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Hr analytics – demystified!

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Hr analytics – demystified!

  1. 1. HR Analytics – Demystified! ARUN KRISHNAN, PH.D, FOUNDER & CEO, nFactorial Analytical Sciences
  2. 2. What is Analytics? information resulting from the systematic analysis of data or statistics
  3. 3. Since when have we been doing analytics?
  4. 4. Daniel Kahnemann & Amos Tversky System 1 System 2  Solve: -5 +2 x 2 + 9 /3 - 8 What words would you choose to describe her?
  5. 5. So why the buzz around analytics now? Technological advances - price / performance Pervasive digitization Artificial Intelligence, Machine Learning
  6. 6. Big Data and Analytics What is Big Data? " One bit more data than your system can hold" Source: www.cloudlendinginc.com
  7. 7. Analytics is a continuum … Complexity Perspective Low Past High Future BusinessValue Reporting What happened? Analysis Why did it happen? Monitoring What is happening now? Prediction What will happen?
  8. 8. ...and Analytics is a journey! Source: Applied Insurance Analytics, by Patricia Soparito
  9. 9. Analytics Domains Retail Sales Marketing Collections Telecom Financial Services Risk & Credit Consumer Behavior Fraud Supply Chain Talent / HR Pricing Web
  10. 10. Football Analytics AN EXAMPLE
  11. 11. What happened?
  12. 12. Why did it happen?
  13. 13. What’s happening now?
  14. 14. What could happen in future?
  15. 15. HR Analytics
  16. 16. Analytics is coming to HR! Source: www.bersin.com
  17. 17. Why HR Analytics? Measure & Manage "What gets measured, gets managed; what gets managed, gets executed" - Peter Drucker Linkage of Business Objectives to People Strategies HR Dashboards - SAP "To clearly demonstrate the interaction of business objectives and workforce strategies" Return on Investment - David Foster "The business demands on HR are increasingly going to be on analysis just because people are SO expensive" Performance Improvement - CedarCrestone "Global organizations with workforce analytics and workforce planning outperform all other organizations by 30% more sales/employee"
  18. 18. HR capability gaps are increasing Source: Deloitte Human Capital Report, 2015
  19. 19. HR Analytics - Much promise - wanting in rewards? Source: Deloitte Human Capital Report, 2015
  20. 20. The HR Analytics Continuum Complexity Perspective Low Past High Future BusinessValue Head Count Attrition Training Payroll reports Performance Tracking Requisition Tracking Turnover Ratio Accession Ratio Low performer management Promotion Ratio Hiring Fit Hiring No-shows Prediction Attrition Prediction Attrition Segmentation Employee Segmentation Candidate Stickability Prediction High Performer Segmentation Workforce Planning Informal Network Analysis Voice of Employee Analysis Recruitment Engagement Retention
  21. 21. What metrics do we typically track? Source: Bersin & Associates 2012 – US research
  22. 22. What metrics should we track? Recruitment Retention Performance Management Career Management Training Workforce Planning Comp & Benefits Org. Effectiveness
  23. 23. Measuring Human Resources Management  Over 100 different metrics across Hiring and Staffing Compensation and Benefits Training and Development Employee Relations and Retention
  24. 24. So how about some recruitment- related metrics to start with? Cost •Cost per hire •Source cost per hire •Advertising cost per hire •Agency cost per hire •Referral bonus per hire •Unsolicited no- cost per hire •Special costs per hire •Interview costs •Source cost per hire per interview •Sign-on bonus factor Time •Response time •Average response time per hire •Time to fill •Time to start •Referral factor Career Development •Job posting response rate •Job posting response factor •Job posting hire rate •Internal hire rate •Career path ratio - promotions •Career path ration - transfers Efficiency Metrics •Average interview length •Hire rate •Hit rate Quality •Quality of Hire •Recruiter Effectiveness
  25. 25. Detailed Case Study GOOGLE
  26. 26. The early days - Finding the right people  Spent hours screening resumes from job portals like Monster.com  Built an applicant tracking system that checked candidate resumes against a database of Googler resumes  Idea was to get more realistic "backdoor" references  Also looked at innovative ways to identify smart people The solution to the first riddle will land you at http://7427466391.com/. On this page you’ll find the following: “Congratulations. You’ve made it to level 2. Go to www.Linux.org and enter Bobsyouruncle as the login and the answer to this equation as the password.” f(1)= 7182818284 f(2)= 8182845904 f(3)= 8747135266 f(4)= 7427466391 f(5)= __________
  27. 27. Initial data analysis & insights •Academic grades did not correlate well with performance except for the first 2-3 years. Analysis •Stopped asking for academic transcripts except for fresh graduates. Actions •Did not see any discernible drop in performance because of this. Results
  28. 28. Initial data analysis & insights - 2 • Google's hiring was focused on minimizing "false positives", that is, candidates who looked good at first glance but turned out to be poor performers later. • Their hiring took a long time - 250,000 hours to hire 1000 people/year Analysis • Looked at referrals as a way of hiring great candidates. Actions • In the initial years - >50% of hires were through referrals Results
  29. 29. Employee referrals •The rate started to fall after 2009 Challenge •Could be because rewards weren’t high enough. Hypothesis •Google increased the reward for successful referrals thinking that it would help to bring up the referral rates Actions •They found however, that this brought NO change in the decline. Results •Rewards are extrinsic motivators •People were more motivated by intrinsic factors like pride in their place of work Analysis
  30. 30. Employee referrals • Exhausted known networks Challenge • Started using aided recalls Action • Volume of referrals increased by 33%! Results
  31. 31. Cultivating the best people • Requirement of ~300,000 referrals/year vs <100,000 they were getting Challenge • Realized that the very best people are not looking for work. They are happy Analysis • Rejigged their staffing team and equipped them with a home-grown tool called gHire to cultivate people across different organizations. Action • >50% of Google's hires are found by this in-house team! Results
  32. 32. Hiring the best people •People during an interview make up their mind in the first 10 seconds •Rest of the interview is spent finding corroborative evidence •CONFIRMATION BIAS! Challenge •Most interviews are unstructured. •Unstructured interviews can predict only ~14% of an employee's performance •Work sample test predicts ~29% of performance •General cognitive tests predict ~26% of performance •biased towards white males (at least in the US) ! •Structured interviews were found to be as good at predicting performance as cognitive tests Analysis [paper by Frank Schmidt & John Hunter] •Use a combination of behavioral and situational structured interviews with assessments of cognitive ability, conscientiousness and leadership •Identified key attributes essential for "Googleeyness" Actions •Consistent scoring mechanism that allows people to compare across interviewers. . Results
  33. 33. Hiring the best people - 2 •Hiring was taking too much time – median of 90-180 days Challenge •What should be the number of rounds of interviews? • Found that 4 interviews were enough Analysis •Brought down the number of interviews from 25 to 4 Action •Changed median time to hire from 90-180 days to 47 days! Results
  34. 34. Revisit assumptions - Then test!  Looked for people with high scores who were rejected  2010 - ran 300,000 rejected candidates through the system  Filtered 10,000 and chose 150  Hit rate of 1.5% > 0.25% - Google's hit rate  Tested False Negatives as well! Revisit program •Feed resumes of all past candidates through algorithm Common Keywords •Assess common keywords found Score resumes •Score keywords based on their occurrences in rejected vs successful resumes Test •Score resumes over next 6 months against weighted keywords
  35. 35. Predictive Analytics for Recruiting
  36. 36. Some Examples Best Buy Could precisely identify a 0.1% increase in employee engagement among employees at a particular store. This value was identified at more than $100,000 in the store's operating income. Oracle / Sprint Oracle was able to predict which top performers would leave and why. This information is now driving key global policy changes for retaining key performers. Sprint has identified the factors that best foretell which employees will leave after a relatively short time. Dow Chemicals Has evolved its workforce planning over the past decade, mining historical data on its 40,000 employees to forecast promotion rates, internal transfers, and overall labor availability. Dow uses a custom model to segment its workforce and calculates future headcount by segment and level for each business unit. Dow can engage in "what if" scenario planning altering assumptions on internal variables.
  37. 37. State-of-the art for Predictive Analytics in Recruitment Hiring Fit Models Candidate Stickability Predictions Hiring No- Shows Predictions Workforce Planning Personality Matches
  38. 38. Predictive Modeling - Watchouts!  All models are wrong! Some are less wrong than others  Predictive Models cannot be used to predict rare, black-swan events  Models can’t predict what is not already present in the training data.  Building the right model depends on the question that needs to be answered.  This in turn determines the data that needs to be gathered.  Even with enough data, we might not have the “right” data to build a good predictive model.  Exploratory data analysis and Feature Selection is an extremely critical part of the model building workflow.  Always check model performance using any of confusion matrix, p- values, ROC curve etc.  Keep updating your model as and when new data comes in.
  39. 39. Keys to success in HR Analytics Start with the business problem in mind Develop culture of data-driven decision making Empower line leaders Be transparent Analytics is a journey, not an end Don't wait for the perfect data You don't HAVE to automate everything - at least at first Deliver Actionable Business Information
  40. 40. Thank you for your patience!

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