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HR ANALYTICS

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: 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: HR ANALYTICS İsmail Hakkı Gedik Date 1 What is HR Analytics? What is HR Analytics? HR Analytics is; systematic identification and quantification of the people drivers of business outcomes.* *Heuvel & Bondarouk, University of Twente Data-Driven HR Data-Driven HR HR ANALYTICS PROCESS HR Analytics Process Being successful in HR Analytics; Identify your problems, Determine where you will collect the data, Clear the data and set your definitions, Identify the data analysis tool, Determine the profile (Data Scientist) to analyze. Benefits Benefits & Challenges of HR Analytics • Better hiring practices, • Task automation, • Improved employee experience, • More productive workforce, • Make better decisions using data, • Gain employee trust Challenges Challenges • Finding people with the right skillset for HR Analytics, • Data cleansing and quality, • Too much data to parse or not knowing what data is most important, • Data privacy and compliance, • Proving its worth to executive leaders, • Tying actions and insight to ROI, • Identifying the best HR technologies to keep track of the data Statistics of Large-Scale Finance Company Global Case Study The common characteristics of people with high sales performance are as follows: - No grammatical mistakes in their CV, - To complete their school without leaving them, - Having experience in real estate or auto sales, - Having had success in their previous works, - Achieving success in uncertain situations, - Time planning and ability to execute many jobs at the same time. *Article of Bersin in Forbes > 4 MILLION IN REVENUE > 4 MILLION IN REVENUE When this data analysis was put into recruitment processes, the company generated more than $ 4 million in revenue.* A Study of IBM & MIT Study of IBM & MIT Companies that implemented the predictive analysis in their HR departments achieved positive results within their business. Using Predictive Analysis to Reduce Employee Turnover of Credit Suisse Bank Credit Suisse Bank The investment banking major, Credit Suisse, deployed predictive analysis; to identify employee churn determine the reasons behind employees wanting to quit. Using these insights, Credit Suisse saved an estimated $70,000,000 a year in recruiting and onboarding costs as a result of this initiative. Suggestions for Suggestions

HR ANALYTICS

Transcript: HR Analytics TRAINING INTRO INTRO HR ANALYTICS IS USED TO IMPORVE THE PERFORMANCE OF AN ORGANIZATION BENEFITS 1 2 3 4 IMPOVES HIRING IMPROVES RETENTION ESTABLISH WORK CULTURE IMPROVES MANAGMENT ABOUT ABOUT HR analytics enables HR managers to improve their operations and decision making with data. HR analytics enables HR personnel to run advanced analytics Outcome ABOUT According to Marr (2016), capability analytics is a talent management process that enables organizations to identify the core competencies, skills, and abilities necessary to enable an organization to succeed. I believe Using HR Analytics will allow our organization to transition to the next level. Implement Implemening Analytics John Director Sarah Supervisor Dave Kate Sr. Analyst Bob HR Specialist Sr. Analyst Steve Sr. Analyst Jade HR Specialist Capabilities PROGRAMS Gap Analysis PROGRAM 1 Capabilities Gap Analysis KEY RESULTS Determine Needs determine steps needed to take to move from a current state and achieve their preferred state Growth PROGRAM 2 Capability Analytics KEY RESULTS Capabilities Analytics KEY RESULTS Identify Core Comptencies, skills, and abilities. Opprotunities for Comparrison TIMELINE TIMELINE MILESTONE 1 MILESTONE 1 DEVLEOP A STAFF IN CHARGE OF RUNNING OUR NEW SYSTEM HIGHLIGHT THIS WILL PROVIDE CONTINUITY AND ALLOW TRAINGING WITHIN THE ORGANIZATION MILESTONE 2 MILESTONE 2 REVIEW HR ANALYTICS QUARTELY TO SUCCESSFULLY COMPARE AND ASSES THE ORGANIZATION PROGRESS MILESTONE 3 MILESTONE 3 ANUALLY REVIEW AND SUBMIT OUR RECORDS TO CORPORATE OFFICE

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