{"id":78,"date":"2020-09-03T09:10:17","date_gmt":"2020-09-03T13:10:17","guid":{"rendered":"https:\/\/opentextbooks.concordia.ca\/hrmcanadian\/part\/chapter-14-talent-anaytics\/"},"modified":"2020-11-19T15:07:38","modified_gmt":"2020-11-19T20:07:38","slug":"chapter-14-talent-anaytics","status":"publish","type":"part","link":"https:\/\/opentextbooks.concordia.ca\/hrmcanadian\/part\/chapter-14-talent-anaytics\/","title":{"raw":"Chapter 11: HR Analytics","rendered":"Chapter 11: HR Analytics"},"content":{"raw":"
\r\n

The Power of HR Analytics for ACME Inc.<\/h1>\r\n<\/header>\r\n
\r\n\r\nACME Inc. has a problem, a big problem. It's bleeding employees to the rate of 18% a year. Benchmarks in their industry is half of this number, at 9%. For the past few years, new government regulations have been making this industry more competitive and profits are on a steady decline. The CEO has identified curbing the high turnover rate as a primary objective for the HR department. The VP HR hired a team of data analysts to look at the issue. The team performed advanced analysis on the anonymous corporate employees\u2019 data integrated from several HR information systems. The dataset contains common and specific HR-oriented features for 1560 individual employees regarding topics such as demographics, satisfaction with the job and the company, absences, salary and even travelling schedule. Importantly, for each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period. This information is used to identify key features connected to attrition issue. Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of this study show the way how to save a value by identifying possible causes of talents.\r\n\r\nFor the purpose of the case-study, the dataset was inspected and using logistic regression method, two different questions were answered:\r\n
    \r\n \t
  • Which specific factors increase or decrease the probability of attrition?<\/li>\r\n \t
  • Which individual employees across different jobs are in high risk of attrition?<\/li>\r\n<\/ul>\r\nRegarding the first question, advanced modelling techniques like neural networks were able to identify \u201cdrivers\u201d that influence the target variable: risk of attrition.\r\n\r\n\"\"\r\n\r\nThis figure shows the most relevant factors influencing attrition. This kind of information provides seemingly straightforward insight. However, in order to deliver more thorough and usable conclusions it is necessary to go a little bit deeper. One possible way how to do that is for example to perform such an analysis separately for different job types and roles. The next figure shows the importance of various factors for three different job categories: technician, scientist and salesman\r\n\r\n\"\"\r\n\r\n \r\n\r\n \r\n\r\nAnswering the second question -who are the employees in a danger of the attrition, a prediction model was developed and applied. For validation purposes, one third of the dataset was separated to test the model accuracy. The rest was used to train the model and perform previous analysis.\r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\n \r\n\r\nThe newly-developed model is able to predict 88.9% of employees with \"left-the-company\" flags.\r\n\r\n \r\n\r\n\"\"\r\n\r\nNow equipped with this information, the VP HR is working at addressing the issues with a concrete and aggressive plan of action to curb turnover.\r\n\r\n<\/div>\r\n
    <\/div>\r\n<\/div>\r\n \r\n\r\n \r\n\r\n ","rendered":"
    \n
    \n

    The Power of HR Analytics for ACME Inc.<\/h1>\n<\/header>\n
    \n

    ACME Inc. has a problem, a big problem. It’s bleeding employees to the rate of 18% a year. Benchmarks in their industry is half of this number, at 9%. For the past few years, new government regulations have been making this industry more competitive and profits are on a steady decline. The CEO has identified curbing the high turnover rate as a primary objective for the HR department. The VP HR hired a team of data analysts to look at the issue. The team performed advanced analysis on the anonymous corporate employees\u2019 data integrated from several HR information systems. The dataset contains common and specific HR-oriented features for 1560 individual employees regarding topics such as demographics, satisfaction with the job and the company, absences, salary and even travelling schedule. Importantly, for each individual record there is an information determining whether the corresponding employee left the company at the end of analyzed period. This information is used to identify key features connected to attrition issue. Uncovering hidden data patterns to predict present employees in the risk of attrition, outcomes of this study show the way how to save a value by identifying possible causes of talents.<\/p>\n

    For the purpose of the case-study, the dataset was inspected and using logistic regression method, two different questions were answered:<\/p>\n

      \n
    • Which specific factors increase or decrease the probability of attrition?<\/li>\n
    • Which individual employees across different jobs are in high risk of attrition?<\/li>\n<\/ul>\n

      Regarding the first question, advanced modelling techniques like neural networks were able to identify \u201cdrivers\u201d that influence the target variable: risk of attrition.<\/p>\n

      \"\"<\/p>\n

      This figure shows the most relevant factors influencing attrition. This kind of information provides seemingly straightforward insight. However, in order to deliver more thorough and usable conclusions it is necessary to go a little bit deeper. One possible way how to do that is for example to perform such an analysis separately for different job types and roles. The next figure shows the importance of various factors for three different job categories: technician, scientist and salesman<\/p>\n

      \"\"<\/p>\n

       <\/p>\n

       <\/p>\n

      Answering the second question -who are the employees in a danger of the attrition, a prediction model was developed and applied. For validation purposes, one third of the dataset was separated to test the model accuracy. The rest was used to train the model and perform previous analysis.<\/p>\n

       <\/p>\n

       <\/p>\n

       <\/p>\n

       <\/p>\n

      The newly-developed model is able to predict 88.9% of employees with “left-the-company” flags.<\/p>\n

       <\/p>\n

      \"\"<\/p>\n

      Now equipped with this information, the VP HR is working at addressing the issues with a concrete and aggressive plan of action to curb turnover.<\/p>\n<\/div>\n

      <\/div>\n<\/div>\n

       <\/p>\n

       <\/p>\n

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