selective, saves hiring manager time 41.30 Retail Candidate Process Flow: 41 assessments per hire Most filtering is at manager review and interview stages 12.17 2.60 1.04 1.00 Completed assessment Reviewed by manager Had Interview Received employment offer Offer Accepted % of total 100% 29% 6% 3% 2% % of previous N/A 29% 21% 40% 96%

At each stage through the interview, the process yields more Green zone candidates Overall Assessment Scores by Stage 25% 42% 63% 73% 74% 74% 33% 37% 27% 26% 26% Assessment Passed Assessment Interviewed Offer Extended Offer Accepted Retail candidates average assessment increase dramatically up to the interview but do not increase much after that.

Which markets yield the highest-scoring candidates? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 27 31 31 31 30 29 32 32 32 33 31 32 33 32 34 33 34 34 35 34 35 34 35 37 38 44 41 42 42 44 44 42 41 43 41 43 43 42 43 41 41 41 42 42 42 42 44 43 42 42 All above locations posted have more than 2,900 candidates 30 28 27 27 27 27 27 27 26 26 26 26 26 25 25 25 25 24 24 24 23 23 22 21 20

Retention rates relate to assessment scores by market 86.0% 85.4% 180- Day Reten?on Rate 81.7% 80.7% 78.2% 78.1% 67.0% Market 1 Market 10 Market 3 Market 4 Market 22 Market 21 Market 25 24

Referred Candidates Are Higher Quality Candidates and Employees Referrals Score Better on the Assessment 32% 25% Referral Hires are Better Performers Net Activations: +15% Accessory Revenue: +9% CSAT: +2% Referrals Stay Longer 42% 41% 26% 33% Yes N=17,090 No N=258,215

Bottom Line: Recruitment Drivers Results Markets vary in proportion of Green Zone candidates, with results relating to turnover Referrals are better candidates and better employees Opportunities Targeted recruiting strategies for markets with fewer Green Zone candidates Enhance and fully leverage employee referrals

Talent Questions Recruitment: Do we identify and select high-quality candidates? Are there certain markets with higher-potential candidates? Where is Sprint finding top candidates? Are referral candidates higher quality? Performance What are non-assessment drivers of performance? Retention What are non-assessment drivers of retention?

Overview of Performance and Retention Results Performance Retention (voluntary) 401k participation ** ** ** Retention (involuntary) New hire training ** * (0-60 days) * (0-180 days) Distance to work ** ** Engagement ** ** * (>240 days) Employee referral ** ** ** Assessment ** ** ** Performance Predictors Employee referral, Engagement, and 401k participation had similar effects. Retention Predictors 401k was twice as important as the next strongest result ** Significant correlation for most or all time periods between 0 and 360 days * Significant correlation for some time periods only

401k Participation is Related to Performance and Retention 100% 50% 1-year retention rates for 401k participants and non-participants 99% 51% Performance for 401k Participants vs. Others Net Activations: +12% Accessory Revenue: +8% CSAT: +2% 0% Non-participants 401k participants Opportunity: Playbook article to encourage 401k participation Store managers will be given talking points

New Hire Training is Related to Retention and Performance Employees attending 100% of new hire training had consistently better retention rates during the first year of employment The effect is strongest in first 180 days Training also related to productivity (Composite Score, Upgrades) Opportunity: Increase execution of Welcome to Sprint program. Manager engagement and support is critical and relates to employee engagement Encourage completion of full curriculum path

Engagement Drives Performance and Retention Highly engaged employees significantly outperformed their less engaged counterparts Engaged employees were also retained at a higher rate Opportunity: Manager is key to improving engagement Enhance and ensure staff meeting quality Reinforce coaching and developing consultants Encourage use of rewards, recognition, e-points, etc.

Retail Talent Analytics Summary Analytics confirm and extend our understanding of performance and retention drivers Analytics can identify and support opportunities to impact performance and retention Targeting recruiting strategies and resources by market Encouraging and enhancing employee referrals Communicating the value and impact of 401k program Considering commute for assignments and transfers Improving employee engagement

Follow us: #LINKUS13 How to recognize when your business is asking for Talent Analytics Mark Ludwick, Ph.D. Director, Selection & Assessment, TWC LINK content not available post-conference 33

Follow us: #LINKUS13 Questions and contacts Michael Griffin griffinm@executiveboard.com Michael Blair Michael.blair@sprint.com Mark Ludwick Mark.ludwick@twcable.com 34