Yellow.ai has introduced Analyze to enhance bot interactions by providing comprehensive conversational insights and advanced self-learning features. Built on Yellow.ai’s proprietary LLM model, Analyze reduces ticket volumes by 30% and increases containment rates by 10%, providing businesses with a significant boost in customer service efficiency.
“Customer interactions and contact center data hold immense potential to elevate customer experience, yet many businesses are missing out due to outdated technology. With the launch of Analyze, we aim to meet this market need and help enterprises close gaps in their customer service strategies. Analyze provides comprehensive metrics that enhance containment opportunities and drive more effective automation,” said Raghu Ravinutala, CEO & Co-Founder of Yellow.ai.
Traditional automation systems often fall short by offering only surface-level insights, such as user counts and session durations, leaving companies with a limited view of chatbot performance. A recent survey by Yellow.ai reveals that 54.5% of customer service professionals are eager to enhance their data analysis capabilities through AI, seeking to understand bot effectiveness, user satisfaction, conversation themes, and areas for improvement.
The new solution not only provides in-depth insights but also leverages this data to continually refine the bot’s ability to handle more complex customer queries autonomously.
Self-learning loopback technology
Analyze boosts bot automation by feeding transcripts from escalated queries back into the system, creating knowledge base articles that allow the bot to better deal with similar queries in the future.
Strategic topic clustering
This feature enables customer service teams to explore AI-generated topic clusters from bot interactions, offering insights into customer sentiments, potential improvements to knowledge base articles, conversation share, and containment rate opportunities.
Advanced conversation analysis
Analyze delivers detailed reports on customer conversations, allowing teams to assess resolution quality, containment opportunities, and other metrics at a granular level, enhancing overall customer support.
Sentiment analysis
By using deep learning, Analyze categorizes conversations into positive, negative, or neutral, providing deeper insights into the quality of resolutions. When applied to topic clusters, this sentiment analysis delivers more reliable data than traditional self-reported feedback.
