Analyzing Chatbot Performance with Kore.ai

📊 Analyzing performance of a chatbot using Chordata AI Thought Builder.

🔄 Monitoring frequency of usage, successful and failed utterances, and other metrics for enhancing user experience.

🔍 Examining chat history to understand user interactions and identify areas for improvement.

🔍 Review conversations and flow of chatbot interactions.

💡 Gain detailed insights into the bot's understanding of user statements.

✅👍 Successful scenarios where the bot accurately identifies user tasks.

❌👎 Scenarios where the bot fails to understand or identify user tasks.

✨ Using the training board, you can map incorrect inputs to desired outputs and quickly retrain the chatbot.

🔄 By analyzing fail scenarios, you can ensure that the chatbot understands and responds correctly to similar utterances in the future.

📊 The chatbot also captures performance metrics behind the scenes through scoring.

📊 Analyzing the performance of a chatbot involves measuring the confidence of matching different inputs.

📚 Documentation is available to help understand why certain inputs are not understood and how to make improvements.

🤖 Metrics and numbers can be used to monitor and evaluate the bot's understanding and make informed judgments.