Talent Analytics - Opower

Talent AnalyticsThe Opower Story

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OPOWER CONFIDENTIAL: DO NOT DISTRIBUTE 2

Hello!

Dawn MitchellDirector, Talent

Acquisition

@DawnJGMitchell

Alan HenshawManager, Technical

Recruiting

@henshawsburgh

Scott WalkerSenior People

Analyst

@scottwalker521


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


What We’re CoveringOur 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 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


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


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

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


Getting StartedFirst 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 PointSecond Year


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


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”

Scott Walker

[email protected] See my notes on this slide and change as needed.


“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


Using Our New Framework:Team Performance


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


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%


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

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.


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


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

Dawn Mitchell

just went through the next few slides and what is missing here is how it turned out. example - we hired x% on time, hired.com, contractor on quad 1 & 2

Dawn Mitchell

I also think we should talk to the attrition rate and not have it in writing - esp if we are sending the deck out after

Dawn Mitchell

In the first box you talk #of engineers and in the next two you talk %. I get why but for those outside of Opower it might be confusing. Maybe we add a % in to the first box or we talk eng all the way through


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


Integrating HR & Recruiting Data


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.


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.


Switch to Interactive DashesExample: Tableau


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


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


Q&A

Talent Analytics - Opower

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