Retaining Critical Talent in Telecom: How Predictive ML Models Made the Difference

The customer is an industry-leading telecommunication technology provider with over 10,000 employees. They are committed to delivering cutting-edge telecom solutions while maintaining a high-performing, engaged workforce.

Challenge

The customer was experiencing rising attrition rates and declining employee satisfaction scores. Their HR team noticed uneven distribution of resources and opportunities across departments but lacked insights into the underlying patterns driving employee turnover.

They needed support to understand disparities in resource allocation, identify the critical factors, and assess and improve overall employee satisfaction across the organisation.

Solution

Our team helped the telco adopt a data-driven approach, leveraging advanced analytics and machine learning to understand workforce dynamics and address retention challenges.

• Delivered actionable insights for targeted interventions that improve employee satisfaction and reduce turnover risk.
• Applied clustering algorithms (K-Means and Hierarchical Clustering) to identify patterns in employee satisfaction and resource allocation.
• Predicted attrition risks through Logistic Regression, Random Forest, and XGBoost, and uncovered the key factors driving turnover through SHAP analysis.
• Analysed employee feedback through Sentiment Analysis to pinpoint pain points impacting morale and engagement.

Impact

• Provided essential HR insights for informed decision-making and proactive issue management.
• Enabled continuous monitoring and adaptation to sustain improvements in employee satisfaction and retention.
• Significantly improved employee morale through targeted, analytics-driven interventions.
• Created a data-informed HR culture that proactively addresses employee engagement and retention.