Missed chats in the Zendesk CSV export are identified using the 'is_missed' metric.
This metric shows 'true' for chats that were missed and 'false' for those that were accepted. By analyzing this data, you can identify patterns in missed chats and take steps to improve response rates and customer satisfaction.
The Zendesk chat details CSV export includes a comprehensive set of 45 columns, each providing specific information about chat sessions. This data encompasses unique identifiers, timestamps, session details, visitor information, and chat metrics….
The chat duration in Zendesk's CSV export is calculated in seconds, representing the time between the first and last message sent during a chat session. This metric, labeled asduration (seconds)
, accounts for all messages exchanged, regardless…
The 'started_by' metric in the chat CSV export indicates who initiated the chat session. This can be an agent, a visitor, a trigger, or none (for offline messages). Understanding who started the chat can help in analyzing chat initiation patterns…
The 'is_triggered' metric in Zendesk's chat CSV indicates whether a chat was initiated by a trigger. It shows 'true' if a trigger ran during the session and 'false' if no trigger was involved. This metric helps in assessing the effectiveness of…
The 'rating_score' metric in the chat CSV export represents the satisfaction rating given to a chat session. Ratings can be 'good', 'bad', or 'none' (for chats without ratings). This metric provides insights into customer satisfaction and can be…
The 'firstresponsetime' in Zendesk's chat CSV is calculated in seconds, measuring the time taken by an agent to pick up the chat and send the initial message. This metric is crucial for evaluating the responsiveness of agents and ensuring timely…
The 'visitor_notes' metric in the chat CSV provides additional information about the customer, which can only be added by agents. These notes can include important details that might help in understanding the customer's needs or preferences,…