Q3 Technologies worked with the client in manufacturing space for predicting the attrition risk score at employee level to identify the high risk employee in their various departments across various geographies.

Client Background

The client is the largest manufacturer and exporter of colored gemstones and studded jewelry with manufacturing facilities in India and China, sales channels in the US and the UK and operations in more than 10 countries at present. The client has 24 hours jewelry D2H TV channels running in the UK, Germany and USA.

Industry Landscape

The client wanted an application for managing their bidding requirements and jewelry item sales. They required a single code base for the multiple applications used to manage auctioning, sales, PnP, discounts, return and refunds, manifesting, customer management, and administration. The current bidding application was divided into two parts, web-based and windows-based. It allowed users to create, schedule, and exhibit auction items through live TV, call centers, or IVR, manage orders, generate invoices, and manage payments.


Nowadays, the businesses are located at various geographical locations but this should not hamper the productivity of the organization. Therefore, the client wanted to upgrade their application in order to provide better manageability through a single codebase at all the locations. Also, the client wanted to optimize the performance of the application with the latest technologies.


Q3 Technologies worked with the client for predicting the attrition risk score at employee level to identify the high risk employee in various departments across geographies.

However, to effectively understand the employee’s situation and prolong for successful career with the organization the BU leaders and key stakeholders needed a view of risk score across the key attrition reason across industry.

Q3 Technologies proposed one possible way of effectively using the risk score for realizing the end result of retaining the right staff.

Business Impact

Predicting employee turnover – what will be the turnover in the coming periods and which will be the leading factors resulting to such trends?
Employee risk scores which employees are most likely to attrite in the near future and what steps should be taken to retain them?
Effect of retention – How did the retention steps undertaken effect the attrition predicted? How to loop the feedback into the predictive model to refine the results?


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