Talent Analytics - Opower

Talent Analytics
The Opower Story
1

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

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

What We’re Covering
Our Journey
● Inspiration
● Analytics past and present
● Team performance
● Forecasting & budget
Analytics Insights
● Integrated HR & TA data
● Wrap up

What Inspired Us?

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

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

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

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

Getting Started
First Year

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

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

Pivot Point
Second Year

Company Reactions
When people see recruiting data…

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”

“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

Using Our New Framework:
Team Performance

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

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%

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?

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
OfferAccepts
Hires Per Recruiter
78 74
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

Using Our New Framework:
Forecasting & Budget

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.

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

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

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

Integrating HR & Recruiting Data

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.

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.

Switch to Interactive Dashes
Example: Tableau

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%

Wrap-Up

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.

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.

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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