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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 [email protected]
Astik RanadePrincipal, Leader, Workforce Analyticsand Planning Asia, Middle East & AfricaMercer+65 6398 [email protected]
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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 [email protected]
Astik RanadePrincipal, Leader, Workforce Analyticsand Planning Asia, Middle East & AfricaMercer+65 6398 [email protected]