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Talent Analytics: Maximizing the TA Value

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All things metrics, analytics, reports, scorecards, and how to tell the story to influence change with recruiting data.

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Talent Analytics: Maximizing the TA Value

  1. 1. Talent Analytics. Maximizing the TA Value “After my wife, Data is my best friend
  2. 2. Current State – Rearview Mirror Metrics • Discussion Agenda Future State - Predictive analytics and causality metrics • Discussion Telling Stories with the metrics • Discussion Optimal scorecards, reporting and KPI’s • Discussion Roadblocks to success - Data Integrity and how to fix it • Discussion Benchmarking with real data – Brightfield Tool • Discussion
  3. 3. “Without data, you are blind and deaf and in the middle of a freeway.” – Geoffrey Moore
  4. 4. Let Me Tell You A Story My Career A-ha Moment!
  5. 5. My Career A-ha Moment! Facts = Data… Data = Credibility… Credibility = Trust… Trust = Partnership
  6. 6. Current State – Rearview Mirror Metrics • Discussion Agenda
  7. 7. Current State Most Recruiting Metrics are still about looking in the rear view mirror
  8. 8. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt
  9. 9. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt We currently use an analytics solution 19% 23% 52% 6% Data & Analytics
  10. 10. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt We currently use an analytics solution 19% 23% 52% 6% We have moved beyond basic data reporting 12% 29% 48% 11% Data & Analytics
  11. 11. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt We currently use an analytics solution 19% 23% 52% 6% We have moved beyond basic data reporting 12% 29% 48% 11% We have a formal dashboard 31% 16% 43% 10% Data & Analytics
  12. 12. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt We currently use an analytics solution 19% 23% 52% 6% We have moved beyond basic data reporting 12% 29% 48% 11% We have a formal dashboard 31% 16% 43% 10% We benchmark our KPIs 29% 19% 39% 13% Data & Analytics
  13. 13. Data & Analytics We do this today We think we need to but are not quite sure how, why or the value We see value and plan on doing in the next 18 months We see no value and are not going to adopt We currently use an analytics solution 19% 23% 52% 6% We have moved beyond basic data reporting 12% 29% 48% 11% We have a formal dashboard 31% 16% 43% 10% We benchmark our KPIs 29% 19% 39% 13% We have a dedicated resource 26% 26 % 28% 20% Data & Analytics
  14. 14. Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured
  15. 15. 32% CPH 31% Diverse Hires 31% Hiring against Demand Plans 28% TA KPI Scorecard >40% Retail >40% Manufacturing Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured Q: What Doesn’t get Tracked or Measured
  16. 16. 32% CPH 31% Diverse Hires 31% Hiring against Demand Plans 31% Funnel Metrics 40% HM Satisfaction 34% Sourcing Time vs Business Time 28% TA KPI Scorecard Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured
  17. 17. 32% CPH 31% Diverse Hires 31% Hiring against Demand Plans 43% Social Media Engagement 31% Funnel Metrics 40% HM Satisfaction 34% Sourcing Time vs Business Time 28% TA KPI Scorecard 44% Candidate Satisfaction Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured
  18. 18. 32% CPH 31% Diverse Hires 31% Hiring against Demand Plans 43% Social Media Engagement 31% Funnel Metrics 40% HM Satisfaction 34% Sourcing Time vs Business Time 28% TA KPI Scorecard 44% Candidate Satisfaction 46% Quality of Hire Reporting Analytics and KPIs….What Doesn’t get Tracked or Measured
  19. 19. A Staffing.org CEO Survey rated new hire quality as the #1 most important performance metric out of 20 possible metrics. It was rated 9.6/10
  20. 20. Quality of Hire (QoH) = (APR + AE + HMS + ER) / N APR = Avg. Performance Rating for new employees in first 12 months AE = Employee Performance as a % of Achieves Expectations of performance in first year. HMS = Annual Hiring Manager Survey Q:“Overall quality of New Hires” ER = % of Employee Retention first 12 months of employment. N = Number of indicators used. APR= 68% + AE= 94% + HMS= 80% + ER= 90% / N = 4 QoH = 83%
  21. 21. QoH
  22. 22. Data Compression & Perception Highest = 83% Lowest = 62% Performance Management New Hires QoH
  23. 23. 25 Retention/Attrition Performance Promotions Business Satisfaction 1 2 3 4 Promotions
  24. 24. Business Accountability Recruiter Accountability Biggest lesson learned?
  25. 25. Number of candidates submitted to the business that they accept as a % (Recruiter Accountability) + % of candidates employed (Retention) in their first 12 months of employment (Business Accountability) divided by these two data points. 1 2
  26. 26. 1,000 Submittals 800 Acceptances 80% First Year Retention 90% + Two Data Points (80% & 90%) = 85% First Year Quality (FYQ)
  27. 27. Discussion What other metrics are you tracking and why? Does the business want metrics that you can’t provide, and why not?
  28. 28. Agenda Future State - Predictive analytics and causality metrics • Discussion
  29. 29. Future State Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends
  30. 30. Profile ‘A’ Pipeline Robustness Recruiter Screen Business Interview 1 Business Interview 2 Offer Hire 100 90 80 70 60 50 40 30 20 10 10:1 7:1 4:1 2:1 1:1 Profile ‘A’ Historical Throughput Benchmark Target # of Candidates Actual Candidates
  31. 31. 100:1 30:1 10:1 8:1 3:1 1:1 Full Funnel Throughput (FFT) Applications Recruiter Screens Hire HM Accepts Final Interviews Submittals
  32. 32. 100:1 30:1 10:1 8:1 3:1 1:1 Full Funnel Throughput (FFT) Tele-Sales Java Developers Job Families Store Mgr’s 55:1 30:1 100:1
  33. 33. 100:1 30:1 10:1 8:1 3:1 1:1 Alert 20 more Quality Candidates needed this week to fill the 5 Tele-Sales positions by end of the month Full Funnel Throughput (FFT)
  34. 34. Predicting Which Reqs Will Be at Risk • Chance of the req being filled by its goal drops dramatically if there is no candidate submission by the end of week two or no candidate interview by week three.
  35. 35. - Approach to Role Difficulty
  36. 36. Speed Quality Cost Req Load Predictive Metric Causality Example Better Quality impacts longer hiring times and increases cost
  37. 37. Discussion What other predictive TA metrics are you tracking? What predictive or causality metrics would you love to get your hands on?
  38. 38. Agenda Telling Stories with the metrics • Discussion
  39. 39. Telling Stories with Data “Stories are the single most powerful weapon in a leader’s arsenal. —Howard Gardner, Harvard University “Why was Solomon recognized as the wisest man in the world? Because he knew more stories (proverbs) than anyone else. Scratch the surface in a typical boardroom and we’re all just cavemen with briefcases, hungry for a wise person to tell us stories. —Alan Kay, vice president at Walt Disney
  40. 40. The most important TA metrics: • Does it make me money? • Does is save me money? Show me the money
  41. 41. Rewire Your Brain To Recognize The $$$$ Stories
  42. 42. Business Executive: “It costs too much money to hire people !
  43. 43. Business Executive: “It costs too much money to hire people ! Talent Acquisition Executive: “Compared to what?
  44. 44. Back of the napkin math stories Example: Hiring Time and Investment 1. We hire 5,000 people a year. 2. At an average of 5:1 (Candidates interviewed to produce one hire). 3. We have 5 different business interviewers involved on average for each candidate going through the interview process. 4. Each business interview on average lasts 60 minutes (early screens a little less, later interviews potentially more), but lets be conservative and call it 30 minutes on average. 5. The average fully loaded hourly rate for a business interviewer is $70, but even being conservative here as well, lets say 30 minute interviews. So that's $35 per hour. 6. 1 candidate gets 5 business interviews x 5 candidates per req = 25 interviews per req.
  45. 45. 5,000 hires (reqs) x 25 interviews on average per req = 125,000 interviews a year. 125,000 x $35 per hour = $4,375,000 in time spent interviewing. If we (Recruiting and Business) could improve our throughput ratio down to 4:1 then the financial impact is a $875,000 less. If we (Business and HR) could create a more effective interviewing and assessment framework which required 1 less business interviewer on average, then that is an additional $700,000 in savings as well. If we could give the business back 22 thousand interview hours going forward, what would you do with them?
  46. 46. 1. Problem we/you are trying to solve 2. Benefit we will get from solving this problem 3. How you are progressing against the plan to solve it (on track/off track) 4. The issues causing you to be off track 5. What are you doing about resolving the issues that get you back on track, and by when 5 Simple Story Telling Rules
  47. 47. Look for the Data Outliers Best Practice Opportunity to share, discuss and apply elsewhere? Challenge or Opportunity to discuss, fix and improve?
  48. 48. Contrast and Compare = Bad = Good Golden Rule No. 1 Don’t compare datasets that are not directionally the same. Example: Your Companies Cost Per Hire to your Competitors if you/they calculate differently. Golden Rule No. 2 Wherever possible compare your dataset to something else. A metric and data point standing alone by itself tells very little. Example: Compare your Cost Per Hire this year/month with last year/month. The Story ‘Did it go up or go down, why and what are you doing about it’?
  49. 49. Different Analytical Recruitment Stories Cost  Cost Per Hire comparisons and outliers vs. other Workforces/Job Families  ROI on different Sourcing Channels and Outliers  Proactive vs. Reactive Sourcing results and Outliers vs. other Workforces/Job Families  Lost Opportunity Cost = Financial impact for unfilled roles Speed  Time to Hire comparisons and outliers vs. other Workforces/Job Families  Time to Source (Recruiting) vs. Interview Time to Hire (Business) and outliers by Workforces/Job Families = RvB Metric. Quality  New Hire Performance & New Hire Managed Attrition (1st 12 months)  Offer Acceptance and outliers over time vs. other Workforces/Job Families  Productivity Throughput Ratio’s (Submits to Hire) vs. other Workforces/Job Families + Operational Effectiveness  Hires against a annual or monthly business/recruiting demand plan  Tracking against Recruitment Goals/KPI’s  Volume of recruiting resources/effort spent on Attrition vs. Growth roles Against Plan Against Time & % Against Goals
  50. 50. Story “We need to do x,y and z to increase the passive candidate initial response %” “We need to do x,y and z to help with efficiencies around active candidate screening to determine quality earlier on in the process” Throughput Analysis: ‘Cradle to Grave Metrics’
  51. 51. Target Company Throughput Target Company Total Candidates in database Total Hires % We reject Candidates % Candidates reject us % of hires to applications A 337 21 67% 9% (16:1) B 222 13 57% 8% (17:1) C 135 13 70% 8% (17:1) D 533 16 71% 10% (33:1) E 351 8 74% 7% (47:1) F 64 1 80% 1% (64:1) Story “Business says look at people from Company ‘F’ but the data does not support the value” “We found people coming from Company ‘A’ are more successful because of ‘x,y and z’. This must be our broad assessment criteria vs. just Target companies? Negative Disposition Trends Story “Can we improve our EVP to move the 60% rejection reason’s down?” “Can we look at more flexible travel arrangements for this profile?” “What additional relationship development programs can we put in place to keep connected to the interested but timing not quite right group?”
  52. 52. Comparative Source Analysis Agency vs. Job Board Hire Overlap Story “Opportunity to get more effective with our own Sourcing Channel Strategy and coverage to optimize costs and results” Comparative Source Analysis Job Board vs. Job Board Overlap Story “Certain jobs should only be posted on Job Board ‘X’” “Stop spending Business Group ‘C’ money on Job Board ‘X’ and reinvest elsewhere.”
  53. 53. Lost Opportunity Cost Stories 60 Creating a Scenario in our WFP tool, a positive shift in attrition by 4.2% Positively impacts Company Revenue by 3.2% (40million) Modeled Forecasting
  54. 54. Talent Mapping Stories
  55. 55. The Future is Big Data Stories ATS SEO CRM Performance Management HRIS Big Data Sourcing Tools Social Networks WFP Multi-dimensional Recruitment Stories
  56. 56. Discussion What are creative ways you have leveraged data to tell the story for change? What are the major roadblocks you have seen on enabling change with data?
  57. 57. Agenda Optimal scorecards, reporting and KPI’s • Discussion
  58. 58. Lots of pretty choices
  59. 59. April: Talent Acquisition Monthly Scorecard Status Enhanced employee referral program go live in Q2 SEO solution delayed to Q4 because of contract negotiations Productivity per Recruiter increases after training investment last year Technology Blueprint and SOW completed for new global ATS Key Performance Indicators Goal/Plan Actual Trending Speed 50 days Cost 2.5k Quality 78% Customer Satisfaction 80%+ Challenges FY Initiatives / Investments + to Goal < 10% of Goal > 10% of Goal • Speed = Core Technical roles in Texas and Germany problematic. Too many interviews and 2 groups extended delays in responding to TA. • Quality = First year attrition issues in sales roles, particularly in the Solutions Group • Customer Satisfaction = While above goal by 4% the Sales Organization is causing trending down. Opportunities & Plan • Speed = Working with SVP on plan to optimize interviews and reduce the decision time. Meeting scheduled 4/5 • Quality = Partnering with Talent Management & Business to create better interviewing/assessment guides. Additional onboarding tweaks being made by Talent Management. Targeting Q3 release. • Customer Satisfaction = Keeping an eye on this and setting expectation with leadership that we will not be trending up until after Sales issues resolved after Q3 On Track Off Track On Track On Track
  60. 60. Hiring Manager and Candidate Satisfaction 67
  61. 61. Transparency and Directional Correctness 68 Transparency 1. Be clear on how and where you collect your data source’s. 2. Be clear on why you are measuring and the benefit you are after by presenting the data. Directional Correctness 1. 100% Data accuracy is very hard to achieve. Pick datasets that are directionally correct. 2. In instances where the data is more opinionated vs. factually driven, make sure the message focuses on the directional story vs. just the numbers
  62. 62. Discussion What scorecard approaches have you found create the greatest impact? What KPI’s do you use to hold the business, HRBP’s and Vendors accountable?
  63. 63. Agenda Roadblocks to success - Data Integrity and how to fix it • Discussion
  64. 64. Roadblocks to success
  65. 65. Metrics Standardization
  66. 66. Still challenges with how recruiters use their ATS.
  67. 67. - Still multiple versions of the Truth - Companies all over the map with how they use ATS’s (or Don’t) - Some ATS’s are just plain useless in their functionality
  68. 68. Pivot Tables can be evil
  69. 69. What can you do to fix it?
  70. 70. “We also had a major problem with data accuracy because recruiters were using our system inconsistently. “First, we spent a month scrubbing our historical data, removing errors and compiling a sample of “clean jobs” with accurate metrics. “Second, we taught recruiters to “live and breathe” our applicant tracking system by holding mandatory, weekly job reviews for 3 months. Recruiters sat in a room for an hour and we would audit one job per recruiter (recruiters wouldn’t know which job until the meeting).
  71. 71. Correct ATS step & status workflow
  72. 72.  Goal and reward the right behaviors  Be Transparent – Create a data integrity report/scorecard  Create one version of the truth
  73. 73. Discussion What other approaches have you taken to fix people, process or technology dependencies associated to data integrity? Do you currently have a data integrity metric or KPI?, if no, why not?
  74. 74. Suggested Reference Materials • CareerXroads Colloquium of course  • David Green – People Analytics IBM • https://www.linkedin.com/in/davidrgreen/detail/recent-activity/posts/ • Your Intelligent Talent Acquisition Advisor Blog • https://intelligentta.wordpress.com/ • Analytics in HR Blog • https://www.analyticsinhr.com/ • Tucana’s Podcast series • https://tucana-global.com/category/podcast/ • hiQ Labs Podcast • https://www.hiqlabs.com/podcast/ Conferences: • Wharton’s People Analytics Conference • https://wpa.wharton.upenn.edu/conference/ • People Analytics World • https://tucana-global.com/people-analytics-world-2017/
  75. 75. Agenda Benchmarking with real data – Brightfield Tool • Discussion
  76. 76. End-user / Employer User Types  CW/TA Program Team Users – Routine operational benchmark look-ups  CW/TA Program Owners – Trend exploration, taxonomy improvement, strategy testing, overall function optimization  SWP Finance/HR Professionals – Worker type comparisons & optimization, location selection Talent Data Exchange (TDX) workforce analytics platform 84 Supplier / Support User Types  MSP/RPO Operations Professionals – Trend exploration, taxonomy improvement, strategy testing  VMS/ATS Implementation & Product Teams – embedded market benchmarks, conditional application behavior  Brightfield Consultants – efficiency & efficacy
  77. 77. 85
  78. 78. Live Demo of Talent Data Exchange (TDX)

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