NHRDN Virtual Learning Session on HR Analytics

Talent AnalyticsThe elixir to a successful HR career

Feb 2012 - NHRD

Copyright © 2011 Deloitte Development LLC. All rights reserved.2 Footer

• Often catch yourself saying “I feel this is the right thing to do”?

• Still use interview conversations to predict the suitability of a potential employee with a reasonable degree of certainty?

• Track attrition statistics on percentage points and feel happy if it shows a downward trend?

• Have a plethora of training programs but no clear link to business impact?

• Want to make an impact but did not know how to do it?

…….And, If I wanted to lift my sagging career, what would I do?

Do You….


• What does one needs to do when one does not have the money muscle to get talent ?

• Has a boss who shows no support for any expense !

• The business and your self respect depends on an outcome that seems challenging to achieve?

• And you wish to make a difference !!

• THINK DIFFERENTLY and use WHAT is AVAILABLE to you…DATA

Moneyball…….

In God we trust, all others bring Data – Edward Demming


Workforce planning and deployment

Talent sourcing and selection

People/leadership development

Performance management

Rewards and recognition

Knowledge management

Tale

nt

man

agem

ent

elem

ents

Talent analytics – your barometer


Weather reports – track the talent radar

Yesterday Tomorrow

Today

…Talent

analytics

Mea

sure

perf

orm

ance

Manage

performance

Maximizeperformance

• Those which summarize and compare operational and/or financial data on key workforce variables within defined time

frames. – e.g., totals, averages, percentages, and trends.

• Those which apply one or two internal data sources to derive useful information.

– e.g., the past education experience of job

candidates is compared to job performance during the first year of employment.

• Mathematical models that use multiple internal and external data sources to

predict future talent events. – e.g., a predictive model

that uses internal and external employee level

data to predict the likelihood that a particular employee will resign in the

next six months and supply the reasons for the

prediction (e.g., long commute).


Attrition waterfall

Management reports

2,000

4,000

6,000

8,000

10,000

FY11 March

Attrition New starts New starts attrition

FY12Feb

Hea

dco

un

t

Back to Talent

Analytics

8,480801

2,161 198 9,642


Management reports (cont.)

Outstanding

Very good

Good

Average Average

Good

Very good

0 | 0%

0 | 0%

0 | 0% 0 | 0%

0 | 0%

0 | 0%

0 | 0%

0 | 0% 0 | 0%

0 | 0%

0 | 0% 0 | 0%

Avg. Avg.

0 | 0%

FY09 000 000 000 000 000 000 000

FY08 000 000 000 000 000 000 000

20 | 0% 3

0 | 0%

10 | 0%

Performance rating movements

Back to Talent

Analytics



Yesterday Tomorrow

Today

…Talent

analytics

Mea

sure

perf

orm

ance

Manage

performance

Maximizeperformance













Employee commitment

Human Resource (HR)/ people practices (Drivers)

Employee commitment

(Goal/Outcome)

Culture(Moderators)

Switchingfactors

(Commitmentmoderators)

Employee

Commitment• Affective Attachment

• Willing to be Proactive

• Intent to Stay

Employee Focus

Switching Alternatives

Switching CostsCustomer Focus

Community Focus

Financial Focus

• Service leadership

• Change management

• Employee communications

• Immediate supervision

• Employee growth and development

• Training and education

• Performance evaluation

• Recognition of employee performance

• Compensation

• Benefits

• Career-life fit

• Teamwork/team management

• Diversity management

• Customer relationship management

Back to Talent

Analytics



Yesterday Tomorrow

Today

…Talent

analytics

Mea

sure

perf

orm

ance

Manage

performance

Maximizeperformance













-40

-30

-20

-10

0

10

20

30

40

Lift chart – demonstrates the effectiveness/benefit of the retention model

Retention tracker

Deciles

Re

lati

ve

att

riti

on

ris

k

Model Equation

target = a+b1(tenure)+b2(commute to work)+ b3(pay)+ b4(rating)+b5(training hours)+…

This equation is used to give a score to each employee.

1 2 3 4 5 6 7 8 9 10

Low attrition risk segment

Moderate attrition risk segment

High attrition risk segment

Extremely high attrition risk

segment

Back to Talent

Analytics


Retention tracker (cont.)

Solution set to provide an overview of the attrition risk in the organization

Attrition risk probabilities are generated for each employee in the organization

Attrition risk projections can be analyzed at organizational level

Attrition risk projections can be analyzed at a business level

Attrition risk projections can be analyzed at a team level

Team 1

1%

Team 2

2%

Team 3

4%

Team 4

5%

Business 2

2%

Business 3

4%

Business1

3%

Organization 4%

Back to Talent

Analytics



Yesterday Tomorrow

Today

…Talent

analytics

Mea

sure

perf

orm

ance

Manage

performance

Maximizeperformance













Talent Analytics – A Model

Busines

s

Issu

e

Insights /

Recommendations

Data

Mining &

Analysis

Measurem

ent of

effectiveness and

reporting

• What kind of data is required to analyze the issue?

• How do we look at the data to draw meaningful conclusions?

• What insights can we draw from the analysis?

• Is it possible to extrapolate past data to predict future outcomes?

• How are we measuring the effectiveness of these measures?

• Can we measure it in $ terms and report to the business?

• What is the business issue that we are looking to address?

Analytics - The Past and The Future


Evolution of Analytics

Data and Basic

Reporting

Consolidated reporting

Basic Analytics

Cross process and functional Analytics

Predictive Analytics


Predictive Modeling Defined

Predictive modeling uses available internal and external data to predict future events at an applicant, employee, or claimant level. Models can be designed to predict a variety of outcomes and have broad based applications.

Data Mining — a process, which utilizes a number of mathematical techniques, to analyze large quantities of internal and external data, in order to unlock previously unknown and meaningful business relationships.

Predictive Modeling — the application of data mining techniques and algorithms to produce a mathematical model that can effectively predict and segment future events.


The 4 Critical Modeling Questions

1. Is there a compelling problem or opportunity?• What is the business case scope and size? • What are the costs, including opportunity costs?

2. Do we have the data we need?• What is the state of available data and our ability to access it?

3. Can we segment or predict potential outcomes and does it put us in a position to make a difference?

• Is there a basis to build a predictive model?• Will the model output help us solve the defined business problem?

4. Can we effectively act upon the predictive model output?• What is our change readiness and ability to implement?• Will implementation drive the required ROI?• What is our political and legal climate?


What Can Predictive Modeling Do?

• Predictive models use available internal and external data to predict the likelihood of future events at the customer or employee level

• Models are deployed by businesses to direct limited resources to the actions that will yield the largest economic benefit

A business unit uses predictive models to maximize ‘customer lifetime value’

• Targeting new customers

• Optimizing pricing, customer service and costs

• Focused customer retention

HR can use predictive models to maximize ‘employee lifetime value’

• Recruiting and hiring new employees

• Optimizing development and performance

• Focused employee retention

For example… Similarly…


Talent Acquisition – A Case in Point

150,000 applications p.a. which results in an eligible candidate

pool of 81000

Initial screening brings this number down to

8000

Further filtration occurs through multiple rounds

of interviews

2100 Offers / 1675 accepted

Problem Statement: How do we review more qualified candidates, faster, with improved accuracy, and with less cost?


Sachin Saina Rahul

10 years of work experience 10 years of work experience 10 years of work experience

4 previous employers in past 10 years

2 previous employers in past 10 years

1 previous employer in the past 10 years

Current employer is large technology company

Current company is small technology company

Current company is a microchip mfg. company

Attended Tier I Engineering College

Attended Tier II Engineering College

Attended Tier III Engineering College

B.E. in Electronics & Communication

B.E. in Information Technology B.E. in Computer Science

Engineering Society member NA Engineering Society member

Traditional Recruiting Data

Limitations:

• Simple set of rules comparing education level and work experience• Uniform approach across candidate base• Customary education/work experience• Difficult to differentiate people

• Who would be the most successful and Who would be the long term employee?


• How long has the candidate been residing in the city?

• Does he / she own a house?

• Is he / she a member of any external agencies / non-profit ventures?

• What is the commute time to the office?

• How many promotions has the person had in the last 5 years?

• What is the average compensation increase that he / she has received in the last 3 years?

• Does the person belong to a Tier I or a Tier II city? (aspirations)

Talent Analytics – Expand the data set

Predictive models built from these and hundreds of other data elements can better quantify the likelihood and reasoning of future individual employee events.


Analytics Dashboard – Illustration

Sachin Saina Rahul

Likelihood of future event

20% less likely than average to be a successful hire and stay with the company for 3 years

80% more likely than average to be a successful hire and stay with the company for 3 years

30% more likely than average to be a successful hire and performance rated above average

Top 3 reasons

• Sub-optimal employment history

• Long Commute – 40 miles

• Has been a resident of this city for 2 years

• Optimal past employment history

• Short Commute – 1 mile

• Owns a house in the city

• Sub-optimal employment history

• Medium Commute – 15 miles

• Has been a resident of this city for 5 years

Possible actions • Unlikely pursuit – third Tier

• Actively pursue for national position – Primary Tier

• Possible pursuit - Second tier (possible option for local/regional position)

If the Predictive Analytics Model is effectively implemented, it allows scarce resources to be better focused, resulting in measurable benefits.

Building a Predictive Model


Predicting Attrition – The Holy Grail

Example of potential model variables for active and terminated employees that may already be available in existing HR systems:

Employment Data

Employee specific data

Time and expense

Compensation Performance

• Office Address• Department• Date of hire• Supervisor• Supervisor’s

Performance Rating

• Home Address• Age• Gender• Education Level• Marital Status• Number of

Dependents

• Hours Worked• Number of Training

Days• Vacation/Sick Days

Taken

• Salary• Bonus • ESOPs• Performance Rating• Recognition Awards

• Performance Rating Previous 5 Years

• Expected promotion date

• Date of Last Promotion

• Date of 2nd to Last Promotion

• Date of 3rd to Last Promotion

Example of potential model variables from external sources:

External Data Elements

• GDP Growth Rate• Unemployment Rate• Number of Talent Competitors in the same city

• Average salary increase• Niche skill vs Easily replaceable skill• Additional Macro Economic Variables


Employee Name Saina

Employee ID 1234

Location/Region Hyderabad / AP

Employees in Location 6,000

Date of Hire 01-03-2006

Rating 3

Tenure 5 years

Base Pay $64,000

Position Level Analyst

Working Hours/Yr 1,920

Training Weeks/Yr 2

Expected Promotion Year 2011

Vacation Days Taken to DateCommute to Work

16> 25 miles

Risk Segment High

Risk of Leaving 88%

Actual Cost of Replacement $6,000

Expected Cost (Risk * Actual Cost)

$84,480

First Most Important Reason for Risk of Leaving

Time until promotion

Second Most Important Reason for Risk of Leaving

Supervisor’s past retention rate is low

Third Most Important Reason for Risk of Leaving

Long commute

Sample Model Input Sample Model Output

Produce Attrition-Impact Reports for Employees

ILLUSTRATIVE

Actual cost is based on performance rating, level and hiring costs. Reason codes are developed from statistically significant terms in the predictive model.


Key Roadblocks for Implementing Talent Analytics

Roadblock Suggested Approach

Lack of Sponsorship Gain support from those who derive value from the work being done.

Unreliable Data Quality & Availability

Ensure data requirements and data integrity are addressed.

Not Aligned to Strategy Develop metrics from a very clear understanding of the company’s strategy.

Not Understanding the “Customer’s” Needs

Keep in mind the audience and what they might value.

No Accountability Set guidelines of expectations and create responsibility among people.

Not Starting Simple and Small Focus on the “vital few” measures that really make a difference.


Needs Tools

Issues Outcomes

Q1 At the current time, what are your key needs in terms of analysis that you would like to be carried out on your HR data, and what are the key outcomes and business decisions that you are trying to address with that analysis?

Q2 What are your views in relation to the quality and completeness of the data that you have available to carry out a) basic analysis of your HR data and b) more advanced analytics on your HR data?

Q3 How do you think your needs/wishes around HR analytics will evolve, when the economy improves?

Q4 What is your “holy grail” of HR analytics that you would like to carry out, but don’t have the time/data/resources to do so?

Points for Discussion


Success of Talent Analytics (TA)

TA = MC2

Measure X

Context X

Competence

Incisive insights embedded in the right context can drive immense value!


What Are the Key Takeaways?

• Analytics can support and drive change and can be quantified .

• Analytics can assess issues objectively and consistently

• Analytics can add sophistication for HR to manage talent and perception.

• Analytics can serve executive leadership in a strategic advisory role

• Analytics can make you look good and reboot your career