Case Study: a US-based networking solutions provider improves customer sentiment accuracy with Persistent Systems

US-based networking solutions provider improves customer experience with a self-learning sentiment analyzer

The US-based networking solutions provider was facing challenges with its degraded in-house machine learning model for analyzing customer sentiment. The outdated model, with only 55% accuracy, could not provide actionable insights from new feedback surveys or map to updated processes. To address this, the company engaged Persistent Systems to upgrade its analyzer for better accuracy and root cause analysis.

Persistent Systems re-engineered the model using Google Cloud Platform's AI solutions, including Vertex AI, to enable real-time data ingestion and introduce self-learning capabilities. This improved the sentiment analyzer's prediction accuracy by 10%. The upgraded solution now provides root cause analysis, helps prioritize support tickets, monitors engineer performance, and has led to improved CSAT scores for the client.