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Crafting a talent analytics function and building strategic partnership
This document discusses building a talent analytics function and the speaker's journey in doing so at LinkedIn. Some key points: - The speaker discusses guiding principles for crafting a talent analytics function, such as identifying its core purpose, staffing it with a blend of skills, focusing on questions that create business value, investing in maturity, and prioritizing efforts. - Examples are provided of how the speaker focused on quick wins to build credibility, developed a framework to help stakeholders ask strategic questions, and is leveraging analytics across the talent lifecycle. - Case examples show how the speaker used analytics to help with engineering hiring needs, improve operational planning and retention, and identify critical skills and attractive hiring regions.
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Crafting a talent analytics function and building strategic partnership
- 1. Crafting a talentanalytics function and fostering strategic partnership November 2015 Will Gaker Talent Analytics
- 2. Agenda Introduction Case examples Building atalent analytics function Our journey so far…
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- 4. More than400M members in over 200 countries around the world Professionals are signing up to join LinkedIn at a rate of more than 2 new members per second. There are over 39 million students and recent college graduates on LinkedIn. They are LinkedIn's fastest-growing demographic. LinkedIn operates the largest professional network on the internet Regional membership 127M+ EMEA 96M+ Europe 78M+ Asia and the Pacific 15M+ Southeast Asia 7M+ DACH 17M+ MENA 55M+ LATAM
- 5. Total revenue advanced37% year-on-year to $780M 5 2015 Q3 distribution by revenue stream Premium Subscriptions 18% - $138M Marketing Solutions 18% - $140M Talent Solutions 64% - $502M Source: Linked.com 2015 Q3 Revenue
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- 8. 8 We are ateam of HR experts, data scientists, and consultants who help HR solve problems
- 9. A center ofexcellence for Strategy & Analytics 9 Business Operations & Analytics Talent Analytics BizOps Analytics Field Sales Online Sales Product Strategy Growth Marketing Strategy Talent Analytics sits in Business Operations
- 10. 10 Talent analytics isgetting bigger Source: LinkedIn self-reported member data based on job title, start date, and company
- 11. 11 …but still alot of room to grow Source: LinkedIn self-reported member data based on job title, start date, and company ©2015 LinkedIn Corporation. All Rights Reserved.
- 12. 12 Talent analytics professionals havea variety of skill sets Source: LinkedIn, estimate based on member reported job titles and skills
- 13. 13 4,500companies have employees focused ontalent analytics 43% of the Fortune 1000 have talent analytics teams 55% of all talent analytics functions have been started within the last 5 years 70% of all talent analytics functions only have 1-2 team members Source: LinkedIn, estimate based on member reported job titles and skills
- 14. Building a talent analyticsfunction
- 15. Guiding principles for craftinga talent analytics function Identify the core purpose of your team Staff your analytics team with a blend of skills Focus on questions that create business value Invest capacity to develop maturity Prioritize your efforts to maximize impact
- 16. Problem-solving focus drivesaction and impact at LinkedIn What is the core purpose of your team? Research Problem Solving Focused on methods Long maturity curve Business may not be ready for results May not help HR become more strategic Focused on impact Short maturity curve Helps the business where they need it Problem solving with HR build business acumen
- 17. Align skill mixto your core purpose 17 Reporting Business Intelligence Ad Hoc Reporting Data Mining / Cleaning Technical PM Consulting Process Improvement Performance Mgmt. Org Design Strategic Planning Analytics Statistical Modeling A/B Testing Survey Design Research ©2015 LinkedIn Corporation. All Rights Reserved. Staff your analytics team with a blend of skills
- 18. Focus on questionsthat create business value
- 19. 19 Business impact iscreated through behavior change, not by analytics alone ©2015 LinkedIn Corporation. All Rights Reserved. Impact Talent Analytics Maturity Level 1 Data Monkey Level 2 “Why?” Asker Level 3 Proactive Business Partner Level 4 Change Leader “I need this data tomorrow for the company All Hands” “How do we shape our talent strategy to meet our business goals?” “Help me understand what problem you are trying to solve?” “How do we reduce bias in our people decisions?”
- 20. 20 Invest capacity toimpact your organization across all three maturity curves Technological Maturity Stakeholder Maturity Analytical Maturity Integrated data and automated reporting Stakeholders ask strategic questions and act on results Predictive analytics Integrated systems but manual reporting Stakeholders ask strategic questions but don’t act Advanced analytics Cleaner data-entry but no integrations Stakeholders ask for insights Manual reporting with a few deep dives Messy data in disparate systems Stakeholders ask for reports Manual reporting and data cleaning1. 2. 3. 4.
- 21. 21 Prioritize your effortsand focus where you can have an impactImpact Org Readiness Focus NowFocus Later Avoid Automate
- 22. Our journey sofar…
- 23. 23 We focused ourefforts on quick wins What we had Business demand Our solution Analytics Infrastructure Reporting Team resource allocation Building the IT infrastructure is a long journey… Reporting will consume 100% of capacity and never be 100% accurate Prioritize quick wins that solve business problems to build credibility
- 24. 24 We developed aframework to help our stakeholders ask strategic business questions Data - Oriented Research questions need evidence in order to be answered Questions that are not data-oriented usually need to be more specific Objective Testable Specific Great questions do not have the desired answer built-in Make sure the answer to your question has the possibility of being positive and negative Great questions allow you to test your instinct Sometimes the greatest learnings happen when the answer is unexpected Specific questions focus the insights you are looking for and make them easier to find Broad questions can usually be broken down into several specific questions
- 25. 25 We are leveraginganalytics to be the connective tissue across our lifecycle Hiring Data-driven recruiting using LinkedIn data Monitoring org shapes to inform hiring strategy Onboarding Monitoring onboarding satisfaction and time to productivity Inclusion Using analytics and natural language processing to inform inclusion strategies Learning Measuring the impact of learning and identifying learning needs to inform content creation strategy Engagement Measuring and monitoring key drivers of engagement Retention Predicting attrition with advanced analytics and developing strategies to retain top talent Succession Measuring succession risk and creating strategies for reducing readiness time Performance Evaluating programs based on impact to performance
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- 27. How do weacquire the technical talent to meet our growth objectives? ©2015 LinkedIn Corporation. All Rights Reserved.
- 28. How many engineeringrecruiters do we need? Forecasted hiring needs # of Hires Headcount forecasts # of FTE 2015 2016 2017 2015 2016 2017
- 29. Are we hiringthe right mix of people? Org. shape has shifted over time % of Engineering FTE 2013 20142013 2014 Senior+ Mid-Level Entry-Level Hiring has focused on entry level… % of new hires
- 30. Partnered with HRBPand talent acquisition leads to double mid-level and senior hires # of new hires 1H 2014 1H 2015 Senior+ Mid-Level Entry-Level
- 31. How do weimprove operational planning & better retain top talent?
- 32. 32 We built analgorithm to forecast sales attrition using machine learning Future Attrition Rate Comp Predictors Background Predictors Role Predictors
- 33. Need to focuson retention strategy before predictive analytics 33 The attrition forecast matched the number that our HRBPs provided for headcount planning Attrition forecast (Algorithm) Q2 Q3 Q4 Attrition forecast (HRBPs) Q2 Q3 Q4
- 34. Enables predictive analyticsto be actionable and scalable 34 Developing retention playbook and then scaling forecast process X XXXXX X X O O OO OO O O O O
- 35. What are themost attractive regions to hire software engineers?
- 36. Supply of softwareengineers in region DemandforsoftwareengineersWhat is the supply and demand for software engineers? Seattle Chicago Boston Washington D.C. New York SF Bay Phoenix Houston Denver Philadelphia Dallas Toronto Raleigh-Durham Detroit Montreal Austin San Diego LA Atlanta Minneapolis High Low Low High
- 37. LI Profile features LIProfile Features Candidates from ATS Machine learning algorithm Classification model Classified profiles TrainPredict Used profile data to classify software engineers into tracks
- 38. Where do wefind critical skills? Engineering track concentration by region Below average Above average Systems & Infra Apps Data Mobile Eng Manager Eng Services OpsIT
- 39. Conclusion Talent Analytics is growingbut still maturing Focus on questions that create business value Build a team that helps HR solve problems Leap frog the maturity curve by finding quick wins
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