Optimizing Call Operations: How Machine Learning Slashed Handling Times and Costs

Challenge:

A leading Managed Service Operator (MSO) faced operational inefficiencies in their call centers, characterized by prolonged call handling times. The goal was to streamline operations and reduce costs by optimizing the customer issue resolution process.

Solution:

Our tailored Machine Learning (ML) Optimization Service was deployed to transform the call center’s operations. The service consisted of the following steps:

• Feature Engineering: We analyzed and computed Interactive Troubleshooting Guide to extract and process key and / or correlated data points.
• Model Development: Our team of ML experts developed and trained sophisticated models capable of predicting issues and solutions with a high degree of accuracy.
• Environment Build-Out: Utilizing technologies such as TensorFlow, Docker, we constructed a self-driven, automated ML engine designed for scalability and efficiency.
• ML Engine Integration: The final step involved integrating the custom-built ML engine into the MSO’s existing systems to support real-time analytics and decision-making.

Results & Benefits:

• Efficiency Boost: ML system cut call handling time by an average of 5 minutes.
• Precise Predictions: ML models achieved 99.9% accuracy for common call types.
• Cost-Efficiency: Anticipated savings of millions annually demonstrate the financial impact of our ML Optimization Service.