Abstract
Employee attrition is one of the key issues for every organization these days, because of its adverse effects on workplace productivity and achieving organizational goals. Employee attrition means not just the loss of an employee, but also leads to the loss of customers from the organization. This in turn results in more attrition among employees due to lesser workplace satisfaction. Hence it is important for every organization to understand how to attract potential employees, retain existing employees and predict attrition early to reduce significant loss of productivity among hiring managers, recruiters, and eventual loss of revenue. High employee attrition shows a failure of organizational effectiveness in terms of retaining qualified employees. To predict attrition among employees, we propose an AI-based solution as a SaaS, because of less investment of time, effort, and cost for the companies. We will be collecting the data from various sources like HRMS, Employee Pulse Surveys, Yammer, etc. as input to our model. We intend to utilize AI/ML models like Decision Tree, SVM, Random Forest, NLP. Our model will be trained to estimate attrition risk among employees in real-time with about 95% accuracy rate.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kapoor, E.: Great resignation impact being felt in India as attrition rate remains elevated at 20% in 2022. In: Business Insider India (2022). https://www.businessinsider.in/careers/news/great-resignation-impact-being-felt-in-india-as-attrition-rate-remains-elevated-at-20-in-2022/articleshow/94453768.cms. Accessed 02 May 2023
Shinde, S.: IT industry logged 25% attrition in FY22, trend to continue: Report. Business Standard (2022). https://www.business-standard.com/article/economy-policy/it-industry-logged-25-attrition-in-fy22-trend-to-continue-report-122092900620_1.html. Accessed 02 May 2023
Raza, A., Munir, K., Almutairi, M., Younas, F., Fareed, M.M.S.: Predicting employee attrition using machine learning approaches. Appl. Sci. (Basel) 12(13), 6424 (2022)
“7 statistics on employee turnover every HR manager should be aware of,” Workforce.com. https://workforce.com/news/7-statistics-on-employee-turnover-in-2022-every-hr-manager-should-be-aware-of. Accessed 02 May 2023
“Employee retention,” Akkio. https://www.akkio.com/applications/employee-retention. Accessed 02 May 2023
“Real-time analytics for a user-facing world,” StarTree. https://startree.ai/. Accessed 02 May 2023
Plotnikov, A.: Predicting churn with AI: A playbook. DataRobot AI Platform (2020). https://www.datarobot.com/blog/predicting-churn-with-ai-a-playbook/. Accessed 02 May 2023
Rosenbaum, E.: IBM artificial intelligence can predict with 95% accuracy which workers are about to quit their jobs. CNBC (2019). https://www.cnbc.com/2019/04/03/ibm-ai-can-predict-with-95-percent-accuracy-which-employees-will-quit.html. Accessed 02 May 2023
Scanlon, N.: Reducing employee churn with a data science solution. Smartbridge (2023). https://smartbridge.com/reducing-employee-churn-data-science-solution/. Accessed 02 May 2023
Naz, K., Siddiqui, I.F., Koo, J., Khan, M.A., Qureshi, N.M.F.: Predictive modeling of employee churn analysis for IoT-enabled software industry. Appl. Sci. (Basel) 12(20), 10495 (2022)
Researchgate.net. https://www.researchgate.net/profile/Ibrahim-Yigit-4/publication/320298197_An_Approach_for_Predicting_Employee_Churn_by_Using_Data_Mining/links/59dc7932aca2728e201f79ba/An-Approach-for-Predicting-Employee-Churn-by-Using-Data-Mining.pdf. Accessed 02 May 2023
Academia.edu. https://www.academia.edu/download/58244348/A_Comparative_Study_of_Empl%20oyee_Churn_Prediction_Model.pdf. Accessed 02 May 2023
Jain, A., et al.: Career support platform for older adults powered by AI. In: 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), pp. 47–53 (2023).
Darapaneni, N., et al.: A detailed analysis of AI models for predicting employee attrition risk. In: 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), pp. pp. 243–246 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix: COST SUMMARY TO CLIENT
Appendix: COST SUMMARY TO CLIENT
Assumption:
-
All the development & storage activities happen on the client network and systems
-
Cloud platform will still be needed to support integration
-
SaaS based on the employee population within a company
-
Net Positive in year 2 of existence
-
5 year returns of 515%
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Agarwal, S., Bhardwaj, C., Gatkamani, G., Gururaj, R., Darapaneni, N., Paduri, A.R. (2023). AI Based Employee Attrition Prediction Tool. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_54
Download citation
DOI: https://doi.org/10.1007/978-3-031-36402-0_54
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36401-3
Online ISBN: 978-3-031-36402-0
eBook Packages: Computer ScienceComputer Science (R0)