Guide
Manually sorting through the overwhelming volume of feedback is neither efficient nor pleasant. AI changes how we handle, analyze, and respond to user feedback. In particular, it comes handy for calculating sentiment score. Such analysis helps understand how the team is performing, how happy the customers are, and what can be improved.
In this guide, we'll explain how to configure AI to complete this task in Fibery automatically.
In the Product Team template, the Automations that rely on AI are described but disabled by default. Once you've set your OpenAI API key, you can enable them and tweak the prompts to your liking. To see an example of calculating sentiment score, go to Feedback Space and proceed to Automations in the Interview Database.
How it works
To automatically calculate sentiment scores for a conversation with a customer, use AI in Automations.
Here is the prompt for AI:
Sentiment scores are a metric for measuring customer sentiment. Scores can range from 0-100, where 100 is the most positive possible outcome and 0 is the least. Generate a sentiment score for the text below and return result as a number in a format [sentiment score] without brackets
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This works perfectly for live communication platforms, for example, if you have Intercom integration , Zendesk Integration or Discourse integration .
Example
Let's say we use Intercom for customer success, and we want to automatically calculate sentiment score when chat is closed.
So we set an Intercom Integration, and now we have a Conversation Database were all chats are stored.
Here is how Automation Rule will look for calculating sentiment score for Intercom conversations.
How to visualize sentiment scores
Sentiment score is useful for dynamically analyzing customer success over time.
You can do it using our powerful Reports. For example, here is what it looks like for Fibery team:
And let's see how it changes within time for a single teammate:
See also