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Talent Analytics - Opower

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Talent Analytics - Opower - Fall 2015 Conference

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Talent Analytics - Opower

  1. 1. Talent Analytics The Opower Story 1
  2. 2. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 2 Hello! Dawn Mitchell Director, Talent Acquisition @DawnJGMitchell Alan Henshaw Manager, Technical Recruiting @henshawsburgh Scott Walker Senior People Analyst @scottwalker521
  3. 3. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 3 About Opower What is Opower? Opower is the leading provider of cloud-based software to the utility industry. Our purpose is to accelerate the transition to a clean energy future. What do we do? We combine big data and behavioral science to motivate people to save energy. We also transform the way utilities relate to customers by improving customer engagement. Our Results We’ve saved 8 terawatt hours of energy, over 20 million lbs of CO2, and over $1 billion in utility bills (….and we’ve only penetrated 1% of the market). @Opower
  4. 4. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 4 What We’re Covering Our Journey ● Inspiration ● Analytics past and present ● Team performance ● Forecasting & budget Analytics Insights ● Integrated HR & TA data ● Wrap up
  5. 5. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 5 What Inspired Us?
  6. 6. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 6 Talent Analytics Maturity Model Level 1: Reporting Monkey Ad hoc, operational reports only “Can I get this data for tomorrow’s all-hands?” Level 2: Advanced Reporting Reports focus on benchmarks/trends “How has our time to fill changed over time?” Level 3: Proactive Analytics Solving talent challenges through data/statistical analyses “How do we staff our team for constantly shifting hiring needs?” Level 4: Predictive Analytics Using data to forecast future talent outcomes “How much attrition will we experience next year and how much $ do we need to eliminate time in empty seats?” 10% of orgs 4% of orgs 30% of orgs 56% of orgs Goal: develop a mature talent analytics function Bersin, 2013
  7. 7. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 7 Snapshot: Past, Present, Future 2013 2015 2017 BandwidthAllocation Level 4 Level 2 Level 1 Level 4 Level 3 Level 2 Level 1 Level 4 Level 3 Level 2 Level 1 Level 3
  8. 8. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 8 The Value Organizations with mature talent analytics functions... 12% 6% 12% 10% 30% improvement in talent metrics over all improvement in gross profit margins increase in employee performance increase in quality of hire ratings higher stock than the S&P 500 over the last 3 years CEB, 2013
  9. 9. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 9 We Were Warned 2 Advanced Reports 3 Proactive Analytics 4 Predictive Analytics 1 Operational Reports Level of Value Level of Effort/Skills Choke point for most Organizations Finally seeing ROI Bersin’s Maturity Model
  10. 10. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 10 Getting Started First Year
  11. 11. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 11 Getting Started ● Multiple 3-5 page dashboards created weekly ● Metrics calculated in isolation (no trends, forecast, benchmarks) ● 90% of time spent scrubbing the data, remainder of time spent trying to make pretty charts in company colors
  12. 12. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 12 A Year Later... Stuck at level 1 ● Lack of alignment between Recruiting, HR, and Finance data ● Lack of process among recruiters led to inaccurate data ● Lack of collaboration with executives/mgmt led to unhelpful dashboards ● Result: inconsistent improvement over time & “hot mess” reputation among business leaders
  13. 13. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 13 Pivot Point Second Year
  14. 14. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 14 Company Reactions When people see recruiting data…
  15. 15. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 15 Focusing On Our Biggest Challenges How do we staff our team for constantly shifting needs? ● Baby ● IPO = C U Later ● Changing company direction + fickle hiring managers who don’t know what they need ● Do we need generalists, SMEs, or flex recruiters? ● Capacity = “hey, can you take another req?”, and goals = “ASAP” “The Situation”
  16. 16. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 16 “Trystorming” A New Framework Quadrant Model 2 Goal: 70 days 3 capacity pts 3 Goal: 80 days 4 capacity pts 1 Goal: 60 days 2 capacity pts 4 Goal: 120 days 6 capacity pts FrequencyofHire Uniqueness of Skillset Project Mgr Receptionist Sales Exec SVP Quadrant model: We categorize roles into 4 levels of difficulty, based on frequency and uniqueness of skill set. This allows us to evaluate recruiter capacity and set goals. Recruiting goals: based on avg. time to fill by quadrant. Capacity: 25-30 quadrant points Example of “Level 3 analytic” (using data to solve problems
  17. 17. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 17 Using Our New Framework: Team Performance
  18. 18. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 18 What Gets Measured Gets Improved Time To Fill Performance Time to fill is an awful and an awesome recruiting metric, depending on how you use it. While it doesn’t provide much insight in and of itself, it is a gateway to improving performance Our Historical Time To Fill was 93 days on average (between 2012 and 2015). In 2015, we reduced our average time to fill to 76 days. Level 2 = trends over time vs. goal
  19. 19. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 19 What Gets Measured Gets Improved Level 2: Recruiter Scorecards Avg. Days In Stage - Tech Recruiting Eng Recruiter Resume Review Screen Hiring Mgr Int Onsite Offer Time to fill Time to fill last quarter Time to FIll vs.. Goal Rick 12 14 38 14 5 108 104 80% Maggie 4 7 8 13 2 71 93 112% Eng Avg. 11 10 13 16 3 90 102 93% Quality of Candidates - Tech Recruiting Eng Recruiter Total Applicants Screened # Hiring Mgr Int # Onsite # Offer Candidate quality Quality: last quarter Rick 192 119 16 8 3 24% 21% Maggie 176 53 47 36 12 43% 35% Eng Avg. 184 84 39 26 6 35% 27%
  20. 20. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 20 cRaZy QuArTeR bOnUs (Q2 2015) Level 3: Bonus Program Based On Quadrant Model What we did Hiring plan spiked drastically in Q2 2015 Data showed salaries increased in proportion to difficulty of roles. Recruiters were awarded 0.5% of all new hire salaries for Q2 “Equal opportunity” since capacity points were spread evenly (~30K per Q, ~3K per recruiter). So, what happened?
  21. 21. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 21 cRaZy QuArTeR bOnUs (Q2 2015) Level 3: Bonus Program Based On Quadrant Model 21 27 Q1 (no bonus) Q2 (bonus) QuadrantPoints Capacity Points 5.2 7.1 Q1 (no bonus) Q2 (bonus) OfferAccepts Hires Per Recruiter 78 74 Q1 (no bonus) Q2 (bonus) Days Time to Fill Bonus Program Results 28% increase in capacity pts ~2 more hires on average per recruiter 4 day reduction in time to fill
  22. 22. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 22 Using Our New Framework: Forecasting & Budget
  23. 23. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 23 Forecasting & Getting $ Expecting the Unexpected (Our Best Ex. Of Level 4) Previous forecasts: ask leaders what they want to hire for the year, add in expected attrition rate, and voila! Problem: has no resemblance to what actually happens. Why: Need to factor in rate of mid-year adds, transfer backfills, possibility of re-orgs, and new business, and attrition trends rather than historical avg.
  24. 24. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 24 Forecasting & Getting $ Making a business case for resources 80% capacity 7 recruiters 30% of roles filled 2-3 months late (not able to support new business deals) Additional $700K Heavy use of agencies required for an “Immediate fix”, since hiring new recruiters and ramping them up would take 3-4 months. 5-10% of roles hired late if agencies are effective Forecasting ~250 hires to fill by EOY... Current resources Expensive Fix
  25. 25. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 25 Forecasting & Getting $ Making a business case for resources Forecasting ~250 hires to fill by EOY... Additional $350K to spend in 2015 required Subscription for Hired.com – engage active tech candidates 1 contractor for Q2/Q3 to focus on quadrant 1/2 2 new recruiters Recruiter bonus program Referral bonus program De-prioritize non-critical roles and accept that 10-15% roles will be hired 2-3 months late. What We Proposed: Cost-Effective Fix
  26. 26. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 26 Forecasting & Getting $ What Happened? On track to meet 100% of goal by EOY of year (hired 235 out of 250)! What didn’t work: $10K referral bonus program didn’t yield any increase in referral hires What worked Hired.com yielded ~2 hard-to-fill tech hires per month New resources/incentives increased capacity by ~20 roles per Q Recruiter bonus program effectively increased capacity during Q2
  27. 27. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 27 Integrating HR & Recruiting Data
  28. 28. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 28 Can Interviews Predict Performance? Magical Pairing: HR + Recruiting Data Findings Interviews predict performance only if there were 5 or more interviewers. 83% of involuntary terminations were interviewed by < 5 people.
  29. 29. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 29 Quality of Hire by Source Magical Pairing: HR + Recruiting Data No significant relationship between source of hire and performance found. Inconsistent with the notion that “our best hires come from referrals”. Referrals and intern converts are 2x likely to stay past 2 years than agency/ passive candidates. Hypothesis: they get the most realistic job preview.
  30. 30. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 30 Switch to Interactive Dashes Example: Tableau
  31. 31. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 31 Glassdoor Reviews Comparing ourselves to talent competitors Company A B C Us D E E F Avg G Overall Ratings 4.5 4.4 4.1 4.0 4 3.9 3.4 3.4 3.2 3.2 Career Ops 4.3 3.9 3.9 3.7 3.7 3.9 3.4 3.3 3 3 Comp/Ben 4.5 4.3 4.2 3.5 3.8 3.8 3.5 3 3.2 3.3 Culture & Values 4.5 4.4 4.2 4.2 4.1 4.1 3.3 3.3 3.2 3.4 Leadership 4.2 3.9 3.8 3.8 3.5 3.8 3 3.1 2.9 2.8 Work/Life Balance 3.9 4 4 3.5 3.6 3.1 2.7 3.5 3.3 3.9 Recommend? 91% 93% 79% 79% 72% 77% 64% 64% 58% 51% Outlook (% Pos) 88% 76% 80% 60% 71% 77% 63% 64% 39% 34% CEO Approval 96% 97% 88% 86% 93% 96% 82% 65% 69% 37%
  32. 32. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 32 Wrap-Up
  33. 33. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 33 Do... Live in the system & consolidate Always be goaling Analyst as an insider Construct narratives & ask “why?” Beg, borrow, & steal 100% adoption of ATS. 1 hiring plan spreadsheet, 1 system of record, 1 main dashboard. Define success, set realistic goals, and track them. What gets measured gets improved. Empower your analyst; include in mgmt and strategy meetings. The more they know the more they can help. Summarize take-aways, caveats, and relevance. Don’t accept data as is: dig, segment, and identify causes. Lack expertise and budget? Borrow from Finance, Sales, Ops, IT. Bare minimum: get their opinion.
  34. 34. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 34 Don’t... Waste time on things that don’t matter Let perfect be the enemy of good Get comfortable No “so what?” metrics or excessive dashboards, teach entire team to pull basic reports. Ask, “What is the impact of data being 95% vs. 100% correct?” (some metrics need to be perfect, others don’t). Keep on iterating; re-evaluate which metrics are still valuable. Switch up what you show to keep engagement. Overlook Quick Wins Start by using data you already have. Difficult and expensive isn’t always better than simple and cheap. Get discouraged Analytics = delayed gratification. It gets better.
  35. 35. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 35 Recommended Reading Author: former head of Google’s People Analytics team All about how to get your point across with data – almost entirely within Excel Guide for what makes a good vs. bad graph Her blog: www.storytellingwithdata.com
  36. 36. OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 36 Q&A

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