PREDICTIVE ANALYTICSHow analytics can drive business success

6 November, 2013

MERCER WEBCAST

Brian Kelly, Partner, Global Practice Leader, Workforce Analytics & Planning

Astik Ranade, Principal, Leader, Workforce Analytics & Planning, Asia, MiddleEast & Africa

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Today’s presenters

Brian KellyPartner, Global Practice Leader,Workforce Analytics & PlanningMercer+215 350 2641brian.j.kelly@mercer.com

Astik RanadePrincipal, Leader, Workforce Analyticsand Planning Asia, Middle East & AfricaMercer+65 6398 2692astik.ranade@mercer.com

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AgendaWhat we will cover today

• Strategic rationale and the analytics journey– What is driving the need for analytics?

• Foundational analytics– How do I get started?

• Advanced analytics– How do I get predictive?– RetailCo

• Lessons Learned

• Q&A

STRATEGIC RATIONALE AND THEANALYTICS JOURNEYWhat is driving the need for analytics?

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What is the role of A Workforce Analytics and Planning Center of Expertise withinthe Next Gen HR service delivery model?

EVIDENCE-BASEDHuman capitalINSIGHTS

Data-driven stories

HR evolution

Strategic people agenda

How do analytics and planning support a company’s people strategy?

How do I transition my HR organization to a “Next Gen” analytics delivery model?

Analytics andPlanning Centerof Expertise

PEOPLESTRATEGYNext Gen workforce

analytics delivery model

transition

HR Is Asking New Questions

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Human capital risk, employability and mobility was the number onecritical issue at the WEF annual meeting in Davos

“The world is movingfrom capitalism

to talentism”— Klaus Schwab,

Founder & Executive Chairmanof World Economic Forum

“The world is movingfrom capitalism

to talentism”— Klaus Schwab,

Founder & Executive Chairmanof World Economic Forum

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Meet the New Boss: Big DataCompanies Trade In Hunch-Based Hiring

for Computer Modeling–The Wall Street Journal

Big Data Upends the WayWorkers Are Paid

–The Wall Street Journal

Big Data for HR – Why Does It Matter?Leading HR Functions Are Leveraging Extensive Data in Their Systems,As Executives Are Demanding Insights

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Data in Motion

Velocity

Volume

Data at RestData in Different Forms

Variety

Data in Doubt

Veracity

Source: IBM Whitepapers; 451 whitepapers, and Innosight Analysis

Data in Motion

Velocity

Volume

Data at Rest

Challenging Aspects of “Big Data”

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Measurement Continuum

ReactiveChecks

Predictive/Causal

Modeling

Correlations

Benchmarks

On-GoingReports

Simulationsand

Forecasting

Anecdotes

Your Analytics Journey

FOUNDATIONAL ANALYTICSHow Do I Get Started?

MERCER WEBCAST GETTING STARTED WITH METRICS

Visualize Your Workforce: Internal Labor Market (ILM®) Map

GETTING STARTED WITH METRICS

Promotions

338

1,001

2,093

650

273

195Total Employees

(11.8%)

(13.3%)

(5.8%)

(16.0%)

(12.0%)

(2.2%)

52

91

299

65

(19.0%)

(14.0%)

(21.1%)

(29.9%)

(19.2%)

20(10.2%)

442

52

65

403

221

39

(19.0%)

(10.4%)

(19.6%)

(23.9%)

(12.5%)

12(6.1%)

5

4

2

6

3

1

HIRES EXITSCAREERLEVEL

2626

7878

117117

169169

3939

1313

1

18

23

34

7

Lateral moves

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Promotions

338

1,001

2,093

650

273

195Total Employees

(11.8%)

(13.3%)

(5.8%)

(16.0%)

(12.0%)

(2.2%)

52

91

299

65

(19.0%)

(14.0%)

(21.1%)

(29.9%)

(19.2%)

20(10.2%)

442

52

65

403

221

39

(19.0%)

(10.4%)

(19.6%)

(23.9%)

(12.5%)

12(6.1%)

5

4

2

6

3

1

HIRES EXITSCAREERLEVEL

2626

7878

117117

169169

3939

1313

1

18

23

34

7

Lateral moves

Outside hires are usurpingcareer opportunities

High potentialemployees are

giving up

Analyze an Internal Labor Market (ILM®) Map

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To what extent is the organization buying or building talent? Is the reality alignedwith the stated sourcing strategy?

Even if the organization is oriented to building talent, is there sufficient velocity inthe system to motivate employees?

Where are the career choke points? Are they as expected? What are theassociated risks?

Are there focal points of entry or exit, and do they reveal areas of concern?

What is the “production” or “decision making” model? The ILM Map’s shape canpoint to differences.

Generally, an ILM map cannot tell you details about who is progressing orwhy employees are leaving – it is the start of a more significant

conversation about how an organization manages its talent.

Internal Labor Market (ILM®) MapBuild Hypotheses in Response to Key Questions

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Buy vs. BuildLook At Patterns of Entry and Promotion Throughout The Hierarchy

HIRES

HIRES

EXITS

EXITS

EXITS

EXITS

EXITS

EXITS

EXITS

EXITS

EXITS

HIRES

HIRES

HIRES

HIRES

HIRES

HIRES

Limited hiring atmiddle and upper

levels

Most entry points areat lower levels—

limits touch pointswith the market

Focus on promotionto feed upper levels

from within

Diffuse exit points

BUILD BUY

Diffuse entryand exit points

Significant numberof hires at middleand upper levels

Tend to see morehires than

promotions

Many touch pointswith the market

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Projections Based on ILM Maps Can Be Used for Workforce PlanningIn This Case, Focused on Diversity

Projections: % Female Senior Managers 2006-2016

22%

24%

26%

28%

30%

32%

34%

36%

38%

40%

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

%Fe

mal

e(S

enio

rMan

ager

s)

Baseline

With adjustedpromotion %s

With adjusted hire,promotion andtermination %s

“Soft target”

With adjusted hire %s

With adjusted term %s

This organization needs to focus on a combination of interventions to expeditiously achieve its objectives.

Disguised case example

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ADVANCED ANALYTICSHow Do I Get Predictive?

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Time 1 Time 2

EmployeeEngagement

ProductivityPer FTE

EmployeeEngagement

ProductivityPer FTE

EconomicConditions

Spans ofControl

LaborMarket

EmployeeEngagement

ProductivityPer FTE

Statistical Modeling Gets to Cause and Effect

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How Does theWorkforce

Impact

and ProfitabilityStore Revenue

RetailCo: Advanced Analytics

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How Does theWorkforce

Impact

and ProfitabilityStore Revenue

• Local unemployment rates• Distance to work• Local labor pool (diversity, education, age, income)

• Diversity• Prior year’s sales trends• Supervisor promotion rates,

turnover rates, age, tenure• Non-supervisor promotion rates,

turnover rates, age, tenure

• Size• Supervisor spans of control• Promotion rates• Turnover rates• Age• Tenure

• Job status• Participation in training• Internal mobility• Employment status• Hire source• Location

• Age• Gender• Ethnicity• Tenure• Performance ratings• Compensation

OrganizationalPractices

ExternalInfluences

IndividualAttributes

RetailCo: Statistical Analyses Focused on Three Broad Categories ofDrivers of Both Turnover and Sales

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IndividualAttributes

Less Likely to Leave More Likely to Leave

-100% -75% -50% -25% 0% 25% 50% 75% 100%

Short (4-6 mo.) vs. Long (>1yr) Tenure

Employee is a Rehire

Younger Neighborhood

Younger (19 vs. 24 Years Old)

Took 3 vs. 0 Training Classes

First-time Hire That Month

10 Hours of OT vs. 0

3% Pay Raise vs. 1.5% Raise

% Change in Voluntary Turnover Probability

A Change from0–10 Hours of OT

per Month Decreases anEmployees Likelihood of

Leaving by >60%

IndividualAttributes

RetailCo: Many Factors Play a Role in RetentionIncluding Pay, Overtime and Training

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The models on which these results are based control for individualattributes, organizational factors and external influences.

Influ

ence

Performance

The Optimal Model

Full TimeEmployees

Location

HighUnemployment Rate

Part TimeEmployees

OvertimeCompensation

ExperiencedFull Time Manager

HighTenure

RetailCo: What Internal Factors Drove the Sales of Goods?

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LESSONS LEARNED

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Measure what matters. Focus on information that can have ameaningful impact on business results2

3

Find a pilot partner5

Focus on information that impacts critical roles and jobs first

Don’t wait for perfect data – just get started4

Analytics is a journey. Focus on moving from WHAT? To WHY?

Lessons Learned

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What’s coming up nextWorkforce analytics and planning workshop series

WORKFORCE METRICS AND ANALYTICS:Driving Business Results with DataNovember 22, 2013 – ManilaNovember 26, 2013 – JakartaJanuary 23, 2014 – Singapore

STRATEGIC WORKFORCE PLANNING:Defining and Fulfilling Business RequirementsNovember 15, 2013

For more details, visit www.mercer.com/analytics-and-planning-workshops-amea

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Questions?

Brian KellyPartner, Global Practice Leader,Workforce Analytics & PlanningMercer+215 350 2641brian.j.kelly@mercer.com

Astik RanadePrincipal, Leader, Workforce Analyticsand Planning Asia, Middle East & AfricaMercer+65 6398 2692astik.ranade@mercer.com