Basic Analytics
Want to know more about your AI Chatbot performance?
LiveChatAI's basic Analytics is designed to provide you with a comprehensive view of your chat performance, both for AI and human agents. By understanding these metrics, you can make informed decisions to enhance your customer support and optimize your chatbot’s efficiency.
Here’s a detailed breakdown of each metric to help you get the most out of your analytics.
AI Agent Analytics
Total Conversation Count
This metric shows the total number of conversations handled by the AI agent. It helps you gauge overall chat activity and AI engagement.
Open Conversations
Tracks the number of conversations that are currently active and have not yet been closed. This metric helps in understanding ongoing chat volume and workload.
Closed Conversations
Refers to the number of interactions that have been concluded by the AI agent. Conversations can be closed in two ways:
- Human Review: Human agents may review and manually close chats that were handled by the AI. This ensures that any follow-up actions or additional support needed are addressed before the conversation is marked as closed.
- Automatic Closure: If a conversation remains open for 24 hours and is primarily marked with 'AI-helped' feedback, the AI agent will automatically close it. This automatic closure helps to manage chat volume and ensure that interactions are resolved efficiently.
By monitoring closed conversations, you can understand how effectively the AI and human agents are working together to resolve and finalize interactions.
Total Resolutions
Measures the total number of resolutions provided by the AI, including chats that were closed automatically after 24 hours. This metric includes the chats marked as 'AI helped,' showing the effectiveness of the AI in resolving issues.
Human Support Switch Rate Per Conversation
Calculates the frequency at which users request human support during their interactions with the AI.
It tracks both manual clicks on the human support button within chats and automatic triggers based on the Live Chat AI Trigger if it is enabled under the Human Support Live Chat Settings. This metric helps in evaluating how often the AI needs to escalate issues to human agents.
Total Resolution Rate
Displays the percentage of conversations resolved by the AI relative to the total number of conversations. This metric is crucial for assessing the overall effectiveness and efficiency of AI in resolving issues.
Important Note
The downward/upward icon in the image is likely indicating a decline/increase in performance or activity compared to the previous period.
In this case, if you select the "last 7 days" as the time range, this 100% decrease in the "Total Conversations" would be calculated by comparing the number of conversations during the selected 7-day period to the previous 7-day period.
Here’s how the calculation would work:
- Selected Period (last 7 days): Total conversations = 0
- Previous Period (7 days before the selected period): Let's assume there were 10 conversations.
- The percentage change is calculated as: Percentage Change = ((Previous Period - Selected Period) / Previous Period) x 100
- In this example: ((10-0) / 10) x 100
This results in the 100% decrease displayed, indicating that the number of conversations has dropped to zero in the selected period compared to the previous one.
This pattern could apply to any time range, with the system dynamically comparing the selected range against the previous range to indicate any increase or decrease in performance.
Human Agent Analytics
Total Conversations
Counts all messages handled by human agents. This metric helps in understanding the volume of work handled by the human support team.
Closed Chats
Tracks the number of conversations that human agents have ended. This shows the volume of chats that were completed or closed by human intervention.
Response Time
Measures the average time between the creation of a chat and the human agent’s first response. This metric is essential for evaluating the responsiveness and efficiency of your human support team.
Messages Per Chat Count
Provides the average number of messages sent by human agents in each chat. This metric is useful for analyzing the depth and complexity of interactions handled by human agents.