Context:

A large service-based business managing 1,000 customer support tickets per month.

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Problem Statement:

Support tickets lacked consistent categorisation, making it difficult to distinguish between technical and process-related issues, which impacted the accuracy of reporting and decision-making.

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Solution:

We developed a machine learning model to automatically segment and categorise support tickets, enhancing dataset quality and enabling clearer reporting on technical vs. process-driven problems.