NHRDN Virtual Learning Session on HR Analytics

Talent Analytics
The elixir to a successful HR career
Feb 2012 - NHRD

Do You….

• 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?

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

Moneyball…….

• 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

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


Talent analytics – your barometer

Workforce planning and deployment
Talent management elements

Talent sourcing and selection

People/leadership development

Performance management

Rewards and recognition

Knowledge management


Weather reports – track the talent radar

• 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.

Today

• Mathematical models that
use multiple internal and
external data sources to
predict future talent events.
• Those which apply one or – e.g., a predictive model
two internal data sources to that uses internal and
Talent
derive useful information. … external employee level
analytics data to predict the
– e.g., the past education
likelihood that a particular
experience of job
employee will resign in the
candidates is compared to
next six months and
job performance during the
supply the reasons for the
first year of employment.
prediction (e.g., long
Maximize
commute).
performance
Yesterday Tomorrow


Back to
Management reports Talent
Analytics

Attrition waterfall
2,161 198 9,642

10,000
8,480
801

8,000

6,000
Headcount

4,000

2,000

FY11 Attrition New starts New starts FY12
March attrition Feb


Back to
Management reports (cont.) Talent
Analytics

Performance rating movements

1
0 | 0% 2
0 | 0% 3
0 | 0%

0 | 0% 0 | 0%

0 | 0% 0 | 0%

Outstanding

Very Very
good 0 | 0% 0 | 0% good

Good 0 | 0% 0 | 0% Good
0 | 0% 0 | 0%
Average Average

FY09 000 000 000 000 000 000 000
FY08 000 000 000 000 000 000 000

0 | 0% 0 | 0%

Avg. Avg.
0 | 0%



frames.

Today

Talent
experience of job
next six months and
Maximize
commute).
performance
Yesterday Tomorrow


Back to
Employee commitment Talent
Analytics

Human Resource (HR)/ Culture Employee Switching
people practices (Drivers) (Moderators) commitment factors
(Goal/Outcome) (Commitment
moderators)
• Service leadership
• Change management
• Employee communications Employee Focus
• Immediate supervision Employee
• Employee growth and development Commitment Switching Costs
• Training and education Customer Focus • Affective
• Performance evaluation Attachment
• Recognition of employee • Willing to be
performance Community Focus Proactive
Switching Alternatives
• Compensation • Intent to Stay
• Benefits
• Career-life fit Financial Focus
• Teamwork/team management
• Diversity management
• Customer relationship management



frames.

Today

Talent
experience of job
next six months and
Maximize
commute).
performance
Yesterday Tomorrow


Back to
Retention tracker Talent
Analytics

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

40

30

20 Extremely high
High attrition risk
Relative attrition risk

attrition risk
10 segment segment

0

-10
Low attrition risk Moderate attrition risk
-20 segment segment

-30

-40
1 2 3 4 5 6 7 8 9 10
Deciles

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.


Back to
Retention tracker (cont.) Talent
Analytics

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

Organization
4%
Attrition risk projections can be analyzed
at organizational level

Business1 Business 2 Business 3
3% 2% 4%
at a business level

Team 1 Team 2 Team 3 Team 4
1% 2% 4% 5%
at a team level

Attrition risk probabilities are generated
for each employee in the organization



frames.

Today

Talent
experience of job
next six months and
Maximize
commute).
performance
Yesterday Tomorrow


Talent Analytics – A Model

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

• What kind of data is required to
analyze the issue?
• How do we look at the data to draw
meaningful conclusions?

• How are we measuring the
effectiveness of these
measures?
• Can we measure it in $ terms
and report to the business?

• What insights can we draw from the
analysis?
• Is it possible to extrapolate past
data to predict future outcomes?


Analytics - The Past and The
Future

Evolution of Analytics

Predictive
Analytics

Cross
process and
functional
Analytics

Basic
Analytics

Consolidated
reporting

Data and
Basic
Reporting


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

For example… Similarly…

A business unit uses predictive HR can use predictive models to
models to maximize ‘customer maximize ‘employee lifetime
lifetime value’ value’
• Targeting new customers • Recruiting and hiring new
employees
• Optimizing pricing, customer
service and costs • Optimizing development and
performance
• Focused customer retention
• Focused employee retention


Talent Acquisition – A Case in Point
Problem Statement: How do we review more qualified candidates, faster, with
improved accuracy, and with less cost?

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


Traditional Recruiting Data

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

Sachin Saina Rahul
10 years of work experience 10 years of work experience 10 years of work experience
4 previous employers in past 10 2 previous employers in past 10 1 previous employer in the past
years years 10 years
Current employer is large Current company is small Current company is a microchip
technology company technology company mfg. company
Attended Tier I Engineering Attended Tier II Engineering Attended Tier III Engineering
College College College
B.E. in Electronics &
B.E. in Information Technology B.E. in Computer Science
Communication
Engineering Society member NA Engineering Society member

Limitations:

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


Talent Analytics – Expand the data set

• 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)

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
20% less likely than 80% more likely than 30% more likely than
average to be a average to be a average to be a
Likelihood of future
successful hire and stay successful hire and stay successful hire and
event
with the company for 3 with the company for 3 performance rated
years years above average
• Sub-optimal
• Optimal past
• Sub-optimal
employment history employment history
employment history
• Long Commute –
• Short Commute – 1
• Medium Commute –
Top 3 reasons 40 miles 15 miles
mile
• Has been a
• Owns a house in the
• Has been a resident
resident of this city of this city for 5
city
for 2 years years
• Possible pursuit -
• Unlikely pursuit –
• Actively pursue for Second tier
Possible actions national position – (possible option for
third Tier
Primary Tier local/regional
position)

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


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 Employee Time and
Compensation Performance
Data specific data expense
• Office Address • Home Address • Hours Worked • Salary • Performance Rating
• Department • Age • Number of Training • Bonus Previous 5 Years
• Date of hire • Gender Days • ESOPs • Expected promotion
• Supervisor • Education Level • Vacation/Sick Days • Performance Rating date
• Supervisor’s • Marital Status Taken • Recognition Awards • Date of Last
Performance Rating Promotion
• Number of
Dependents • 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 • Average salary increase
• Unemployment Rate • Niche skill vs Easily replaceable skill
• Number of Talent Competitors in the same city • Additional Macro Economic Variables


Produce Attrition-Impact Reports for Employees
ILLUSTRATIVE

Sample Model Input Sample Model Output
Employee Name Saina Risk Segment High
Employee ID 1234 Risk of Leaving 88%
Location/Region Hyderabad / AP Actual Cost of Replacement $6,000
Employees in Location 6,000 Expected Cost
$84,480
Date of Hire 01-03-2006 (Risk * Actual Cost)
Rating 3 First Most Important Reason Time until
for Risk of Leaving promotion
Tenure 5 years
Supervisor’s past
Base Pay $64,000 Second Most Important
retention rate is
Reason for Risk of Leaving
Position Level Analyst low
Working Hours/Yr 1,920 Third Most Important
Long commute
Reason for Risk of Leaving
Training Weeks/Yr 2
Expected Promotion Year 2011
Vacation Days Taken to Date 16
Commute to Work > 25 miles

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 &
Ensure data requirements and data integrity are addressed.
Availability

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

Not Understanding the
Keep in mind the audience and what they might value.
―Customer’s‖ Needs

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.


Points for Discussion

Q1 At the current time,
what are your key needs in Q3 How do you think your
terms of analysis that you needs/wishes around HR
would like to be carried out analytics will evolve, when
on your HR data, and what the economy improves?
are the key outcomes and
business decisions that you
are trying to address with Needs Tools
that analysis?

Q2 What are your views in
relation to the quality and
Issues Outcomes Q4 What is your “holy
completeness of the data grail” of HR analytics that
that you have available to you would like to carry
carry out a) basic analysis out, but don’t have the
of your HR data and b) time/data/resources to do
more advanced analytics on so?
your HR data?


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 


NHRDN Virtual Learning Session on HR Analytics

NHRDN Virtual Learning Session on HR Analytics

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