Traditionally, HR teams have made decisions on hiring, retaining, assigning and developing employees using intuition, experience, and basic descriptive statistical reports. Predictive analytics complements and extends on these approaches by enabling HR teams to make proactive ‘forward-looking’ data-driven decisions on its people across the employee lifecycle. Examples of this include gaining insights into the drivers and predicting who are our top performers, what employees are at risk of leaving, is our training program effective, and more. This capability can support HR teams to better align HR programs with strategic business goals. This presentation outlines the limitations with current approaches and explain what predictive analytics is so business users can understand the business opportunity and problems it can be applied to. A number of case studies on its use across the employee lifecycle are described and guidance given on how to get started on your HR predictive analytics journey.

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Predictive Analytics for HR: A
Primer to Get Started on Your HR
Predictive Analytics Journey
Dr Susan Entwisle
Distinguished Technologist
Hewlett Packard Enterprise

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Agenda
• Cognitive decision making
• Predictive analytics
• HR analytics use cases and industry examples
• Question and answers

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Cognitive decision making
Human brain has two cognitive decision-making systems.
Thinking Fast: System One (Default)
• Quick, automatic, emotional and intuitive
• Subject to human cognitive biases
• Examples: detecting hostile body language, judging distance
between objects
Thinking Slow: System Two
• Slow, conscious, deductive and logical
• Deliberate effort required
• Prone to analysis paralysis
• Examples: parking car, solving mathematical equations
Thinking, Fast and Slow, Daniel Kahneman, 2013.

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Thinking fast cognitive bias
Everyday we make thousands of decisions using system one thinking. Faster, easier but
prone to implicit human bias that influence our decisions.
Facial recognition -
stereotypes
Attractive People -
Earn 3 – 4% more
Focus on recent
events
First 10 seconds

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Mitigations to address cognitive bias
Cognitive bias cannot be eliminated but it can be reduced
through the use of:
• Methods and processes
• Tools and checklists
• Regular structured reviews
• Use of analytics
• Use of evidence-based studies
• Use of psychological assessments e.g. myer briggs
• Promoting understanding how we think - metacognition
• Promoting a culture of critical thinking

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Moneyball for Human Resources
40-50% of companies revenue spent on
payroll
Right people into the right jobs, make
them productive and happy, and get them
to help us attract more customers and
drive more revenue
Requires fundamental shift in leadership
and culture
Nirvana might be perfect blend of domain
experts, analytics and psychometrics

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Big data – people data everywhere
Large or complex data sets – increased range of data sources, data volume, and rate of change. New
data methods and tools.
Information Management Reference Architecture, KPMG, 2015.

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Analytics – four stages of maturity
Moving beyond descriptive statistics to predictions
Talent Analytics Maturity Model, Bersin by Deloitte, 2012.
Understand data to gain insights on our
people.
Insights support better decisions about
our people.
Most HR departments range from maturity
level 1 to 3.
Get good results with descriptive statistics.
Predictive analytics offers outstanding
results and new ways HR can deliver
business value.

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What is predictive analytics?
Predictive analytics enables data-driven predictions about the future. Uses techniques from statistics, data mining,
machine learning and artificial intelligence to analyse current and historical facts to make predictions about
future.
Phase 1: Learning
Phase 2: Prediction
Model
Training Data
Pre-processing
Normalisation
Dimension reduction
Image processing
Etc.
Learning
Supervised
Unsupervised
Semi-supervised
Re-inforcement
Error Analysis
Precision
Over fitting
Test validation data
New Data
Model Predicted
Data
Introduction to Machine Learning, Twitter, Rahul Jain, 2014.

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Building a neural network – supervised learning
Introduction to Machine Learning, Twitter, Rahul Jain, 2014.
Features:
1. Color:
Radish/Red
2. Type : Fruit
3. Shape
etc…
Features:
1. Sky Blue
2. Logo
3. Shape
etc…
Features:
1. Yellow
2. Fruit
3. Shape
etc…
Input model for learning and testing
Optimisation techniques: genetic, exhaustive, stepwise refinement
What do you mean by Apple?
Network design
Network parameters: number layers, activation function
Network guesses output for each input row in learning set. If correct,
greater weighting is given to network connection between hidden layers
that were joined to create correct prediction.
Output: neural network model, input importance

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Types of insights and prediction
Introduction to Machine Learning, Twitter, Rahul Jain, 2014.
Classification: identify what category a new object belongs to from a set of
pre-defined categories.
Regression: predict value (real number) from observations. Popular method
is linear regression.
Clustering: group together a set of objects in such a way that objects in the
same group are more similar to each other. Popular methods are hierarchical
and k-means clustering.
Linear regression
Hierarchical clustering
K-means clustering

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Focus on business value, not the data
Enhance Scale Accelerate
People, Knowledge, Capabilities
Cognitive Computing, Jouko Poutanen, IBM, 2016.

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Predictive analytics across employee lifecycle
Better Hiring
Pre-employment screening
Predictive model to identify
candidates who are more likely
to perform better and stay
longer based on performance
requirements and cultural fit.
Identify optimal role(s)
Predictive model to identify
optimal roles types within the
company for a candidate.
Higher Growth
Employee engagement
Identify key drivers for employee
engagement and use to classify
employees in groups.
Customer satisfaction and
employee engagement linkage
Identify metrics of customer
satisfaction and employee
engagement that have strong
linkages.
Workforce planning
Develop predictive models and
run simulations to calculate
future headcount requirements
by business unit, which can be
rolled up to company level.
Attrition Mgmt.
Attrition prediction model
Key drivers to attrition and
employee attrition probability
prediction, for proactive
management.
Top talent hunt
Predictive model to help identify
top talent in company.
Training & Education
Key factors improving learning
outcomes
Identification of key factors that
drive improved learning
outcomes.
Training forecasting
Develop predictive models and
run simulations to determine
training requirements based on
workforce planning inputs.

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Case study: HPE attrition model
Employee level attrition probability prediction, for proactive management
Context
Understand drivers for attrition at across
HPE
HP 300,000+ employees (original)
Flag employees that are a high-flight risk
Identify actions to be suggested to managers
Approach and impact
• Implementation
across HR,
engineering and
‘high-rated’
populations
• Estimate business
impact from better
planning
• Evolve analytical
model using logistic
regression
• Test model accuracy
using out of sample
and out of time data
• Employee level
information including
salary, age, role,
career progression,
bonus, and more
were used
• Confidentially
maintained through
usage of masked ids
3. Insights & Actions
2. Model set-up and
deployment
1. Data collection
Identified savings of $300 M associated with 1% reduction in attrition and
related improvement in productivity and replacement costs

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Case study: top talent hunt
Predictive model to help identify top talent from within HP executives
Context
Talent analytics model to:
• Understand characteristics of top talent at HP
• Identify executives with these characteristics
Approach involving:
• Relevant data sources including internal
(BlueBook, Talent Data Science reports, Talent
Universe) and external sources
• Segmentation of executives based on
performance and talent characteristics
Approach and impact
• Review model
periodically, based
on new data points
available
• Scoring (e.g. logistic
regression /
classification) model
using current talent
pool
• Predicting potential
leaders from
executive base
• Identifying and
sourcing key data
across performance
(e.g. rating, role,
promotion) and
talent (e.g.
Leadership skills,
market calibration)
parameters
• Data clean-up and
test
3. Tracking and
refinement
2. Model set-up and
deployment
1. Data gathering
Model expected to help improve succession planning across HP,
including efficiency and effectiveness

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Starbucks, Limited Brands, and Best Buy – can 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 costs.
Many companies prefer job candidates with outstanding academic records from prestigious
schools. Google and AT&T have established through quantitative analysis that a demonstrated
ability to take initiative is a better indicator of high-performance on the job.
Next Evolution of Talent Analytics, Human Capital Analytics, Conference, February 2013.
Industry case studies
Salesforce.com have adopted wearable technology into their corporate wellness program.
Salesforce.com are measuring the outcomes of this program using both employee engagement
and sales metrics. Does an employee who is active during the day close more deals? How does a
good nights sleep impact the number of quality customer engagements?

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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 labour availability.
Dow uses a custom modelling 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.
Dow can engage in ‘what if’ scenario planning altering assumptions on internal
variables, such as employee staff promotions or external variables such as legal
considerations.
Next Evolution of Talent Analytics, Human Capital Analytics, Conference, February 2013.
Industry case studies

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Getting started
1.Develop a strategy for HR analytics: assess current state, develop a vision for
the future state, define roadmap for program of work, achieve alignment among
stakeholders.
2.Execute a series of pilots for HR analytics programs: to elaborate requirements,
business value, design, build and deploy. Irrespective of whether the programs are
strategic reports, executive dashboards, workforce plans, or predictive models.
3.Evaluate pilots and update strategy: as needed, to support continuous
improvement.

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Question and answers

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Thank you
Dr Susan Entwisle
HPE Distinguished Technologist
Enterprise Services
M: +61409533694
susan.entwisle@hpe.com