Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry
Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/20/10495 |
| _version_ | 1797475546850918400 |
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| author | Komal Naz Isma Farah Siddiqui Jahwan Koo Mohammad Ali Khan Nawab Muhammad Faseeh Qureshi |
| author_facet | Komal Naz Isma Farah Siddiqui Jahwan Koo Mohammad Ali Khan Nawab Muhammad Faseeh Qureshi |
| author_sort | Komal Naz |
| collection | DOAJ |
| description | Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations. |
| first_indexed | 2024-03-09T20:46:40Z |
| format | Article |
| id | doaj.art-680dac049e544c74b524d418056eff50 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:46:40Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-680dac049e544c74b524d418056eff502023-11-23T22:45:38ZengMDPI AGApplied Sciences2076-34172022-10-0112201049510.3390/app122010495Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software IndustryKomal Naz0Isma Farah Siddiqui1Jahwan Koo2Mohammad Ali Khan3Nawab Muhammad Faseeh Qureshi4Department of Information Technology, Government College University, Hyderabad 71000, PakistanDepartment of Software Engineering, Mehran University of Engineering and Technology, Jamshoro 76020, PakistanCollege of Software, Sungkyunkwan University, Suwon 16419, KoreaDGIP, 785400, PakistanDepartment of Computer Education, Sungkyunkwan University, Seoul 03063, KoreaEmployee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations.https://www.mdpi.com/2076-3417/12/20/10495employee churn analyticsmachine learningprediction analytics |
| spellingShingle | Komal Naz Isma Farah Siddiqui Jahwan Koo Mohammad Ali Khan Nawab Muhammad Faseeh Qureshi Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry Applied Sciences employee churn analytics machine learning prediction analytics |
| title | Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry |
| title_full | Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry |
| title_fullStr | Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry |
| title_full_unstemmed | Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry |
| title_short | Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry |
| title_sort | predictive modeling of employee churn analysis for iot enabled software industry |
| topic | employee churn analytics machine learning prediction analytics |
| url | https://www.mdpi.com/2076-3417/12/20/10495 |
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