Flight Risk Models - Retention...

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Flight Risk Models

Rana Dalbah, BAE Systems, Inc.

Kathryn VanDixhorn, Nationwide Insurance


What is your familiarity with

Flight Risk Models?


Flight Risk


Introduction


• As part of our growth towards advanced HR analytics, WRC is partnering with the Sectors and COEs to create a model to predict the likelihood that an employee, with critical skills, will voluntarily leave BAE Systems

• The results of this model will be used to help us:

• Reduce turnover in critical skills groups

• Identify areas for intervention to improve retention

• Guide management decisions

• Target knowledge transfer activities where attrition probability is greatest

• Be better prepared to manage the overall risk associated with “those going out the door”

Why Flight Risk?


Predicting Attrition Risks

$ Compensation

Commute

Work-Life

Home-Life

Individual attrition decisions weigh a number of different factors, each of which can be more or less critical depending on the person.

Predictive Analytics uses this understanding, along with advanced analytic techniques, to help HR answer the following questions:

1. Why are employees leaving?

2. Who is at risk to leave in the future?

3. What can HR do to mitigate attrition risk within the organization?

Every employee who leaves causes a significant cost to the organization, roughly equal to 150% of their salary (and even more if they occupy a pivotal role).

8% 12% 10% 6%

$22.5M $27M $24.75M $20.25M

$4.35M $0 $2.15M $6.55M

Attrition rate

Cost of turnover

Potential savings

Approach 1

Low hanging fruits

Overall

No Solution

Approach 2

Bigger initiatives

Approach 3

Integrated Decision making


Project Timeline

• Set project scope

• Conduct

hypothesis

generation

workshop with key

stakeholders

• Document set of

preliminary

hypotheses

• Data elements

definition and

identification

Phase IV

Model Deployment

Phase I

Program Design

Phase III

Predictive Model

Development

• Secure extraction

from various

sources

• Obtain ancillary

data

• Combine data

sources to create

core analytic

dataset

• Finalize model

coefficients

• Score employees using

finalized model

• Segment data by

demographics and

generate heatmaps

• Share findings with

stakeholders

• Set up data refresh

plan

• Perform preliminary

analyses (accuracy,

distribution,

frequencies,

reliability, validity,

etc.)

• Test hypothesized

associations

• Run competing

models and select

most suitable

• Finalize and validate

model

Phase II

Data Extraction &

Integration


The test population

• Focus on a function that is vital

to BAE Systems

• Sub-function within that pilot

group

• Keep it manageable at around

2500 employees if possible

• Has an attrition problem

• Discuss with Senior Leadership

• Get agreement/buy-in

• Representation


Team

9 May 6, 2015

Nida

Sohail

Data

Architect

Carol Darling VP, Workforce Relations & HR Compliance COE

Chief Privacy Officer

Megan Peacock

Director

Affirmative Action &

Compliance Programs

Rachel Kim

Manager HR III

HR Business

Partner Inc. HQ &

COEs

Rana Dalbah

Manager HR III

Workforce

Intelligence &

Processes

Joshua Kabler

Data

Visualization/

Metrics

Specialist

Brian Edwards

Data Scientist

Christopher Mitchell

Sr. Compliance

Specialist

Bobbi-Jean Liyari

Affirmative Action

Manager

Vacant

Sr. Principal

Affirmative

Action Specialist Kristin Brown

Sr. Compliance

Analyst

Jason Bryn

HR Compliance Manager

Workforce Disabilities

Chuck Andersen

Director II

Safety, Health &

Environment Assurance

Devra Cotherman

Special Projects / IWO


Tools


Part 1—Dataset


Hypothesis


Project Kickoff

• Develop working hypotheses about

what leads to attrition within the pilot

group

• Why do we think System Engineers

are leaving?

• Identify possible data sources to help

measure those hypotheses

• What data do we need in order to

prove or disprove each hypothesis?

• Obtain input from the data stewards

on the availability and/or integrity of

potential data sources

• Prioritization of hypotheses

Workshop Goals


Data

WI

TA

OD&L

Comp

Benefits

HRO&A

Sector HR


Part 2—Predictive Model

Development

15 May 6, 2015


Data Analysis


Part 3—Model Deployment

18 May 6, 2015


Final Model

• Finalize model coefficients

• Create employee scoring file

and heatmaps

• Maintain model

• Educate the users

“The fact that it’s perceived as

dangerous speaks to its power; if it

were weak, it wouldn’t be a threat.”*

* John Elder


• Engage the stakeholders to define issues

• Clearly define analysis process before gathering

the data

• Data format

• Waves/timeframe creations

• Workload planning

• Data gathering really is 85% of the process

• Use scalable methods to combine data

20 May 6, 2015

Lessons Learned


Flight Risk


The test population

• Initial models began with entire

enterprise

• Later refined by business unit

• Further refined to areas with

more turnover

• Call Center populations


Team

23 May 6, 2015

Computing

&

Visualization

HR & Business Acumen

MATT SUBJINSKE BI Visualization & Data

Integration

EVAN GUILFOYLE Dashboard Configuration

& Reporting KEVIN HILLIER Analytics & Consulting

Support

Statistics &

Behavioral

Science HR

Analytics KATHRYN VANDIXHORN Advanced Analytics &

Insights

SCOTT NEMETH HCRM HR Reporting &

Analytics


Tools


Predictive Model Development

25 May 6, 2015


Project Kickoff

• Representatives in Call Center

Environments (HRBPs, Data

Stewards, Compensation, Job

Shadowed at Call Centers, etc.)

• Generate ideas as to what might

increase retention


Data Analysis


Model Deployment

28 May 6, 2015


• Validation

• Socialize to stakeholders

• Consulting Strategy

• Educational

Reimbursement*

• Is it properly advertised?

• How can more people use it?

• Change tenure

requirement?

• Pass/Fail rather than a C?

• Increase maximum

reimbursement amount?

• Partner with

universities/community

colleges?

• Student Loan repayment?

*Stepwise Analysis to control for tenure


Exceptional/Strong Performers

Unacceptable/ Inconsistent Performers

High Flight Risk

114

(Focus on Retention)

80

Medium Flight Risk

187

57

Low Flight Risk

411

71 (Focus on Development)


• Hypothesis Generation

• Stakeholders’ Investment in Process

• Level Setting

• What model will do

• What model will not do

• What is the deliverable?

• Data Integration

• Focus

• Data gathering really is 85% of the process!!

31 May 6, 2015

Lessons Learned


GROUP DISCUSSION


WHAT TECHNIQUES DID

YOU USE?


WHAT CHALLENGES

HAVE YOU

ENCOUNTERED?


HOW HAVE YOU

EDUCATED OTHERS

ABOUT THE MODEL?


HAVE YOU PROVIDED

TRAINING TO YOUR

USERS?


HOW HAVE YOU USED

YOUR MODEL?


HOW IS YOUR MODEL

MAINTAINED?


WHAT ETHICAL/LEGAL

CHALLENGES HAVE

YOU ENCOUNTERED?


WHAT ARE YOUR

LESSONS LEARNED?


WHAT’S NEXT?

Flight Risk Models - Retention...

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