talent analytics - opower
TRANSCRIPT
Talent AnalyticsThe Opower Story
1
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Hello!
Dawn MitchellDirector, Talent
Acquisition
@DawnJGMitchell
Alan HenshawManager, Technical
Recruiting
@henshawsburgh
Scott WalkerSenior People
Analyst
@scottwalker521
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About OpowerWhat 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 ResultsWe’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
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What We’re CoveringOur Journey
● Inspiration● Analytics past and present● Team performance● Forecasting & budget
Analytics Insights
● Integrated HR & TA data● Wrap up
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What Inspired Us?
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Talent Analytics Maturity Model
Level 1: Reporting MonkeyAd hoc, operational reports only
“Can I get this data for tomorrow’s all-hands?”
Level 2: Advanced ReportingReports focus on benchmarks/trends
“How has our time to fill changed over time?”
Level 3: Proactive AnalyticsSolving talent challenges through data/statistical analyses
“How do we staff our team for constantly shifting hiring needs?”
Level 4: Predictive AnalyticsUsing 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
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Snapshot: Past, Present, Future
2013 2015 2017
Ban
dwid
th A
lloca
tion
Level 4
Level 2
Level 1
Level 4
Level 3
Level 2
Level 1
Level 4
Level 3
Level 2
Level 1
Level 3
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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
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We Were Warned
2Advanced
Reports
3Proactive Analytics
4PredictiveAnalytics
1Operational
Reports
Level of Value
Level of Effort/Skills
Choke point for most
Organizations
Finally seeing ROI
Bersin’s Maturity Model
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Getting StartedFirst Year
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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
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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
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Pivot PointSecond Year
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Company ReactionsWhen people see recruiting data…
Not hiring fast
enough
Not hiring quality talent
We are under staffed
Show us
more dataWhy is our goal
changing?
What is happening?!
Just tell me if it’s good or bad
We are over staffed
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Focusing On Our Biggest ChallengesHow 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”
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“Trystorming” A New Framework
Quadrant Model
2Goal: 70 days
3 capacity pts
3Goal: 80 days
4 capacity pts
1Goal: 60 days
2 capacity pts
4Goal: 120 days
6 capacity ptsFr
eque
ncy
of H
ire
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
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Using Our New Framework:Team Performance
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What Gets Measured Gets ImprovedTime 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
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What Gets Measured Gets ImprovedLevel 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%
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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?
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cRaZy QuArTeR bOnUs (Q2 2015)Level 3: Bonus Program Based On Quadrant Model
Bonus Program Results
28% increase in capacity pts
~2 more hires on average per recruiter
4 day reduction in time to fill
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Using Our New Framework:Forecasting & Budget
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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.
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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
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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 candidates1 contractor for Q2/Q3 to focus on quadrant 1/2
2 new recruitersRecruiter bonus programReferral 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
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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 workedHired.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
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Integrating HR & Recruiting Data
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Can Interviews Predict Performance?Magical Pairing: HR + Recruiting Data
FindingsInterviews predict performance only if there were 5 or more interviewers.
83% of involuntary terminations were interviewed by < 5 people.
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Quality of Hire by SourceMagical 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.
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Switch to Interactive DashesExample: Tableau
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Glassdoor ReviewsComparing 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
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Wrap-Up
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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.
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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.
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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
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Q&A