Crafting a talent analytics function

and fostering strategic partnership

November 2015

Will Gaker

Talent Analytics

Agenda

Introduction

Case examples

Building a talent

analytics function

Our journey so far…

Introduction

3

More than 400M members in

over 200 countries around the

world

Professionals are signing up to

join LinkedIn at a rate of more

than 2 new members per

second.

There are over 39 million

students and recent college

graduates on LinkedIn. They are

LinkedIn's fastest-growing

demographic.

LinkedIn operates the

largest professional network on the internet

Regional membership

127M+ EMEA

96M+ Europe

78M+ Asia and the Pacific

15M+ Southeast Asia

7M+ DACH

17M+ MENA

55M+ LATAM

Total revenue advanced 37% year-on-year to $780M

5

2015 Q3 distribution by revenue stream

Premium

Subscriptions

18% - $138M

Marketing

Solutions

18% - $140M

Talent

Solutions

64% - $502M

Source: Linked.com 2015 Q3 Revenue

6

7

8

We are a team of HR experts, data scientists,

and consultants who help HR solve problems

A center of excellence for Strategy & Analytics

9

Business

Operations

& Analytics

Talent

Analytics

BizOps

Analytics

Field

Sales

Online

Sales

Product

Strategy

Growth

Marketing

Strategy

Talent Analytics sits in Business Operations

10

Talent analytics is getting bigger

Source: LinkedIn self-reported member data based on job title, start date, and company

11

…but still a lot of room to grow

Source: LinkedIn self-reported member data based on job title, start date, and company

©2015 LinkedIn Corporation. All Rights Reserved.

12

Talent analytics professionals

have a variety of skill sets

Source: LinkedIn, estimate based on member reported job titles and skills

13

4,500 companies have

employees focused

on talent analytics

43% of the Fortune 1000

have talent analytics

teams

55% of all talent analytics

functions have been

started within the last

5 years

70% of all talent analytics

functions only have

1-2 team members

Source: LinkedIn, estimate based on member reported job titles and skills

Building a talent

analytics function

Guiding principles for

crafting a talent analytics function

Identify the core purpose of your team

Staff your analytics team with a blend of skills

Focus on questions that create business value

Invest capacity to develop maturity

Prioritize your efforts to maximize impact

Problem-solving focus drives action and impact at LinkedIn

What is the core purpose of your team?

Research Problem Solving

Focused on methods

Long maturity curve

Business may not be

ready for results

May not help HR

become more strategic

Focused on impact

Short maturity curve

Helps the business

where they need it

Problem solving with HR

build business acumen

Align skill mix to your core purpose

17

Reporting

Business Intelligence

Ad Hoc Reporting

Data Mining / Cleaning

Technical PM

Consulting

Process Improvement

Performance Mgmt.

Org Design

Strategic Planning

Analytics

Statistical Modeling

A/B Testing

Survey Design

Research

©2015 LinkedIn Corporation. All Rights Reserved.

Staff your analytics team with a blend of skills

Focus on questions that create business value

19

Business impact is created through

behavior change, not by analytics alone

©2015 LinkedIn Corporation. All Rights Reserved.

Imp

act

Talent Analytics Maturity

Level 1

Data Monkey

Level 2

“Why?” Asker

Level 3

Proactive Business Partner

Level 4

Change Leader

“I need this data tomorrow for the company All Hands”

“How do we shape our talent

strategy to meet our business

goals?”

“Help me understand what problem you are

trying to solve?”

“How do we reduce bias in our people decisions?”

20

Invest capacity to impact your organization

across all three maturity curves

Technological

Maturity

Stakeholder

Maturity

Analytical

Maturity

Integrated data and

automated reporting

Stakeholders ask strategic

questions and act on results Predictive analytics

Integrated systems but

manual reporting

Stakeholders ask strategic

questions but don’t act Advanced analytics

Cleaner data-entry

but no integrations

Stakeholders ask for

insights

Manual reporting with

a few deep dives

Messy data in

disparate systems Stakeholders ask for reports

Manual reporting and

data cleaning 1.

2.

3.

4.

21

Prioritize your efforts and focus

where you can have an impact Im

pact

Org Readiness

Focus Now Focus Later

Avoid Automate

Our journey so far…

23

We focused our efforts on quick wins

What we had Businessdemand

Our solution

Analytics Infrastructure Reporting

Team resource allocation Building the IT

infrastructure is a long

journey…

Reporting will consume

100% of capacity and

never be 100% accurate

Prioritize quick wins that

solve business problems to

build credibility

24

We developed a framework to help our

stakeholders ask strategic business questions

Data - Oriented

Research questions need evidence in order to be answered

Questions that are not data-oriented usually need to be more specific

Objective

Testable

Specific

Great questions do not have the desired answer built-in

Make sure the answer to your question has the possibility of being positive

and negative

Great questions allow you to test your instinct

Sometimes the greatest learnings happen when the answer is unexpected

Specific questions focus the insights you are looking for and make them

easier to find

Broad questions can usually be broken down into several specific

questions

25

We are leveraging analytics to be

the connective tissue across our lifecycle Hiring

Data-driven recruiting using LinkedIn data

Monitoring org shapes to inform hiring strategy

Onboarding Monitoring onboarding satisfaction

and time to productivity

Inclusion Using analytics and natural

language processing to

inform inclusion strategies

Learning Measuring the impact of learning

and identifying learning needs to

inform content creation strategy

Engagement Measuring and monitoring

key drivers of engagement

Retention Predicting attrition with

advanced analytics and

developing strategies to

retain top talent

Succession Measuring succession risk and

creating strategies for reducing

readiness time

Performance Evaluating programs based

on impact to performance

Case

Examples

26

How do we acquire the

technical talent to meet

our growth objectives?

©2015 LinkedIn Corporation. All Rights Reserved.

How many engineering recruiters do we need?

Forecasted hiring needs

# of Hires Headcount forecasts

# of FTE

2015 2016 2017 2015 2016 2017

Are we hiring the right mix of people?

Org. shape has shifted over time

% of Engineering FTE

2013 20142013 2014

Senior+

Mid-Level

Entry-Level

Hiring has focused on entry level…

% of new hires

Partnered with HRBP and talent acquisition leads

to double mid-level and senior hires

# of new hires

1H 2014 1H 2015

Senior+

Mid-Level

Entry-Level

How do we improve operational

planning & better retain top talent?

32

We built an algorithm to forecast

sales attrition using machine learning

Future

Attrition

Rate

Comp

Predictors

Background

Predictors

Role

Predictors

Need to focus on retention strategy before predictive analytics

33

The attrition forecast matched the number that

our HRBPs provided for headcount planning

Attrition forecast (Algorithm)

Q2 Q3 Q4

Attrition forecast (HRBPs)

Q2 Q3 Q4

Enables predictive analytics to be actionable and scalable

34

Developing retention playbook

and then scaling forecast process

X

X X X X X X

X

O O O O O O

O

O O

O

What are the most attractive

regions to hire software engineers?

Supply of software engineers in region

De

ma

nd

fo

r s

oft

ware

en

gin

ee

rs

What is the supply and demand for software engineers?

Seattle

Chicago

Boston

Washington D.C.

New York

SF Bay

Phoenix

Houston

Denver

Philadelphia

Dallas

Toronto

Raleigh-Durham

Detroit

Montreal

Austin

San Diego LA

Atlanta

Minneapolis

High

Low

Low High

LI Profile features

LI Profile

Features

Candidates from ATS

Machine learning

algorithm

Classification model Classified profiles

Tra

in

Pre

dic

t

Used profile data to classify

software engineers into tracks

Where do we find critical skills?

Engineering track concentration by region

Below average Above average

Systems

& Infra Apps Data Mobile

Eng

Manager

Eng

Services OpsIT

Conclusion

Talent Analytics is

growing but still maturing

Focus on questions that

create business value

Build a team that helps

HR solve problems

Leap frog the maturity

curve by finding quick wins

Questions

40

?