Typically, employee attrition is a major challenge for companies worldwide. The objective of this project was to predict employee attrition for a Multinational software-based company on parameters like Supervisor, wages, distance from home, tenure with company, travelling, overtime etc. This helped the client to make better decisions to curtail employee attrition rate. Current employee related data is collected from company’s internal databases / HR systems using Robotic Process Automation (RPA).
User-Friendly, cloud-based AI platform
No coding skills are required for results or predictions.
Provides you a list of % dependency features that can be used to optimize your business
Ability to fine-tune or optimize the models by trying different algorithms/prediction attributes
Easy integration with other third-party solutions such as TABLEAU for data visualization
Reduced turnover hassle.
Decreased acquisition and training time.
Continuity in talent acquisition/retention.
Increased overall productivity.
Better customer experience.
Employees leave organizations for various reasons and this causes morale issues and ongoing disruptions for internal teams. In order to address this, companies are turning to AI to help minimize attrition. The client is a US based Multinational Software Company that deals with computer hardware, middleware and software. The objective was to predict employee attrition considering parameters like wages, distance from home, tenure with company, overtime, supervisor name etc. This helped them to make better HR decisions to curtail employee attrition rates. After a detailed evaluation of their data, AI Labs (www.ailabsinc.com) used its proprietary Minsky AI Engine to optimize the models by using a combination of AI algorithms and prediction attributes. In this case, Minsky used historical data for model creation and predicted employee attrition rate by using real time employee data. This solution was optimized and implemented in less than a week.
Typical Sales Forecast Challenges:
Meeting customer deadlines.
Making a continuous impact on growth and stability of an organization.
Investing time and money in training individuals to train them in all aspects only to find out that they want to leave the organization.
Difficult to find replacements to exactly match the unique skill set. At the desired wages.
Difficulty in managing the shifts.
After comprehensive evaluation of the client‘s challenges, we used Minsky to accurately model historical data to reduce employee attrition rate. This process includes historical data of past employees who left the company. Once the AI Models were generated by Minsky for the selected Algorithms, current employee related data was used by Minsky to forecast employee attrition possibilities. The current employee related data is collected using Robotic Process Automation (RPA) from company’s internal database. Prediction data Reports/ Dashboards was generated by integrating Minsky with 3rd Party data visualization tools like Tableau. In addition, RPA was also used to email employee attrition prospect reports generated by Minsky to desired personnel.