Introduction to HR Analytics

Unit 1
Introduction to HR Analytics
1. Introduction to HRM
2. HR Decision-making
3. Concept and Definitions of Analytics
4. Importance and Benefits of HR Analytics
5. Steps to implement HR Analytics
6. Critical HR decision making and HR analytics
7. Predictive HR Analytics
8. Aligning HR to Business through HR Analytics
9. Steps for Alignment of HR Analytics with Business Goals and Strategies
10. HR Analytics Framework and Models
1.1 Introduction to HRM?
• Human resource management (HRM) is a strategic approach to
supporting and nurturing employees and ensuring a positive workplace
environment.
• Functions:
• Human Resource Planning
• Recruitment and Selection
• Training and Development
• Performance Management
• Compensation and Benefits
• Employee Maintenance
1.2 HR Decision Making
• Human Resource Managers should consider the four C’s of HRM
while making decisions:
• Commitment
• Competence
• Cost Management
• Compatibility
• Decision making in Human Resource Management
• Involves all people processes
• Impacts all business processes
• Connects with organizational goals
1.2 HR Decision Making
Some principles for effective HR decision-making include:
• Making the right choice
• Using the right words
• Making the right decision at the right time
• Being able to execute
Some tools that can help with HR decision-making include:
• Decision frameworks: Provide logical connections between decisions about resources and strategic success
• Decision matrices: Compare different options side-by-side

Some HR metrics that can be tracked include:

• Headcount
• Turnover
• Diversity
• Total cost of workforce
• Compensation
• Spans and layers
• Employee engagement
• Talent acquisition
1.3 Concept and definition of analytics
• Analytics is the process of discovering, interpreting, and
communicating significant patterns in data.
• Analytics helps us see insights and meaningful data that we might not
otherwise detect.
• Business analytics focuses on using insights derived from data to
make more informed decisions that will help organizations increase
sales, reduce costs, and make other business improvements.
• Specifically, business analytics refers to:
• Taking in and processing historical business data
• Analyzing that data to identify trends, patterns, and root causes
• Making data-driven business decisions based on those insights
Types of Analytics

Descriptive Diagnostic

Predictive Prescriptive
Descriptive HR analytics – What happened?
• Using statistical data, it explains and summarizes what has
happened already. It does not make any predictions for the
future.
• A couple of examples of descriptive analytics in action in HR
are:
• Analysing average number of paid time offs availed by employees
in a year.
• Comparing attrition levels of employees over the past 5 years.
Diagnostic HR analytics – Why did it
happen?
• Diagnostic analytics is based on the same data as descriptive analytics
but goes one step further and gives reasons for what happened.
• It identifies the patterns and anomalies within the data, and then study
them further to understand what factors could be contributed to them.
• Applying diagnostic analysis to the same two processes described
earlier, we can:
• Why employees in a certain demographic have availed more paid time-offs?
• Why has there been an increase in attrition over the past two years?
Predictive HR Analytics – What will happen?
• It categorizes past and present data to isolate patterns and
anomalies in them and develop a model to predict the future
based on them.
• In the process, the analytical model that is built to predict the
future is then evaluated by applying new data to it.
• Let us look at two examples of predictive analytics in HR:
• Predict the average absenteeism of employees in the next month based
on inputs for the that month over the past five years.
• Predict what channels and what locations must be targeted in recruiting
candidates with a particular skill.
Prescriptive HR Analytics – How can we
make it happen?
• Prescriptive analytics takes predictive analytics further and analyses why
something happened and what corrective measures to take to improve it
further, hence the name “prescriptive”.
• Let us revisit the same two examples for predictive analytics and see how
prescriptive analytics will modify them.
• Predict the average absenteeism of employees in the next month based on
inputs for the given month over the past five years, why it is so, and how to
reduce absenteeism by 25% in the next month.
• Predict what channels and what locations will provide candidates with a
particular skill, why they do so, and how the messaging can be tweaked to
improve the recruitment in these zones?
1.4 Importance and benefits of HR Analytics
• HR analytics is the sum of data that supports HR analysis and decision-making. With
the help of HR analytics, you don’t need to rely on anecdotal observations or
guesswork to improve the effectiveness of HR activities. Instead, you can measure and
report people
• We can use HR data analytics to identify trends and patterns in processes associated
with employee pay, benefits, training, and other areas. HR analytics can help you make
improvements in just about every area of human capital management, including:
• Recruitment and onboarding: The processes used for attracting and hiring talent.
• Compensation and benefits: The metrics and activities covering all areas of rewards and recognition.
• Performance management and engagement: The mechanisms for measuring and improving employee
performance and commitment.
• Learning and development: The activities designed to build employee skills and capabilities.data to help you
better understand your workforce.
1.4 Importance and benefits of HR Analytics
• Make Informed Decisions
• Measure the Impact of HR Policies
• Understand employees' needs, wants, and challenges
• Identify trends, patterns, and areas for improvement
• Improve their workforce processes
• Promote positive employee experience
• Identify which existing talent programs are not working
• Assess the effectiveness of new programs
• Identify common areas where employees need more training or support
• Provide useful information on trends or frequent occurrences of misconduct
1.4 Importance and benefits of HR Analytics
• Streamline recruitment and onboarding processes
• Improve payroll management
• Support diversity and inclusion goals
• Examine benefits for cost-effectiveness
• Measure the success of training and development activities
• Help build equitable compensation and benefits packages
• Enable effective workforce planning
• Easily conduct skills gap analyses
Few Examples
Revenue per Employee
• Revenue per employee measures how much money the business is bringing in for every employee it
has on staff and is paying expenses, such as salary and benefits, for. It is calculated by dividing a
company’s revenue by the total number of employees in the company. Businesses love to track this
because it provides a way to see how efficient businesses are at generating revenue for each new hire.
• Example: If a business has 100 employees and brings in $10 million in revenue, its revenue per
employee would be $100,000.

Time To Fill
• The time to fill metric measures how long it takes to fill an open position at the company. It is
calculated by counting the number of days from posting the job to someone accepting an offer. This
gives good insight into how efficient the hiring team is at finding good candidates and moving them
through the hiring process.
• Example: If a company posts a job on March 1 and completes its interviewing process, makes an offer,
and gets that offer accepted on April 20, then the time to hire would be 51 days.
Voluntary and Involuntary Turnover Rates
• These rates measure the percentage of employees who end up leaving the company. The voluntary rate calculates
the percentage of employees who decided to leave the company while the involuntary rate calculates the percentage
of employees who end up getting fired.
• While the voluntary rate measures how well the company is at retaining employees, the involuntary rate measures
how well it is at hiring the right people and managing them efficiently. Both are calculated by dividing the number
of employees who fall into each category by the total number of employees in the organization.
• Example: If 10 employees were fired in the last year, out of the 100 total employees the company had, then the
involuntary turnover rate would be 10% of employees.

Offer Acceptance Rate

• The offer acceptance rate is another hiring metric that measures how well the hiring team is at convincing the people
they want to take the job. If a company is making offers to people who are declining those offers at a high rate, then
the hiring process likely needs to be adjusted to move candidates through the hiring pipeline who are more
interested in joining the company. It is calculated by dividing the number of accepted formal job offers by the total
number of job offers made.
• Example: If the hiring team has received 10 formal job offer acceptances this year, out of 20 given out, then the
offer acceptance rate would be 50%.
Retention Rate
• In contrast to the turnover rate above, it can be important to see how well the business does at keeping
employees working for the business. This can be measured company-wide or on a per-manager level. To
calculate the retention rate, you can divide the total number of employees who decided to stay employed
over a given time period by the total number of employees over that same time period.
• Example: If a business had 100 employees in the last year and 85 decided to remain employed, the
retention rate would be 85%.

Absence Rate
• The absence rate is the total number of days an employee is absent from work, not including approved
time off such as vacation, over a specific period of time. This is also referred to as absenteeism and is
important to measure in positions where individuals call out of work at a high rate, such as retail
businesses. It is calculated by dividing the number of days worked by the total number of days that the
employee could have worked over a specific period of time.
• Example: When measuring the absence rate for June, let’s say there are 20 possible work days. Our
worker, John, worked 14 of those days and was on vacation for another three days. This means he
worked 14 out of a possible 17 days. That means he worked about 82% of the time and it gives him an
absence rate of about 18%.
 Technology used in HR
Analytics
(Software/Platforms/BI tools)
Examples of HR Analytics
• It is a programming language that is widely used for statistical analysis and visualization. It can handle
R large data sets and has a rich library of packages that enable various types of analyses and
visualizations.

• It is another programming language that can be used for data analysis. It is easier to learn than R but
Python offers slightly fewer functionalities. It also has a large number of packages that support data analysis.

• It is a spreadsheet software that is commonly used for basic data analysis. It can perform calculations,
Excel create charts, and apply filters. However, it has limitations in terms of data size, complexity, and
automation.

• A business intelligence tool that allows users to create interactive dashboards and reports from various
Power BI data sources. It has a user-friendly interface and supports data visualization, exploration, and sharing.

• Another business intelligence tool that specializes in data visualization. It enables users to create
Tableau stunning charts and graphs from different types of data. It also supports data blending, filtering, and
storytelling.
• A cloud-based HR analytics platform that integrates data from multiple sources and provides ready-
Visier made dashboards and reports on various HR topics such as workforce planning, talent acquisition,
diversity and inclusion, employee engagement, retention, performance, learning, compensation, etc.
• A cloud-based HR analytics platform that consolidates data from different HR systems into one place.
Crunchr It offers pre-built dashboards as well as customizable ones on various HR topics such as workforce
planning, talent management, employee experience, etc.
• A survey software that helps organizations collect feedback from employees on various aspects such as
Qualtrics engagement, satisfaction, well-being, culture, etc. It also provides analytics tools to analyze the survey
results and generate insights.
1.5 Steps to implement HR Analytics
Define the business problem or question that needs to be answered

Identify the data sources and metrics that are relevant to the problem or
question

Collect and clean the data to ensure its quality and validity

Analyze the data using appropriate methods and tools

Interpret and communicate the results and recommendations to

stakeholders

Implement and monitor the actions and outcomes

1.6 Critical HR decision making and HR
analytics

• Hiring
• Attrition
• Training
• Capacity
• Performance
• Anomaly
 • HR analytics can help organizations hire the right talent by using competency
acquisition analytics, recruitment channel analytics, and classification
Hiring analysis. These methods can help identify the skills and attributes that are
required for different roles, evaluate the effectiveness of different sources of
candidates, and predict the success rate of teams based on their composition.

• HR analytics can also help reduce employee turnover by using attrition

analysis. This method can help identify the factors that influence employee
Attrition retention, such as job satisfaction, engagement, performance, compensation,
etc. It can also help segment employees into different risk groups and design
targeted interventions to retain them

• With HR analytics, organizations can personalize training programs for

employees by using learning analytics. This method can help assess the
Training learning needs and preferences of employees, customize the content and
delivery of training courses, and measure the impact of training on employee
performance and behavior
 • Companies can optimize their workforce capacity and utilization
by using capacity analytics. They will be able to forecast the
Capacity demand and supply of labor, allocate resources based on skills
and availability, and monitor the utilization and productivity of
employees

• By using performance analytics, organizations can improve

Performa employee performance. This can also help set and track
performance goals, provide feedback and coaching, and reward
nce and recognize employees based on their achievements

• HR analytics can help businesses detect and prevent anomalies in

HR data by using anomaly detection analysis. They can identify
Anomaly outliers, errors, frauds, or irregularities in HR data that may
indicate potential problems or risks
1.7 Predictive HR Analytics
• Predictive data analytics are everywhere. It is in its essence a
technology that learns from existing data, and it uses this to forecast
individual behavior. This means that predictions are very specific. In the
movie Moneyball, predictive analytics were used to predict the potential
success of individual baseball players.
• https://youtu.be/AiAHlZVgXjk
• Predictive analytics involves a set of various statistical (data mining)
techniques that analyze historical data and outcomes. These techniques
then try to create a formula, or algorithm, that best mimics these
historical outcomes. This algorithm then uses current data to predict
outcomes in the future.
1.8 Aligning HR to Business through HR
Analytics
• People analytics can help make informed decisions in HR-related areas. For
Data analysis example, HR analytics can help measure the impact of employee engagement on
financial performance.
• HR analytics can help organizations predict future workforce needs and optimize
Workforce planning
workforce productivity.

• HR analytics can help with strategic planning, solve business problems, and
Strategic planning
streamline HR functions.

Business objectives • HR analytics can help management align strategies with business goals.

Action plans • HR analytics can help HR development make better decisions using data.

Change management • Change management can help align IT activities with business objectives.

HR strategy • HR analytics can help align HR strategies with business goals.

1.9 Steps for Alignment of HR Analytics
with Business Goals and Strategies
• Identify key HR metrics that align with organizational goals
• Monitor progress towards achieving goals
• Manifest satisfaction levels, intention to quit, OCB, organizational
commitment, and productivity
• Reduce turnover
• Plan for the future with confidence
• Plan changes and help implement changes in an organization
• Provide higher-quality candidates
• Focus on the strategic needs of the business
1.10 HR Analytics Framework and Models
1) LAMP framework
2) 8-Step Framework for HR Analytics
i) LAMP Framework
• The LAMP framework is a strategic compass for HR analytics that
helps companies identify and evaluate factors that affect employee
performance. It also helps companies understand how employee
behavior affects overall performance.
 • This framework element refers to the methodical and
logical process of discovering the pertinent data and
Logic metrics required to assess and enhance employee
performance.

• This part of the framework examines the determined

Analytics data and metrics to gather knowledge and spot patterns
and trends.

• The exact metrics and indicators used to track and

Measures evaluate employee performance are included in this
framework section.

• This part of the framework refers to how the measuring

system is implemented and managed, including how
Process goals are created, progress is tracked, and decisions are
made in light of the data.
ii) 8-Step Framework for HR Analytics
(Adopted from Quantum Workplace)
https://www.quantumworkplace.com/future-of-work/people-analytics-frame
work
• Understand
• Identify
• Collect
• Clean
• Analyze
• Extract
• Communicate
• Evaluate
 • What business problem are we trying to address?
• Are we asking the right questions?
Understand • Who will champion this project?
When beginning an HR data • What are the expected or hopeful outcomes of this project?
analytics project, the first step • Will the results of this project lead to any meaningful
is to understand your changes?
purpose. • Who will be impacted most, least, or not at all by these
results?

• What types of data do we need?

• Behavioral data (e.g., absenteeism)
Identify • Perceptual data (e.g., employee engagement)
• Demographic data (e.g., tenure)
After you have a strong
• Is the data structured or unstructured?
understanding of the project,
• Structured (e.g., numeric data)
you should identify the data
• Unstructured (e.g., text, audio, or video)
and sources of data needed to
• Does the data already exist, or does new data need to be
carry out the project.
created?
• What needs to be done to gather the necessary data?
 Collect
• Conduct interviews, focus groups, or surveys
After identifying the • Work with other departments to obtain necessary data
kinds and sources of • Track and document obstacles to keep in mind for future
data you need, collect projects

data.

• Data points that don’t belong (e.g., numbers where text should
be)
Clean • Incorrect data (e.g., a value of 12 in a 10-point scale)
• Inconsistent data (e.g., a manager has 5 direct reports, but has
Before you can analyze data for 6 direct reports)
your dataset, it needs to • Extreme outliers (e.g., a value of 100 when the next highest
be cleaned. value is 15)
• Excessive amounts of missing data
 • Always start with descriptive analytics. This will help you to
familiarize yourself with the data.
Analyze • Never jump straight into diagnostic or predictive analytics. If
This is the step people you do, you might overlook important insights that could render
your advanced analytics unreliable or useless.
typically think of when they • Think of statistics and machine learning like toolsets. Each
see “HR Analytics.” analysis is a separate tool, which means certain tools are better
for answering certain questions.

Extract • Which results are noise?

Analysis is about creating • Which results tell the most important or relevant stories?
• Do the results support your hypotheses or expectations?
results from data, and
• Are any results surprising?
extraction is about
• Did any results spark thoughts for additional analyses or
determining which results are projects?
meaningful.
 Communicate • Only to your immediate manager
After going through the previous • To individual department and team leads
steps, now is the time to share • To the senior leadership team
what you’ve found. This step • To the entire organization
depends entirely on the project • No matter who you present to, make sure your points are clear, concise,
and your role. You might explainable, and rely on visuals whenever possible.
communicate insights:

Evaluate • The Project: What went well with this project? What didn’t go well?
HR analytics projects never • Follow-through: Were any plans or strategies put into action?
really end because new data is • Trends: Have employee behaviors or perceptions changed? What do the
always being created. As such, an trends look like at 3, 6, 12 months after your project?
evaluation schedule should be • Impact: What impact did your project have on employees
created to assess: