2  Designing HR Metrics

2.1 Overview of Designing HR Metrics

Designing HR metrics is a critical process in aligning human resources practices with organizational goals. The goal of creating HR metrics is to measure the effectiveness of HR activities, identify areas for improvement, and drive decision-making based on data. In today’s data-driven business environment, HR professionals must go beyond anecdotal evidence and subjective observations, using quantifiable metrics to evaluate the impact of HR strategies on business outcomes.

  • Effective HR metrics are not just numbers—they are tools that provide valuable insights into how HR contributes to organizational success. By designing meaningful metrics, HR professionals can help the organization monitor and enhance its talent management strategies, recruitment processes, employee retention efforts, and overall workforce productivity.

  • The process of designing HR metrics involves clearly defining the objectives of HR activities, selecting the right data sources, creating actionable and insightful metrics, and ensuring that these metrics align with the organization’s strategic goals. Once designed, HR metrics help organizations track performance, identify trends, and make informed decisions regarding their human resources initiatives.

  • In this section, we will explore the key steps involved in designing effective HR metrics, how to align them with business objectives, and the tools that can be used to transform raw data into actionable insights. Whether it’s measuring employee engagement, tracking recruitment efficiency, or calculating turnover rates, designing the right metrics is essential for driving HR success.

2.2 Steps to create meaningful and actionable HR metrics.

Creating meaningful and actionable HR metrics involves a structured approach that aligns with the organization’s strategic goals and HR objectives. HR metrics are essential for evaluating the effectiveness of HR practices and ensuring that HR is contributing to the organization’s overall success. Below are the key steps to create such metrics:

1. Define the Objective or Purpose

The first step in creating HR metrics is to define the objective behind the metric. Ask the following questions:

  • What specific HR issue or objective do you want to measure?
  • How does this metric align with the organization’s strategic goals (e.g., employee productivity, retention, engagement)?
  • What action do you want to take based on the metric results?

For example:

  • If the objective is to reduce turnover, the metric could be turnover rate.
  • If the objective is to improve recruitment efficiency, the metric could be time-to-fill.

2. Align Metrics with Organizational Goals

Metrics must be aligned with both the HR strategy and the business strategy of the organization. This ensures that HR activities are contributing to the larger goals of the business, such as increasing profitability, improving employee satisfaction, or optimizing workforce productivity.

  • Example: If the company is focused on employee engagement, metrics like employee satisfaction score and employee engagement index can help assess how HR practices are supporting this goal.

3. Choose the Right Data Sources

To calculate meaningful HR metrics, accurate and relevant data sources must be identified. Some data sources include:

  • HRIS (Human Resource Information System): This is the central repository for employee data.
  • Surveys: Employee engagement, satisfaction, or exit surveys can provide valuable data.
  • Performance Reviews: Data on employee performance is useful for metrics like goal achievement and employee productivity.

Ensure that the data is reliable, valid, and accessible for consistent measurement over time.

4. Select the Appropriate Metric Type

There are different types of metrics, and choosing the right one is crucial. These can be broadly classified into:

  • Descriptive Metrics: Provide historical data and give insights into past performance.
    • Example: Turnover Rate, Absenteeism Rate
  • Diagnostic Metrics: Help identify the cause of an issue or performance gap.
    • Example: Exit Interviews and Employee Feedback to determine why employees are leaving.
  • Predictive Metrics: Use historical data to predict future trends.
    • Example: Retention Forecasting or Future Hiring Needs based on trends.
  • Prescriptive Metrics: Recommend actions based on data.
    • Example: Training Needs Analysis that prescribes skills development programs based on gaps.

5. Make Metrics Actionable

Actionable metrics provide clear insights that can drive decisions and improvements. It is important that HR professionals can translate the results of metrics into actionable steps.

  • Example: If the time-to-fill metric is too high, an actionable step might be to streamline the interview process or improve the candidate sourcing methods.

Make sure that:

  • Metrics are easy to interpret.
  • They have clear benchmarks or targets that are tied to business goals.
  • The results suggest specific actions that can be taken.

6. Set Benchmarks and Targets

To determine whether a metric is good or needs improvement, set benchmarks and target values. Benchmarks could be:

  • Internal benchmarks: Compare against previous performance or historical data.
  • External benchmarks: Compare against industry standards or best practices.

For example, if the time-to-fill for a position is 40 days, the target may be to reduce it to 30 days based on industry standards or internal performance goals.

7. Regularly Review and Update Metrics

HR metrics should not be static. As business goals, workforce dynamics, and HR practices evolve, metrics should be reviewed and updated regularly. This includes:

  • Checking whether the metrics still align with the organization’s strategic goals.
  • Refining the metric definitions and data sources as the organization grows and changes.

Ensure that metrics are adaptable to new challenges and emerging trends, such as the growing importance of employee well-being or remote work efficiency.

8. Communicate the Results Effectively

Once the metrics are calculated, they should be communicated clearly and effectively to stakeholders. This can include HR teams, department heads, and senior leadership. Use data visualization tools such as:

  • Dashboards
  • Charts (Bar charts, Line charts, Pie charts)
  • Reports

Metrics should be presented in a way that allows for easy interpretation and decision-making, avoiding overwhelming the audience with raw data.

9. Analyze and Take Action

The ultimate goal of creating HR metrics is to take informed action. HR professionals should analyze the results in relation to the organization’s goals, identify patterns, and make recommendations for improvements.

  • Example: If a high turnover rate is identified in a specific department, an action plan might be developed to address the root causes, such as poor management, lack of career development opportunities, or low employee engagement.

10. Track Progress and Evaluate the Impact

Finally, HR metrics should help organizations track progress over time and evaluate the impact of actions taken. Regular monitoring of key metrics allows HR professionals to measure the success of interventions and make further adjustments as needed.

  • Example: If an organization implements a new recruitment process to reduce time-to-fill, they can track this metric over several months to see if the new process leads to a decrease in time-to-fill.

2.3 Aligning metrics with organizational goals

Aligning HR metrics with organizational goals is a crucial step in ensuring that HR initiatives support the broader objectives of the business. The purpose of any HR metric is not only to track HR activities but also to demonstrate how those activities contribute to the overall success of the organization.

This alignment ensures that HR professionals are focusing on the right priorities, addressing business needs, and driving the company’s strategic vision forward.

To effectively align HR metrics with organizational goals, HR professionals need to follow these key steps:

  1. Understand the Business Strategy:

HR metrics should be grounded in the organization’s mission, vision, and strategic goals. By understanding the long-term direction and priorities of the company, HR professionals can ensure that their metrics are supporting those goals. For example, if the company’s goal is to expand into new markets, HR might focus on recruitment metrics that measure the ability to hire skilled employees in those specific regions.

  1. Identify Key HR Activities:

The next step is to identify which HR activities directly impact the organizational goals. Whether it’s talent acquisition, employee development, retention, or performance management, understanding which HR functions are most critical for business success helps HR professionals design metrics that will track progress in these areas.

  1. Select Relevant Metrics:

Metrics should be carefully chosen to reflect both the organization’s needs and the outcomes HR wants to measure. For example, if the goal is to improve employee retention, the retention rate metric will help HR track progress.

Similarly, if the focus is on improving workforce productivity, HR can align performance metrics that reflect individual and team outputs.

  1. Ensure Actionability:

HR metrics should be actionable, meaning they must provide insights that lead to informed decision-making. For instance, a metric like “Time-to-Fill” is useful in recruitment because it helps HR identify bottlenecks in the hiring process, which can be addressed to streamline operations.

  1. Set Targets and Benchmarks:

    Once the metrics are defined, it’s important to set realistic targets and benchmarks. These targets should align with the organization’s strategic goals, helping HR track progress and make adjustments as needed. For example, if the goal is to reduce employee turnover, HR should set a target turnover rate and monitor it over time to evaluate progress.

  2. Communicate with Stakeholders:

    Regular communication with key stakeholders, such as senior leadership and department heads, is important to ensure that HR metrics remain aligned with the evolving business objectives. Stakeholders should be involved in selecting metrics, setting targets, and understanding the insights derived from these metrics to ensure that the HR team is focusing on the right priorities.

  3. Review and Adjust Regularly:

    Organizational goals and priorities may change over time. HR metrics should be reviewed regularly to ensure they remain relevant and aligned with any shifts in business strategy. This may involve refining existing metrics or introducing new ones to measure emerging business needs.

2.4 Hands-on Exercise: Defining metrics and KPIs using Excel.

Objective: Learn how to use Excel for calculating and analyzing basic HR metrics.

2.4.1 HR Dataset Generation:

To simulate a realistic organizational dataset for HR metrics, responses from 500 employees were generated across several key dimensions.

Here’s an overview of the random data generation process.

  • In Excel, Create a new workbook and rename the first sheet as random data.
  • Create the following columns and use the formula given below to generate the values for the columns.
1. Employee id:
  • Create employee id’s as E001,E002,…,E500. Enter the following formula in first cell of Employee id.
Code
="E" & TEXT(ROW(A1), "000")
  • Drag the values below to 500 employee id’s.
    • ROW(A1): Returns the row number of the current cell, starting from 1.
    • TEXT(ROW(A1), “000”): Formats the row number as a 3-digit number, e.g., 001, 002, etc.
    • &: Concatenates the letter E with the formatted row number to create an ID like E001, E002, etc.
  • After creating the column, to make data analysis easier, select the entire column and assign it a name (e.g., EmployeeId). This will create a Named Range for the column, allowing you to refer to the column by its name instead of using the standard column reference.
  • Similarly, assign names to all the following columns to create Named Ranges for easy reference in your data analysis.
2. Department:
  1. Create a new sheet (Sheet2):

Add a column for Department and enter a list of departments:

Department
Software Engineer
HR Manager
Marketing Manager
Finance Manager
Data Analyst
  1. Go to Sheet1 (Random Data):
  • Under the Department column, enter the following formula to randomly assign departments:
Code
=INDEX(Sheet2!$A$2:$A$6, RANDBETWEEN(1, 5))
  • INDEX function retrieves a value from a specified range based on the row and/or column number provided.
    • Sheet2!$A$2:$A$6 is the array (the range containing the department names).
    • RANDBETWEEN(1, 5) dynamically generates a random row number between 1 and 5.
    • The INDEX function retrieves the department name from the randomly selected row.
3. Job title:
  1. Update Sheet2:
  • Next to the existing Department column, add a new column named Job Title and populate it with corresponding job titles.

Below is the mapping of departments and their corresponding job titles:

Department Job Title
IT Software Engineer
HR HR Manager
Marketing Marketing Manager
Finance Finance Manager
Analytics Data Analyst
  1. Go to Sheet1 (Random Data):
  • Under the Job Title column, enter the following formula to assign job titles dynamically based on the Department column in Sheet1:
Code
=XLOOKUP([@[Department]],Sheet2!$A$2:$A$6,Sheet2!$B$2:$B$6,NA,0)
  • [@[Department]]: Replace this with the cell reference for the Department column in Sheet1.
  • Sheet2!$A$1:$A$5: The range of the Department column in Sheet2.
  • Sheet2!$B$1:$B$5: The range of the Job Title column in Sheet2.
  • NA: The value returned if the department doesn’t exist in Sheet2.
4. Job Posting Date:

Under the Job Posting Date column in your dataset, enter the following formula to generate random job posting dates:

Code
=DATE(RANDBETWEEN(2018, 2022), RANDBETWEEN(1, 12), RANDBETWEEN(1, 28))
  • DATE function creates a valid date using the year, month, and day provided.
  • RANDBETWEEN(2018, 2022): Randomly generates a year between 2018 and 2022.
  • RANDBETWEEN(1, 12): Randomly generates a month from January (1) to December (12).
  • RANDBETWEEN(1, 28): Randomly generates a day between 1 and 28 to avoid invalid dates for months like February.
5. Hire Date:

Under the Hire Date column in your dataset, enter the following formula to calculate the hire date based on the job posting date:

Code
=[@[Job Posting Date]]+ RANDBETWEEN(15, 90)
  • [@[Job Posting Date]]: Refers to the Job Posting Date for the current row in the Excel table.
  • RANDBETWEEN(15, 90): Generates a random number of days between 15 and 90.
  • +: Adds the random number of days to the Job Posting Date, resulting in a random Hire Date.
6. Join Date:

Under the Join Date column in your dataset, enter the following formula to calculate the employee’s joining date based on the hire date:

Code
=[@[Hire Date]]+ RANDBETWEEN(1, 15)
  • [@[Hire Date]]: Refers to the Hire Date for the current row in the Excel table.
  • RANDBETWEEN(1, 15): Generates a random number of days between 1 and 15. +: Adds the random number of days to the Hire Date, resulting in a random Join Date that is between 1 and 15 days after the Hire Date.
7. Exit Date:

Under the Exit Date column in your dataset, enter the following formula to calculate the employee’s exit date or indicate they are still active:

Code
=IF(RAND() < 0.5,  [@[Join Date]]+ RANDBETWEEN(30, TODAY()- [@[Join Date]]), "-")
  • This formula randomly assigns an exit date for approximately 50% of employees while marking the rest as active (“-”).
  • IF(RAND() < 0.5, ...): Generates a random number between 0 and 1, and if it is less than 0.5, the formula proceeds to calculate an exit date; otherwise, it returns “-”.
  • [@[Join Date]]: Refers to the Join Date for the current row in the Excel table.
  • RANDBETWEEN(30, TODAY()- [@[Join Date]]): Generates a random number of days between 30 and the number of days between Join Date and today’s date.
  • +: Adds the random number of days to the Join Date, resulting in a random Exit Date between 30 days and today.
  • -: If the RAND() value is not less than 0.5, the formula returns a hyphen (”-”), indicating no exit date.
8. Status:

Under the Status column in your dataset, enter the following formula to assign a status based on the employee’s exit date:

Code
=IF([@[Exit Date]]= "-", "Active", "Exited")
  • This formula evaluates the Exit Date to determine if the employee is Active or Exited:
  • [@[Exit Date]]= "-": Checks if the Exit Date column contains a dash (“-”), indicating the employee is still active.
  • "Active": If the condition is true, the employee is marked as Active.
  • "Exited": If the condition is false, the employee is marked as Exited.
9. Recruitment cost:

Under the Recruitment Cost column in your dataset, enter the following formula to generate a random recruitment cost:

Code
=RANDBETWEEN(1500, 5000)
  • Generates a random value between 1,500 and 5,000 to simulate recruitment expenses for each employee.
  • The range can be adjusted to reflect your organization’s typical recruitment costs.
10. Training Program:
  1. In sheet2, create a column for training program like the below.
Department Job Title Training Program
IT Software Engineer Advanced Excel Skills
HR HR Manager Leadership Development
Marketing Marketing Manager Conflict Resolution
Finance Finance Manager Communication Skills
Analytics Data Analyst Team Building Workshops
  1. Goto sheet1 (random data), Under the Training Program column in your dataset, enter the following formula to randomly assign training programs to employees:
Code
=INDEX(Sheet2!$C$2:$C$6, RANDBETWEEN(1, 5))
  • The INDEX function retrieves a value from the Training Program column (column C) in Sheet2.
  • RANDBETWEEN(1, 5) randomly selects a row index between 1 and 5 within the range to assign a training program.
11. Training Completion Status:

Under the Training Completion Status column in your dataset, enter the following formula to randomly assign the training completion status:

Code
=IF(RAND() < 0.7, "Completed", "Not Completed")
  • RAND(): Generates a random number between 0 and 1.
  • If the random number is less than 0.7 (70% probability), the status is set to “Completed”.
  • Otherwise, it is set to “Not Completed” (30% probability).

Generated HR Data

Below is a sample HR dataset with randomly generated values for illustration:

Employee ID Department Job Title Job Posting Date Hire Date Join Date Exit Date Status Recruitment Cost Training Program Training Completion Status
E001 IT Software Engineer 01-Jan-2020 25-Jan-2020 01-Feb-2020 - Active 3,500 Advanced Excel Skills Completed
E002 HR HR Manager 15-Mar-2019 01-Apr-2019 05-Apr-2019 20-Dec-2023 Exited 4,000 Leadership Development Not Completed
E003 Marketing Marketing Manager 10-Jul-2021 30-Jul-2021 05-Aug-2021 - Active 2,800 Conflict Resolution Completed

2.4.2 HR Metrics Calculation

  1. Time to Fill: This metric measures the average number of days taken to hire employees across all positions. It provides insights into the efficiency of the recruitment process.

    • Shorter Time-to-Fill indicates an efficient recruitment process, helping organizations quickly address workforce needs.
    • Longer Time-to-Fill may highlight bottlenecks or inefficiencies in the hiring process that require attention.

  2. Average Time to Fill: This metric measures the average number of days taken to fill all open positions within an organization. It helps assess the overall efficiency of the recruitment process.

  • A shorter Average Time to Fill indicates a quick and efficient recruitment process, allowing the organization to fill positions faster and minimize operational disruptions.
  • A longer Average Time to Fill may suggest inefficiencies in the recruitment process, such as lengthy interview rounds, delayed decision-making, or lack of qualified candidates.

  1. Turnover Rate: This is the percentage of employees who leave the organization within a specified time frame. It is a critical metric for understanding employee retention and organizational stability.

  2. Retention Rate: This metric calculates the proportion of employees who remain with the organization during a specific period. It highlights the organization’s ability to retain talent.

  3. Training Completion Rate: This metric assesses the percentage of employees who successfully complete their assigned training programs. It is an indicator of the effectiveness and engagement in professional development initiatives.

Formulae to calcualte the metrics

  1. Time to Fill (Recruitment Data):
  • Insert a new column in sheet1(random data), name it “time to fill”. Enter the below formula under this column.
Code
=[@[Hire Date]]-[@[Job Posting Date]]
  • The formula calculates the difference in days between these two dates, providing the time taken to fill a vacancy.
  1. Average Time to Fill (Recruitment Data):
  • In a new cell, calculate the average time to fill using the formula below:
Code
=AVERAGE(Timetofill)
  • [@[Time to Fill]]: Refers to the existing “Time to Fill” column that has the individual time-to-fill data for each position.

Formula Logic:

  • Uses the AVERAGE function to calculate the mean of all the time-to-fill values from the “Time to Fill” column.
  • This gives the overall average time it takes to fill positions across the dataset.
  1. Turnover Rate (Turnover Data): In a new column, calculate the turnover rate by using the below formula.
Code
=(COUNTIF(Status,"Exited") / COUNTA(Status)) * 100
  • The formula divides the number of exited employees by the total number of employees and multiplies by 100 to get the turnover percentage.
  • COUNTIF(Status, "Exited"): Counts the number of employees with a status of “Exited” in the dataset.
  • COUNTA(Status): Counts the total number of employees (both active and exited).
  1. Retention Rate (Turnover Data): In a new column, calculate the retention rate using the formula below:
Code
=((COUNTA(Status) - COUNTIF(Status,"Exited")) / COUNTA(Status)) * 100
  • COUNTA(Status): Counts the total number of employees in the dataset, including both active and exited employees.
  • COUNTIF(Status, "Exited"): Counts the number of employees with a status of “Exited”.
  • Formula Logic:
    • Subtracts the count of exited employees from the total employee count to determine the number of active employees.
    • Divides the number of active employees by the total number of employees and multiplies by 100 to calculate the percentage of employees retained.
  1. Training Completion Rate (Training Data): In a new column, calculate the training completion rate using the formula below:
Code
=(COUNTIF(TrainingCompletionStatus,"Completed") / COUNTA(TrainingCompletionStatus)) * 100
  • COUNTIF(TrainingCompletionStatus,"Completed"):
    • Counts the number of employees who have completed their assigned training programs.
  • COUNTA(TrainingCompletionStatus):
    • Counts the total number of employees assigned to training programs.
  • Formula Logic:
    • Divides the number of completed training programs by the total assigned training programs.
    • Multiplies by 100 to calculate the percentage of training completion.

2.5 Assignment-1 HR Metrics calculation

2.5.1 Objective 1: Create the following HR Variables

To prepare a dataset in Excel for analyzing key HR metrics, create the following variables and populate them with data based on the scenarios provided.

  1. Total Work Days
    • Represents the total number of working days in the analysis period for each employee. This is fixed for every employee.
    • For a year with 5 working days per week, This can be a fixed value such as 260 for all employees.
  2. Absentee Days
    • Represents the number of days the employee was absent during the analysis period. This should be less than total work days.
    • Considering 20 working days per month, it should be between 0 and 20.
Code
=RANDBETWEEN(0, 20)
  • RANDBETWEEN(0, 20): Generates a random integer between 0 and 20, representing the Absentee Days for an employee, with the value being a random number of days the employee was absent during the analysis period.
  1. Gender
    • Indicates the gender of the employee.
    • Example: Male, Female, Other
Code
=CHOOSE(RANDBETWEEN(1, 3), "Male", "Female", "Other")

  • RANDBETWEEN(1, 3): Generates a random integer between 1 and 3.

  • CHOOSE(...): Chooses one of the values (“Male”, “Female”, or “Other”) based on the random number generated. If RANDBETWEEN returns 1, “Male” is selected; if 2, “Female” is selected; if 3, “Other” is selected.

  • This formula assigns a random gender from the three options.

  1. Training Cost
    • Represents the cost incurred to train the employee.
Code
=RANDBETWEEN(1200, 2500)
  • RANDBETWEEN(1200, 2500): Generates a random integer between 1200 and 2500, representing the Training Cost for an employee, with the value being a randomly assigned amount for the cost of training the employee.

Example Data:

Total Work Days Absentee Days Gender Training Cost
260 15 Male $1,500
260 5 Female $2,000
260 20 Other $1,200

2.5.2 Objective 2: Calculate the following Metrics

1. Time to Onboard

  • Measures the time between the Hire Date and the Join Date, indicating how quickly new hires are integrated into the organization.
Code
=[@[Join Date]]-[@[Hire Date]]

Use Case: Optimize the onboarding process to ensure a smooth transition for new employees.

2. Total Tenure or Retention Period

  • The total length of time an employee worked in the organization, calculated from the Join Date to the Exit Date (if exited) or the current date (if active).

Use Case: Assess employee loyalty and identify trends in retention.

Code
=(IF([@Status]="Exited",[@[Exit Date]]-[@[Join Date]],TODAY()-[@[Join Date]]))/30
  • IF([@Status]="Exited", ...): Checks if the Status of the employee is “Exited”.
  • [@[Exit Date]]-[@[Join Date]]: If the Status is “Exited”, calculates the total Tenure of the employee by subtracting the Join Date from the Exit Date.
  • TODAY()-[@[Join Date]] - Join Date: If the Status is not “Exited” (i.e., the employee is still active), it calculates the Tenure by subtracting the Join Date from the current date (TODAY()), giving the employee’s length of service up to the present day.
  • Finally divided the whole by 30, to convert it from days to months.

3. Recruitment Cycle Time or Time to Fill

  • The total time taken to complete the recruitment process, from Job Posting Date to Hire Date.
Code
=[@[Hire Date]]-[@[Job Posting Date]]
  • Use Case: Evaluate and streamline the recruitment process.

4. Average Employee Tenure

  • The Average Employee Tenure is calculated for all employees in the dataset, and it gives an overall measure of how long employees typically stay with the organization before exiting.
Code
=AVERAGE(TotalTenureinM)
  • Use Case: Identify departments or teams with shorter average tenures and investigate potential issues.

5. Onboarding Efficiency Rate

  • Measures the time efficiency of the onboarding process relative to the total recruitment cycle.

Individual

Code
=[@[Time to onboard]]/[@[Time to fill]]*100

Overall

Code
=(SUM(Timetoonboard) / SUM(Timetofill)) * 100
  • Use Case: Identify and optimize bottlenecks in the onboarding process.

6. Exit-to-Onboard Ratio

  • Compares the number of exits to the number of new hires (onboarded employees).
Code
=(COUNTIF(Status, "Exited") ÷ COUNTIF(Status, "Active")) × 100
  • Use Case: Analyze turnover relative to onboarding rates.

7. Workforce Stability Index

  • The proportion of employees retained relative to those who exited.
Code
=(COUNTIF(Status, "Active") ÷ COUNTA(Status)) × 100
  • Use Case: Monitor workforce stability and ensure organizational continuity.

8. Recruitment Lag

  • The gap between the Job Posting Date and the Join Date.
Code
=[@[Join Date]]-[@[Job Posting Date]]
  • Use Case: Understand the overall time taken to fill a position and onboard a new hire.

9. Turnover Cost Estimate

  • Calculates the estimated cost of turnover, considering recruitment and onboarding expenses.
Code
=(AVERAGE(RecruitmentCost)+AVERAGE(Onboardingcost))*COUNTIF(Status,"Exited")
  • Use Case: Evaluate the financial impact of employee turnover on the organization.

10. Absenteeism Rate

  • The percentage of total workdays missed by employees relative to their available workdays.
Code
=[@[Absentee Days]]/[@[Total Work Days]]*100
  • Use Case: Identify attendance patterns and assess the impact of absenteeism on productivity.

11. Gender Diversity Rate

  • The proportion of female employees in the workforce, expressed as a percentage.
Code
=COUNTIF(Gender,"Female")/COUNTA(Gender)*100
  • Use Case: Monitor workforce diversity and inclusiveness to support organizational equality goals.

12. Cost of Employee Turnover

  • The total cost associated with employee exits, including recruitment and training expenses.
Code
=IF([@Status]="Exited",[@[Recruitment Cost]]+[@[Onboarding cost]]+[@[Training Cost]],0)
  • Use Case: Quantify the financial impact of employee turnover to evaluate retention strategies.

Submission

  • Save the file as RollNo_HRMD_Assignment1.xlsx (Ex: 416_HRMD_Assignment1.xlsx).
  • Submit the dataset with at least 100 rows of data for analysis.

2.5.3 Output file for Assignment 1:

LINK