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Data Mining and Predictive Modeling for HRData Mining Techniques for Analyzing Voluntary Turnover

Jason [email protected]

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Agenda

IntroductionBackground and experience.

What is Data Mining?High level overview.

Decision Tree ExamplesOverview of the decision tree technique, and how I have applied it in different ways.

Final ThoughtsLessons learned.

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Agenda





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IntroductionSanDiskJason Noriega

Senior Workforce Analyst at SanDisk

Experience: eBay, Lawrence Livermore National Laboratory, NASA, and National Geospatial Intelligence Agency.

Mobile

Computing

Consumer

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Agenda





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What is Data Mining?

Explores Your Data

Finds PatternsPerforms

Predictions

Data Mining

Targeted Customer Retention

Building Effective Marketing Campaigns

Targeted Employee Retention

Building Effective Recruiting Initiatives

Typical Uses WF Analytics

Data mining is a field that utilizes methods of machine learning,traditional statistics, database technology, and data visualization.

Predict 12 Months of Sales

of a New Product

Predict 12 Months of Employee

Separations

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High Level Process

Predictive Model

Historical Data Predictive Model

Data with Predictions

• R Program• Weka• SAS EG• SAS JMP• SPSS Modeler

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Agenda





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Decision Tree TechniqueDecision tree algorithms applied to employee attrition data provides you with a

laser precision focus on employee retention.

Recursive Partitioning Decision Rules - Example

Predictors1. Job Function2. Gender3. Ranking4. …..

Predictors1. Age2. Rating3. Commute4. …..

Predictors1. Performance2. Tenure3. Commute

Software Engineer

Top Performer

10%

25%

35%

50%

Leavers

Leavers

Leavers

Leavers

90%Stayers

75%Stayers

65%Stayers

50%Stayers

10%Leavers

90%Stayers

25%Leavers

75%Stayers

35%Leavers

65%Stayers

50%Leavers

50%Stayers

Age < 40

Rule 1: 50% Likelihood of LeavingIf Job Function = Software EngineerAnd Age < 40And Performance = Top Performer

Rule 2: 60% Likelihood of LeavingIf Manager Effectiveness < 70% FavorableAnd Pay Grade < 17, And Region = Americas

Rule 3: 70% Likelihood of LeavingIf Not Promoted in Prior 2 Years = NoAnd Tenure > 3 Years

All data is notional

10All data is notional

Improving the Retention of Employees

Where are the high rates of voluntary

turnover?

Decision TreeAnalysis

Why do these patterns exist?

ExitSurvey

Where should we prioritize areas of

improvement?

Prioritization

What can do to reduce high rates of voluntary turnover?

Action Planning

Example 1

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Example 1 - Variables

2010 2012

Demographics/Tenure

AgeGenderTenure

Variables for Attrition Analysis

Job ContentReasgned Diff. Pers AreaJob FunctionExempt/NonexemptLatest Perform. RatingPerform. Rating DeltaManager Flag

LocationRegionCountryLocation

CompensationPay GradeComp RatioPromotion FlagSpot Award FlagTuition Reimbursement Flag

2011

Model Timeframe for Finding Patterns

Attrition Analysis Timeframe

May

ManagerMgr TenureMgr Perform.

June

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Example 1 - Tree# %

Stayed: 11000 86%

Left: 1800 14%

# %Stayed: 4000 80%

Left: 1000 20%

# %Stayed: 3000 86% Left: 500 14%

# %Stayed: 3000 94% Left: 250 8%

# %Stayed: 1000 95% Left: 50 5%

Employee TenureLess than 1.1 yrs 1.1-2.6 2.6-7.3 > 7.3 Yrs

# %Stayed: 2000 87%

Left: 300 13%

# %Stayed: 500 82% Left: 100 17%

# %Stayed: 1500 71%

Left: 600 29%

Pay Grade<= 16 17-22 > 22

# %Stayed: 1100 85%

Left: 200 15%

Business UnitOrganization #2Organization #1

# %Stayed: 400 50%

Left: 400 50%

# %Stayed: 100 33%

Left: 200 67%

Compa Ratio> 85<= 85

# %Stayed: 300 75%

Left: 100 25%



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Example 1 – Survey Linkage # %Stayed: 11000 86%

Left: 1800 14%

# %Stayed: 4000 80%

Left: 1000 20%

# %Stayed: 3000 86% Left: 500 14%

# %Stayed: 3000 94% Left: 250 8%

# %Stayed: 1000 95% Left: 50 5%

Employee TenureLess than 1.1 yrs 1.1-2.6 2.6-7.3 > 7.3 Yrs

# %Stayed: 2000 87%

Left: 300 13%

# %Stayed: 500 82% Left: 100 17%

# %Stayed: 1500 71%

Left: 600 29%

Pay Grade<= 16 17-22 > 22

Exiting Employees

Job matched expectations

Opportunity for growth

High Importance/Low Performance

Low Importance/Low Performance

Low Importance/High Performance

High Importance/High Performance


Uncompetitive Pay

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Example 1 - Tree


# %Stayed: 15000 86%

Left: 2500 14%

# %Stayed: 4000 80% Left: 1000 20%

# %Stayed: 3000 86%

Left: 500 14%

# %Stayed: 3000 90% Left: 250 10%

# %Stayed: 1000 93% Left: 50 7%

Employee TenureLess than 1.1 yrs 1.1-2.6 yrs 2.6-7.3 > 7.3 Yrs

# %Stayed: 1500 82%

Left: 200 13%

# %Stayed: 1200 93%

Left: 150 7%

# %Stayed: 300 67%

Left: 150 33%

Performance Rating

DNM, Meets Some

Exceeds SomeExceeds MostMeets

# %Stayed: 350 78%

Left: 100 22%

# %Stayed: 850 95%

Left: 50 5%

PromotionYes No

http://www.forbes.com/sites/dorieclark/2013/03/08/how-big-data-is-transforming-the-hunt-for-talent/

Big Data Forbes Article


Example 2 – Grouping Variables

RecruitmentEducation LevelTotal Yrs ExperienceTotal Yrs Relevant ExperienceRecruitment Source

Location/Job/OrgPerformance RatingRegionStateCountryWork LocationPay GradeJob FunctionJob LevelBusiness Unit

Manager ImpactOverall Engagement Score

- Overall Satisfaction- Feel Proud- Feel Motivated- Recommend as Great Place

Overall Job Sat. Score- Work feels important purpose- Clearly understand indv. Objectives- Job makes good use of skills- Oppty to learn and grow

Overall Mgr Sat. Score- Mgr clearly explains expectations- Mgr provides ongoing coaching- Mgr actively supports prof. dev.- Mgr involve people with decisions- Mgr instills positive attitude- Mgr is someone I trust

Mgr PerformanceMgr Tenure

Impact of People Mgr

Location, Job,Org, Other Other

AgeGenderCompa Ratio

Where does the problem exist?

What impact doesthe mgr have?

How can we targetthose most likely to stay?

Are there any other important variables?

Other

Recruitment


Where Does the Problem Exist?# %

Stayed: 1210 83%

Left: 252 17%

# %Stayed: 795 80%

Left: 194 20%

Region

Americas

# %Stayed: 83 87%

Left: 12 13%

Asia Pacific# %

Stayed: 248 93%

Left: 20 7%

EMEA

# %Stayed: 638 77%

Left: 188 23%

# %Stayed: 157 96%

Left: 6 4%

Job FunctionA, B, and C All Other

Performance Rating

# %Stayed: 84 76%

Left: 26 24%

# %Stayed: 1126 83%

Left: 226 17%

Does Not Meet

Meets Some, Meets,Exceeds Some/MostNo Rating

Location/Job/Org- Perform. Rating- Region- State- Country- Work Location- Pay Grade- Job Function- Job Level- Business Unit


What Impact does the Manager Have?Mgr Impact

- Overall Engagement Score- Overall Satisfaction- Feeling Proud- Feeling Motivated- Recommend great place

- Overall Job Sat. Score- Work feels important purpose- Clearly understand indv. Obj- Job makes good use of skills- Oppty to learn and grow

- Overall Mgr Effectiveness- Mgr clearly explains expectations- Mgr provides ongoing coaching- Mgr actively supports prof. dev.- Mgr involve people with decs- Mgr instills positive attitude- Mgr is someone I trust

- Mgr Performance- Mgr Tenure # %

Stayed: 351 73%

Left: 128 27%

# %Stayed: 287 84%

Left: 60 16%

Mgr Effectiveness

<= 75% Favorable > 75% Favorable

# %Stayed: 1210 83%

Left: 252 17%

# %Stayed: 795 80%

Left: 194 20%

Region

Americas

# %Stayed: 83 87%

Left: 12 13%

Asia Pacific# %

Stayed: 248 93%

Left: 20 7%

EMEA

# %Stayed: 638 77%

Left: 188 23%

# %Stayed: 157 96%

Left: 6 4%

Job FunctionA, B, and C All Other

Performance Rating

# %Stayed: 84 76%

Left: 26 24%

# %Stayed: 1126 83%

Left: 226 17%

Does Not Meet

Meets Some, Meets,Exceeds Some/MostNo Rating


Example 3 Quarterly Workforce Dashboard with Deep Dive

Predictors1. Tenure2. Age3. Region4. …..

Predictors1. Region2. Rating3. Pay Grade4. …..

Predictors1. Grade2. Performance3. Age

Tenure

<= 7

10%

25%

35%

50%

Leavers

Leavers

Leavers

Leavers

90%Stayers

75%Stayers

65%Stayers

50%Stayers

19%Leavers

81%Stayers

25%Leavers

75%Stayers

35%Leavers

65%Stayers

50%Leavers

50%Stayers

Region

Quarterly Dashboard Deep Dive

<= 2.5 yrs > 2.5 yrs

India

Grade> 7

12.3%14.2%

15.4%17.1% 19.1%

0%

5%

10%

15%

20%

25%

2013-Q2 2013-Q3 2013-Q4 2014-Q1 2014-Q2

Voluntary-ORG Involuntary -ORG Voluntary-Companywide

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SoftwareR Program for Statistical Computing SPSS Modeler

http://www.r-project.org/

http://rattle.togaware.com/rattle-install-mswindows.html

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Data Mining and Predictive Modeling for HRstatic1.squarespace.com/static/537a63d9e4b002aab7aedbf4/t/53f4eda… · Data Mining and Predictive Modeling for HR ... High Level Process