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Support Conversation labels for analytics and global summary

Reports

What’s the need?

There might be all kinds of reasons customers contact with the support team.

What are the most often reasons?

Answers or hints to that question might show what can be improved in other areas to prevent customers need to contact support and have a smoother experience (although we do like talking to our customers :) ).

Possible Solutions

  1. A set of predefined labels are made for AI to choose from - which one seems to be most relevant (or multiple?) and attached to the chat.

  2. A short summarization of the chat

  3. Both 1 & 2 combined

This way there can be analytics of what kind of reasons users contact support, might be insightful.

i.e. Information Query, Bug report, Insufficient Documentation, Collaboration Request, Praising the Product, Bot, Extend Trial Request, etc

Reference

When using ChatGPT or similar solution - the chat will get a generic summarization of what was discussed.

Posted by Erik Kaljumäe ·
Status: Completed1 comment

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Comments1

  • Markus Palm changed status to Completed
    Team•

    Jul 27

    Pinned

    Hey! This is now supported with Fibi AI Attributes and the new Reports product!

    You can create attributes like Reason for contact, Product area, Urgency, or Sentiment, define the values Fibi should choose from, and enable ‘Let Fibi detect’. Fibi then automatically classifies every conversation it takes part in and saves the result as a conversation attribute:

    In custom Reports, you can use these AI-detected attributes to:

    • Break conversation volume down by reason, product area, urgency, or any other category you define

    • Track how each category changes over time

    • Drill into the actual conversations behind any result

    This gives you an automatic overview of why customers contact support without manually labeling every conversation.

    Set up Fibi AI Attributes →