HR Analytics – Demystified!ARUN KRISHNAN, PH.D,

FOUNDER & CEO,

nFactorial Analytical Sciences

What is Analytics?information resulting from the

systematic analysis of data or statistics

Since when have we

been doing analytics?

Daniel Kahnemann & Amos Tversky

System 1 System 2

Solve: -5 +2 x 2 + 9 /3 - 8

What words would you choose

to describe her?

So why the buzz around analytics

now?

Technological advances - price / performance

Pervasive digitization

Artificial Intelligence, Machine Learning

Big Data and Analytics

What is Big

Data?

" One bit more data

than your system can

hold"

Source: www.cloudlendinginc.com

Analytics is a continuum …

Complexity

Perspective

Low

Past

High

Future

Bu

sin

ess

Va

lue

ReportingWhat happened?

AnalysisWhy did it happen?

MonitoringWhat is happening now?

PredictionWhat will happen?

...and Analytics is a journey!

Source: Applied Insurance Analytics, by Patricia Soparito

Analytics Domains

Retail Sales

Marketing

Collections

Telecom

Financial

ServicesRisk & Credit

Consumer

Behavior

Fraud

Supply Chain

Talent / HR

Pricing

Web

Football AnalyticsAN EXAMPLE

What happened?

Why did it happen?

What’s happening now?

What could happen in future?

HR Analytics

Analytics is coming to HR!

Source: www.bersin.com

Why HR Analytics?

Measure &

Manage

"What gets measured,

gets managed; what

gets managed, gets

executed"

- Peter Drucker

Linkage of

Business

Objectives to

People

Strategies

- HR Dashboards - SAP

"To clearly

demonstrate the

interaction of business

objectives and

workforce strategies"

Return on

Investment

- David Foster

"The business demands

on HR are increasingly

going to be on

analysis just because

people are SO

expensive"

Performance

Improvement

- CedarCrestone

"Global organizations

with workforce

analytics and

workforce planning

outperform all other

organizations by 30%

more sales/employee"

HR capability gaps are increasing

Source: Deloitte Human Capital Report, 2015

HR Analytics - Much promise -

wanting in rewards?

Source: Deloitte Human Capital Report, 2015

The HR Analytics Continuum

Complexity

Perspective

Low

Past

High

Future

Bu

sin

ess

Va

lue

Head Count

Attrition

Training

Payroll reports Performance

Tracking

Requisition

TrackingTurnover Ratio

Accession Ratio

Low performer

managementPromotion Ratio

Hiring Fit

Hiring No-shows

Prediction

Attrition

Prediction

Attrition

Segmentation

Employee

Segmentation

Candidate

Stickability

Prediction

High Performer

Segmentation

Workforce

Planning

Informal Network

Analysis

Voice of Employee

Analysis

Recruitment

Engagement

Retention

What metrics do we typically

track?

Source: Bersin & Associates 2012 – US research

What metrics should we track?

Recruitment RetentionPerformance Management

CareerManagement

TrainingWorkforce

Planning

Comp &

BenefitsOrg.

Effectiveness

Measuring Human Resources

Management

Over 100 different metrics

across

Hiring and Staffing

Compensation and Benefits

Training and Development

Employee Relations and

Retention

So how about some recruitment-

related metrics to start with?

Cost

•Cost per hire

•Source cost per hire

•Advertising cost per hire

•Agency cost per hire

•Referral bonus per hire

•Unsolicited no-cost per hire

•Special costs per hire

• Interview costs

•Source cost per hire per interview

•Sign-on bonus factor

Time

•Response time

•Average response time per hire

•Time to fill

•Time to start

•Referral factor

Career Development

•Job posting response rate

•Job posting response factor

•Job posting hire rate

• Internal hire rate

•Career path ratio - promotions

•Career path ration - transfers

Efficiency Metrics

•Average interview length

•Hire rate

•Hit rate

Quality

•Quality of Hire

•Recruiter Effectiveness

Detailed Case StudyGOOGLE

The early days - Finding the right

people

Spent hours screening resumes

from job portals like Monster.com

Built an applicant tracking system

that checked candidate resumes

against a database of Googler

resumes

Idea was to get more realistic

"backdoor" references

Also looked at innovative ways to

identify smart peopleThe solution to the first riddle will land you at http://7427466391.com/. On this

page you’ll find the following:

“Congratulations. You’ve made it to level 2. Go to www.Linux.org and enter

Bobsyouruncle as the login and the answer to this equation as the password.”

f(1)= 7182818284

f(2)= 8182845904

f(3)= 8747135266

f(4)= 7427466391

f(5)= __________

Initial data analysis & insights

• Academic grades did not correlate well with performance except for the first 2-3 years.

Analysis

• Stopped asking for academic transcripts except for fresh graduates.

Actions

• Did not see any discernible drop in performance because of this.

Results

Initial data analysis & insights - 2

• Google's hiring was focused on minimizing "false positives", that is, candidates who looked good at first glance but turned out to be poor performers later.

• Their hiring took a long time - 250,000 hours to hire 1000 people/year

Analysis

• Looked at referrals as a way of hiring great candidates.

Actions

• In the initial years - >50% of hires were through referrals

Results

Employee referrals

•The rate started to fall after 2009

Challenge

•Could be because rewards weren’t high enough.

Hypothesis

•Google increased the reward for successful referrals thinking that it would help to bring up the referral rates

Actions

•They found however, that this brought NO change in the decline.

Results

•Rewards are extrinsic motivators

•People were more motivated by intrinsic factors like pride in their place of work

Analysis

Employee referrals

• Exhausted known networks

Challenge

• Started using aided recalls

Action

• Volume of referrals increased by 33%!

Results

Cultivating the best people

• Requirement of ~300,000 referrals/year vs <100,000 they were getting

Challenge

• Realized that the very best people are not looking for work. They are happy

Analysis

• Rejigged their staffing team and equipped them with a home-grown tool called gHire to cultivate people across different organizations.

Action

• >50% of Google's hires are found by this in-house team!

Results

Hiring the best people

•People during an interview make up their mind in the first 10 seconds

•Rest of the interview is spent finding corroborative evidence

•CONFIRMATION BIAS!

Challenge

•Most interviews are unstructured.

•Unstructured interviews can predict only ~14% of an employee's performance

•Work sample test predicts ~29% of performance

•General cognitive tests predict ~26% of performance

•biased towards white males (at least in the US) !

•Structured interviews were found to be as good at predicting performance as cognitive tests

Analysis [paper by Frank Schmidt & John Hunter]

•Use a combination of behavioral and situational structured interviews with assessments of cognitive ability, conscientiousness and leadership

• Identified key attributes essential for "Googleeyness"

Actions

•Consistent scoring mechanism that allows people to compare across interviewers. .

Results

Hiring the best people - 2

•Hiring was taking too much time – median of 90-180 days

Challenge

•What should be the number of rounds of interviews?

• Found that 4 interviews were enough

Analysis

•Brought down the number of interviews from 25 to 4

Action

•Changed median time to hire from 90-180 days to 47 days!

Results

Revisit assumptions - Then test!

Looked for people with high scores who were rejected

2010 - ran 300,000 rejected candidates through the system

Filtered 10,000 and chose 150

Hit rate of 1.5% > 0.25% - Google's hit rate

Tested False Negatives as well!

Revisit program

•Feed resumes of all past candidates through algorithm

Common Keywords

•Assess common keywords found

Score resumes

•Score keywords based on their occurrences in rejected vs successful resumes

Test

•Score resumes over next 6 months against weighted keywords

Predictive Analytics for Recruiting

Some Examples

Best Buy

Could precisely identify a 0.1% increase in employee engagement among employees at a particular store.

This value was identified at more than $100,000 in the store's operating income.

Oracle / Sprint

Oracle was able to predict which top performers would leave and why.

This information is now driving key global policy changes for retaining key performers.

Sprint has identified the factors that best foretell which employees will leave after a

relatively short time.

Dow Chemicals

Has evolved its workforce planning over the past decade, mining historical data on its 40,000 employees to forecast promotion rates, internal transfers, and overall labor availability.

Dow uses a custom model to segment its workforce and calculates future headcount by segment and level for each business unit.

Dow can engage in "what if" scenario planning altering assumptions on internal variables.

State-of-the art for Predictive

Analytics in Recruitment

Hiring Fit

Models

Candidate

Stickability

Predictions

Hiring No-

Shows

Predictions

Workforce

Planning

Personality

Matches

Predictive Modeling - Watchouts!

All models are wrong! Some are less wrong than others

Predictive Models cannot be used to predict rare, black-swan events

Models can’t predict what is not already present in the training data.

Building the right model depends on the question that needs to be answered.

This in turn determines the data that needs to be gathered.

Even with enough data, we might not have the “right” data to build a good predictive model.

Exploratory data analysis and Feature Selection is an extremely critical part of the model building workflow.

Always check model performance using any of confusion matrix, p-values, ROC curve etc.

Keep updating your model as and when new data comes in.

Keys to success in HR Analytics

Start with the business problem in

mind

Develop culture of

data-driven decision making

Empower line leaders

Be transparent

Analytics is a journey,

not an end

Don't wait for the perfect

data

You don't HAVE to

automate everything -at least at

first

Deliver Actionable

Business Information

Thank you for your patience!