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Predictive Analytics for HR: A Primer to Get Started on Your HR Predictive Analytics Journey
Dr Susan EntwisleDistinguished 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 biasEveryday 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 leadershipand culture
Nirvana might be perfect blend of domain experts, analytics and psychometrics
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Big data – people data everywhereLarge 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 maturityMoving 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
ModelTraining Data
Pre-processingNormalisation
Dimension reductionImage processing
Etc.
LearningSupervised
UnsupervisedSemi-supervisedRe-inforcement
Error AnalysisPrecision
Over fittingTest validation data
New DataModel 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/Red2. Type : Fruit3. Shape etc…
Features:1. Sky Blue2. Logo3. Shape etc…
Features:1. Yellow2. Fruit3. Shape etc…
Input model for learning and testingOptimisation techniques: genetic, exhaustive, stepwise refinement
What do you mean by Apple?
Network designNetwork 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 engagementIdentify 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 modelEmployee 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 & Actions2. Model set-up and
deployment1. 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 EntwisleHPE Distinguished TechnologistEnterprise ServicesM: [email protected]
