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Transcript: HR Analytics INTRO INTRO HR ANALYTICS IS THE PROCESS OF COLLECTING & ANALYZING HUMAN RESOURCE DATA TO IMPROVE THE PERFORMANCE OF AN ORGANIZATION OUTCOMES 1 2 3 4 JOB PERFORMANCE IMPROVES RETENTION REVENUE & PROFITABILITY CUSTOMER EXPERIENCE ABOUT ABOUT HR Analytics provides data-backed insight of what is working well and what is not to plan more effectively for the future. Metrics ABOUT HR metrics are measurements that aid in tracking key areas in HR data. Key areas in HR metrics Organizational performance HR operations Process optimization Methods METHODS Cost to Hire Cost to Hire KEY RESULTS KEY RESULTS Cost to recruit & hire new employees Determine efficiency of recruitment process Monitor over time to track typical costs Revenue per Employee Revenue per Employee KEY RESULTS KEY RESULTS Indicator of quality of hired employees Dividing revenue by total number of employees indicates average revenue employees generate. Shows efficiency of the company as a whole Engagement Rating Engagement Rating KEY RESULTS KEY RESULTS One of the most important HR outcomes Measure employee productivity & satisfaction to gauge level of engaged employees Engaged employees perform better and are more likely to perceive stress as a challenge Turnover Turnover KEY RESULTS KEY RESULTS Combine with performance metric to track difference in attrition between high/low performers Attrition numbers can be key metric in measuring a manager's success Monitor over time and compare to companies acceptable rate or goal UTILIZATION UTILIZATION COLLECT DATA COLLECT DATA VITAL COMPONENT OF ANALYTICS COLLECT/TRACK HIGH QUALITY INFORMATION COMPARE DATA COMPARE DATA COMPARE & MEASURE DATA TO IDENTIFY PATTERNS KEY METRICS / ORGANIZATIONAL PERFORMANCE TURNOVER ABSENTEEISM RECRUITMENT OUTCOMES ANALYZE DATA ANALYZE DATA RESULTS IDENTIFY TRENDS & PATTERNS THAT HAVE AN IMPACT ON ORGANIZATIONS ANALYTICAL METHODS DESCRIPTIVE PREDICTIVE PRESCRIPTIVE APPLICATION APPLICATION FINDINGS USED FOR ORGANIZATIONAL DECISION MAKING PROCESS CAN BE USED CONTINUALLY TO IMPROVE PERFORMANCE

HR Analytics

Transcript: HR Analytics Critical HRM By Neha Mattu Introduction "HR Analytics is the only future for Human Resource Managers. No HR analytics means no human resource managers" What is it? “HR analytics as creating value when providing analytical outputs that are relevant to decision makers’ immediate business issue” (Ellmer & Reichel, 2021). “HR analytics is the use of people data and analytics tools to understand work and the workforce…to measure and report on key aspects of HR activity, including performance management, engagement and remuneration” (CIPD, 2017) “In its simplest definition, HR analytics is the data related to people…aiming to add strategic value to the organisation” (Jackson et al., 2014) What does it measure? Diversity Metrics Performance Metrics Revenue Metrics Productivty Oriented Metrics Turnover Costs Sickness Absence Engagement Metrics Tabent/Potential Metrics Benefits Uncover skills gap Gain competitive advantage Task Automation Improving employee experience Developing talent Intergrating HR analytics with the entire organisation Easily accessible information for management Hackman & Oldham Hackman & Oldham Job Charateristic Model Manager Benefits Manager Benefits 2 Employee Advancement/Progression 1 Understand employee issues 3 Aid Advacnement decsions Aberdeen Research “The use of HR analytics helped companies to achieve higher results in the range of 8%-15% for customer satisfaction, customer retention and revenue per employee” (Lombardi & Laurano, cited by Soundararajan & Singh, p.17 2012) Drawbacks Hard to measure Access to right information Cost Difficulty integrating data Employee concerns “The most frequently cited reason that HR analytics is not more widely adopted is the shortage of analytically skilled HR professionals” (Marler & Boudreau, p.18, 2017) Marler & Boudreau “Although many organisations have begun to engage with HR data and analytics, most have not progressed beyond operational reporting” (Angrave, Charlwood, Kirkpatrick, Lawrence & Stuart, p.4, 2016) Angrave Conclusion Thank you for listening

HR Analytics:

Transcript: We alter the scorecard as hotels reach their year end: Change sickness to Average Days per Employee Remove commitment to RBH Allow GMs greater input into their targets Employee Survey I changed my mind numerous times while the survey was live! The Future: To develop a way of emulating the continual feedback processes of Revinate so we have an ever changing score. We can use this to demonstrate the effectiveness of HR Initiatives eg CS week DEEP DIVE Analytics We have a strategic approach to entering industry and professional awards, which gains us credibility and showcases our ability People Scorecard Promoting our skills To provide manco with a tool to look more closely at hotel performance, we provide a suite of reports which provide greater detail than the scorecard - leaver reasons, absence reasons, time to fill... Eventually this all links to Cognos and we can compare systems so we can demonstrate the impact of labout turnover on heartbeat scores for example There's no point... if no-one understands it! We need to demonstrate that we have the best professionals around! We want people saying "Wow, look at who is working for RBH" I wont be the only person who knows where the data came from, or who can run the reports! Training Transparency Toolkits External Recognition The whole HR team commit to networking and supporting our industry and profession through participation in industry events and initiatives such as Inspiring The Future HR Analytics: data which tells a story Best in Class HR

HR Analytics

Transcript: Circuit City Company Circuitcity: Bankruptcy in 2008.​ Global economic downturn. Closing underperforming outlets and firing staff. ​ Close all of its remaining locations.​ In March 2009 : close its remaining 567 stores in the country. Displacing almost 34,000 workers.​ Liquidation process: selling off inventory to pay off some of its obligations.​ Circuit City's name and intellectual property were sold to other organizations after the closure. ​ The company was resurrected as an online merchant in 2016. Overview & Analysis Reference: Circuit City: SWOT Analysis -​ Downsize its workforce to reduce costs. ​ Offer competitive compensation and benefits, and provide opportunities for professional development.​ HR policies accommodate remote work. Appoint an experienced, full-time project leader: report to the CEO or division manager—> watch people grow and coach them.​ Use the techniques of conventional project management.​ Define stages from the planning phase through the closure; and conduct regular reviews involving senior leaders.​ Well-being and mental health : assistance programs, wellness programs, and mental health resources.​ Decrease Expenses Reference: The Right Way to Close an Operation ( Recommendations Halting recruitment​ Voluntary unpaid leave​ Temporary furloughs​ Consolidating departments or reassigning employees to different roles.​ Identify non-essential expenses that can be reduced or eliminated. ​ Workforce planning : monitoring market conditions, sales & demand patterns, headcount actions.​ Energy-saving measures such as turning off unnecessary lights and equipment.​ Maintain open lines of communication with employees, keeping them informed.​ Benefits:​ Restore Financial Stability​ Improve Efficiency​ Improve teamwork​ Re-evaluate business​ Security to existing employees​ Initiative Measures References: hrQ Blog | Managing Costs through Workforce Planning (​​ Initiative: Freeze hiring​ Estimated Timeline: 3 to 6 months​ Limitations:​ Losing employees​ Bad Publicity​ Responsibilities Burdening​ No chance of promotion. ​

HR Analytics

Transcript: HR ANALYTICS The problem statement THE PROBLEM STATEMENT Description The business consist of various departments each of which is progressing with a manual cycle of the promotion We are required to support the HR in predicting who is anticipated to be promoted The client necessitates guidance with data driven decisions to facilitate the promotion process The client necessitates guidance with data driven decisions to facilitate the promotion process This project is focused on acknowledging whether or not the employee will be promoted and the probability associated to the promotion This project is focused on acknowledging whether or not the employee will be promoted and the probability associated to the promotion Aims & Objectives AIMS & OBJECTIVES Recognize factors influencing promotion Understand other variables determining the promotion Develop an accurate model that can forecast the probability that an employee is expected to be promoted Why do we need a data driven decision ? THE NEED FOR A DATA DRIVEN DECISION Decisions based on instincts are not baseless as they are strengthened by previous knowledge and information and intuitive judgments have worked in the past Importance of data Decisions are not about trusting your instincts and braving the consequences but are about combining inner wisdom with scientific data The most effective and efficient decisions Today the business environment is highly complex and involves huge amounts of processing thus there is a need to rely on data to expect decisions that are free of cognitive biases Thus a combination of data backed Rational analysis and intuitions produces well rounded decisions Factors affecting promotion Factors affecting promotion The variables available in the data is promoted employee Id region education gender recruitment channel no.of trainings age previous years ratings length of services KPI awards won average training score Intuitive analysis depicts that employee id,region and department should not affect promotion Chi sq analysis claims that all the other factors than the 3 mentioned above affects promotion Data Analysis ANALYSIS OF THE DATA The data comprises of 14 variables with 38,918 data points The data is free from null/missing value Target Variable The dependent variable is 'is_promoted' The dependent variable is described as 'is_promoted' here 0 implies that the employee is not promoted and 1 implies that the employee is promoted Target variable 0(35533) 1(3385) GRAPH Graph Breakdown Of Promotion Breakdown Of Promotion Feature Importance Feature Importance Using sklearn.ensemble extratrees classifier we can predict the feature importance Correlation is a statistic that measures the degree to which two variables move in relation to each other and here there are no two variables that are highly correlated thus there is no need to drop any variable Graph 1 Feature Importance Correlation Correlation Pre -Processing PRE-PROCESSING On the analysis of the data it can be said that our target variable suffers from class imbalance problem as there is a huge difference between promoted and not promoted This can be resolved using a technique called SMOTE (synthetic minority oversampling technique) SMOTE SMOTE SMOTE is a commonly applied oversampling approach to solve the imbalance problem. It intends to balance class distribution by randomly increasing minority class examples by replicating them. On applying this techniques to various classifiers using gridsearchCV we found the best splitting ratio for our model One Hot encoder One Hot encoder The data type of variables such as gender,recruitment channel and education is in object form thus there is a need to convert it into numeric form with the motive of fitting them in the machine learning model Pandas get_dummies was applied to convert the object variables into numeric form Train Test Split The data was spilt in the following manner : 70% 30% Training Testing Train test Split DATA Model Selection As the data now has been processed we are compelled to fit this data into numerous classifiers and discover a classifier that delivers the best accuracy with F1 score, precision and recall with the best ROC-AUC score We have used five classifiers for training and testing our data and those are Model Selection Random Forest Random Forest Decision Tree Decision Tree XGboost XGboost ADA boosting ADA Boosting Click to edit text Gradiant boosting Gradiant Boosting On analyzing all the classifiers we can conclude that Gradiant Boosting is giving us the best scores

HR Analytics

Transcript: AI at Vallourec Today AI in HR Conclusion HR ANALYTICS What is HR Analytics Why HR Analytics? HR Analytics is the process of collecting, analyzing, and reporting relevant HR information to make data-driven decisions. To conduct this analysis, we use key performance indicators (KPIs) to measure the performance of the workforce, and human resources information systems (HRIS) to standardize the reporting process KPIs Human Resources Infomation System (HRIS) HRIS HRIS is a software that allows companies to collect data Why do we need a data driven decision ? We can say that previously, decisions based on instincts are not baseless as they are strengthened by previous knowledge, previous information and intuition which,have in many cases, they've worked in the past.... THE NEED FOR A DATA DRIVEN DECISION Levels of HR Analytics Importance of data We need to change our way of thinking....Decisions are not about trusting your instincts and braving the consequences instead.......they are about combining inner wisdom with scientific data The most effective and efficient decisions Today the business environment is completely different than what it was almost three years ago. This creates the need to rely on data to expect decisions that are free of cognitive biases This is why a combination of data backed analysis and intuitions produces well rounded decisions We have come a ways.... Descriptive analytics deals with the process of collecting and reporting data on what has already happened. Descriptive Diagnostic analytics aims to determine the causes of certain problems or trends. Identifying the issues is necessary for solving the problem or providing information to plan Diagnostic Another example Predictive analytics deals with forecasting future events by using historical data patterns. It helps determine the probability of something happening in the future by analyzing previous data. It is obtained through various statistical techniques, Predictive Prescriptive analytics deals with proposing suggestions for future actions according to the prediction made via predictive analytics. The suggestion proposed by prescriptive analytics are based on data analytics therefore, more reliable. Prescriptive UKG Analytics UKG Analytics We have a very strong analytics tool in Cognos BI (UKG Pro) : Real-time Data Permissions/Security Role Based Scheduling Capabilities With the migration to UKG Dimensions: Reporting has been enhanced Dataviews are more flexible Analytics have been made available to you and managers Report Tool Dynamic Dashboards UKG Dimensions Why Power BI? Power BI and HR In the past couple of years, Vallourec has adopted, we can in some cases even say, strongly encouraged, the use of Power BI within Operations, Controlling, VDIS and so on. What about HR? Data Visualization Power BI enables us to create visually compelling dashboards and reports, making it easier to interpret and communicate HR data effectively Data Visualizations Example Real-Time Insights Real-time Insights With Power BI's real-time data analysis, we can access critical metrics instantly, enabling data-driven decision-making. Self-service Analytics Self-service Analytics Power BI's user-friendly interface empowers us to explore data independently Ease of use Demo Let's go in and look at a few examples Demo Benefits of Power BI in HR Summary Making data more accessible to those other than management.​ Use of information to assist in decision making and actions to meet KPI goals.​ Access to these dashboards starts today. Scan QR Code Artificial Intelligence Overview AI Knowledge View -Results AI: The development of computer systems capable of performing tasks that typically require human intelligence What is AI and ML? What is AI/ML? ML: A subset of AI that involves algorithms and statistical models enabling computers to learn from data and make predictions without explicit programming. How is AI used in our every day life? It is more common than you might realize..... Everyday Life MS Office At work Maps/GPS Commute Movies/TV/Music Entertainment IBM, HR, and Machine Learning Utilize past HR dataset for model training, containing employee attributes and outcomes.​ Automatically assign weights to variables based on importance during model training.​ Apply ML model with weighted variables to predict employee attrition and other HR outcomes. Feature Importance Outcome Confusion Matrix Applications of AI at Vallourec Key Resignation Indicators​ Correlation & Company Analysis​ Individual Analysis Tool Use AI powered insights to assist in decision making​ Identify “At-Risk” individuals and perform targeted actions to mitigate turnover. Example of Output AI Data Displayed in Power BI (example) Our Focus Continue building on our data analytics for the benefit of the team but more importantly, for the benefit ofVallourec Work with all of the functional areas to make data accessible via easy interfaces. These include but are not limited to: Recruiting

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