Large Australian bank identifies the root cause of customer DSAT and enhances self-service experience using GenAI on AWS
Business challenge
A large Australian bank found itself overwhelmed by the volume of customer interactions handled by their contact centers every month. Thousands of calls and chats were happening, but the bank lacked the right tools to effectively analyze this data and extract valuable insights.
Here’s what hampered their efforts:
- Inaccurate transcription: The bank’s existing contact center software struggled with accurate speech-to-text conversion, leading to a distorted picture of customer conversations.
- Limited NLP capabilities: Their natural language processing (NLP) technology relied heavily on topic modeling, a basic approach that couldn’t identify deeper nuances within customer interactions.
- Manual tagging bottleneck: The process of tagging customer issues relied on manual work by agents, which was time-consuming, prone to errors, and limited the effectiveness of data analysis.
Implementation approach
Fractal leveraged Customer Interaction Insights (CII), a GenAI-powered solution, to analyze voice calls and agent chats and draw actionable insights such as the root cause of customer dissatisfaction, sentiment, and other patterns.
Using CII within the bank’s AWS ecosystem, we harnessed the power of Amazon EKS, Amazon Bedrock, and Amazon OpenSearch Service to distill actionable insights from voice calls and agent chats. With features like auto-transcription using STT and unsupervised intent and entity extraction, CII unveiled hidden patterns and pain points, empowering the bank to make data-driven decisions for improvement.
CII offered a range of features designed to transform the bank’s approach to customer data:
- Auto-transcription using STT: CII’s proprietary speech-to-text (STT) feature delivered higher accuracy, providing a clear and reliable foundation for further analysis.
- Unsupervised intent and entity extraction: Unlike traditional NLP, CII’s unsupervised approach could automatically identify customer intent and key entities within conversations, uncovering hidden patterns and pain points.
- Unified dashboard for actionable insights: A central dashboard brought together all the extracted information, presenting emerging trends and actionable insights at a glance. This empowered the bank to make data-driven decisions for customer experience improvement.
Impact delivered
Achieved 85% accuracy on call-driver identification
Built a feedback loop to train agents
Improved STT accuracy from 60% to 85%
Customer Interaction Insights solution is now available on the AWS Marketplace; click here to learn more.