Customer Chat
Last updated
Last updated
this feature allows users to track, analyze, and evaluate the performance of chat interactions between customers and AI agents. This feature provides insights into how effectively the AI agents are engaging with customers and meeting performance goals. It is particularly valuable for businesses and organizations that utilize AI-driven chatbots or agents to interact with customers and users.Key functionality of the Monitoring Performance Chat feature include:
Performance Metrics: The feature provides a set of performance metrics that quantify the effectiveness of chat interactions. These metrics may include the total number of chats, chat distribution across agents and more.
Visualization Representation: The feature presents performance metrics in visual formats such as scorecards, pie charts, graphs, and tables. These visualizations make it easier for users to quickly understand trends and patterns in chat performance.
Agent and Deployment Analysis: Users can analyze the performance of individual AI agents or specific deployments. This analysis helps identify which agents or deployments are generating the most engagement and providing high-quality interactions.
Customer Chat History: this feature is the provision of comprehensive customer chat history. It encapsulates the entire journey of interactions between customers and AI agents.
Customer Identity and Profile: ability to identify and delve into customer identities and profiles.offering insights into their historical interactions, and other attributes that shape their engagement with the AI agents.overall customer experience.
Discover more,see customer chat data visualization in our apps
Once logged in, you can navigate to the performance section as follows:
Choose Monitoring Tab,direct your attention to the navigation options. Look for the "Monitoring" tab among the available choices.
Locate the sidebar on the left-hand side of the screen.
Click on the "customer chat" option to access the monitoring performance chat feature.
Scorecards
Scorecards provide a numerical representation of key chat performance metrics, such as total chat counts.Scorecard (Total Chat Count): A numerical indicator showcasing the total number of chats.
Pie Charts and Graphs
Interactive pie charts and graphs visually represent chat distribution across agents and deployments. Click on segments to access more detailed information.
Hollow Pie Chart (Chat Count by Agent): A pie chart with hollow sections displaying chat distribution across agents.
Hollow Pie Chart (Chat Count by Deployment): Similar to the previous chart, this displays chat distribution by deployment.
Tables:
Top 5 Agents by Chat: A table listing the top 5 agents based on chat volume.
Top 5 Agents by Customer: A table showcasing the top 5 agents ranked by the number of unique customers.
Top 5 Deployment Agents by Chat: This table highlights the top 5 agents for specific deployments based on chat count.
Top 5 Deployments by Customer: A table listing the top 5 deployments with the highest customer engagement.
Viewing Customer Interactions
Access a table displaying customer interactions, including identity, related agent, deployment, last chat, and total records.
Display Table:
Identity: The identities of individuals involved in the chat interaction.
Related Agent: Displays the agent associated with each chat.
Deployment: Indicates the deployment in which the chat occurred.
Last Chat: Shows the last user conversation
Total Record: Presents the overall count of chat records meeting the current criteria.
Applying Filters and Search
Refine table content by applying filters based on customer identity, agent, and app deployment. Use the search functionality to retrieve specific records.
Pagination Controls
Navigate through multiple pages of chat records using pagination controls. Adjust the number of records displayed per page using the dropdown option.
Chat History
Explore the customer's chat history organized into sessions. Each session represents a continuous conversation interaction.
Upon clicking the identity, the system opens the "Chat Detail" view for that customer.
The view is divided into two main sections: "Profile" and "Chat History."
Profile Section
displaying the customer's identity and relevant attribute profiles.
Any available attributes associated with the customer are listed, providing contextual information about the customer.
Chat History Section
The user scrolls down to the "Chat History" section to access the conversation history between the customer and the AI agent.
Navigating Messages
you can scroll through the messages within a session to review the conversation's progression.
If you have any further questions or need assistance, feel free to reach out to our support team.