HR Analytics: Shubham Singhal 80303120053 PGDM NMIMS, Hyderabad
HR Analytics: Shubham Singhal 80303120053 PGDM NMIMS, Hyderabad
HR Analytics: Shubham Singhal 80303120053 PGDM NMIMS, Hyderabad
Shubham Singhal
80303120053
PGDM
NMIMS, Hyderabad
2 Primer
Hypothesis: u0 & u1
Statistical Models
Analytics
= Business
Intelligence
Decision
6 Core concepts and terminologies
Business intelligence (BI) is a set of theories,
methodologies, processes, architectures, and
technologies that transform raw data into meaningful
and useful information for business purposes.
The costs of attrition, poor hiring, sub-optimal compensation, keeping below par employees, bad training
& learning strategies are just too high
Data-driven insights to make decisions are always better than judgmental (subjective) HR practices in
terms of
how to recruit
whom to hire
how to onboard and train employees
how they keep employees informed and engaged through their tenure with the organization
Hence regular tracking and prediction of crucial HR metrics is indispensable
Why HR Analytics?
10
What gets The business
measured, gets demands on HR are
managed; What increasingly going
gets managed, to be on analysis
gets executed Measure & Return on just because people
- Peter Drucker are so expensive
Manage Investment
- David Foster
Recruitment
Organization
effectiveness Retention
HR
Matrices
Performance &
Workforce Career
Management
Comp &
Training
Benefits
Critical areas for HR Predictive analytics
13
1. Turnover modeling. Predicting future turnover in business units in specific
functions, geographies by looking at factors such as commute time, time since last
role change, and performance over time.
2. Targeted retention. Find out high risk of churn in the future and focus retention
activities on critical few people
4. Talent Forecasting. To predict which new hires, based on their profile, are likely to
be high fliers and then moving them in to fast track programs
Trendwise Analytics HR analytics
14 capabilities
Three levels of HR analytics and reporting
Predictive Analytics
What can happen?
Reporting
What happened?
Complexity
Types of Analytical Models
16
PREDICTS
PREDICTIVE ANALYTICS
PREDICTS
Current
Future
Data
Predictive Analytics
PREDICTS
INFERENTIAL ANALYTICS
Drawing
Analysis & Monitoring Conclusions or
Past Data Inferences
REPORT
DESCRIPTIVE ANALYTICS
Representation of
Reporting Data and
Summarizing
17 Critical areas for HR Predictive
analytics
Targeted retention. Find out high risk of churn in the future and focus
retention activities on critical few people
Risk Management: profiling of candidates with higher risk of leaving
prematurely or those performing below standard.
Talent Forecasting. To predict which new hires, based on their
profile, are likely to be high fliers and then moving them in to fast
track programs
19 Tools & Software Used
Typical tools / software:
BI reporting tools
Day to
Predictive day
ANALYTIC existence
is is now
Predicting
touching being
the future
every exploited
sounds
human on by social
mystical
Earth who media
accesses and then
internet the
analytics
HCM Analytics consumers by role
Stakeholders across the organization
HR Analyst
Needs ad-hoc
capabilities to do
sophisticated
MGR
analysis and
Executives; Corporate Strategy planning Middle Managers; Line Managers
Craft and guide long term Execute on strategic plans and
workforce plan based manage organizational performance
on given information Employee to assure strategic objectives are
Needs reached timely and efficiently
x
contextual HR
data to better
perform
HRBP
$
HR Business Partner
Consult with Business Units based Finance; Controlling; Budgeting
on workforce intelligence and HR HR HR Give input regarding financial
drives action plans as final figures and receives insights for
deliverable from the process HR Administration; HR Functions midterm financial planning
Recruiting, Staffing, Talent Management regarding the workforce
and other HR functions support
fulfillment of workforce action plans
Real world case studies
22
Starbucks, Limited Brands, and Best Buycan precisely identify the value of a 0.1%
increase in employee engagement among employees at a particular store. At
Best Buy, for example, that value is more than $100,000 in the stores annual operating
income.
Many companies favor job candidates with stellar academic records from prestigious
schoolsbut AT&T and Google have established through quantitative analysis that a
demonstrated ability to take initiative is a far better predictor of high performance on
the job.
Employee attrition can be less of a problem when managers see it coming. Sprint has
identified the factors that best foretell which employees will leave after a relatively
short time.
In 3 weeks Oracle was able to predict which top performers were predicted to leave
the organization and why - this information is now driving global policy changes in
retaining key performers and has provided the approved business case to expand the
scope to predicting high performer flight .
Real world case studies
23
Dow Chemical has evolved its workforce planning over the past decade, mining
historical data on its 40,000 employees to forecasts promotion rates, internal transfers,
and overall labor availability.
Dow uses a custom modeling tool to segment the workforce and calculates future head
count by segment and level for each business unit. These detailed predictions are
aggregated to yield a workforce projection for the entire company.