AI: Conversation Sentiment Analysis
in progress
I
Imad Ahmed
Business problem:
Managers have no way to tell if a customer was happy, neutral, or frustrated in a conversation, or whether that mood improved or worsened. This applies to both AI Agent and human-handled conversations.
Because of this:
- Negative conversations often go unnoticed until a customer complains, leaves a bad review, or churns
- If an AI Agent or human agent handled a frustrated customer poorly, there's no way to catch it
- Managers have no structured way to know which conversations need attention, so they either check randomly or not at all
Desired outcome:
- Automatically detect the customer's sentiment (positive / neutral / negative) in a conversation, whether it's handled by AI or a human agent
- Understand how a customer's mood shifted over the course of a conversation through sentiment reasons
- Flag conversations with negative sentiment so managers know which ones need a closer look
- Show sentiment in reports so managers can spot patterns over time instead of one-off incidents
- Let managers set up actions in Workflows based on sentiment (e.g. notifications, follow-ups)
Use Case:
Right now, managers find out a conversation went badly only after the damage is done — a complaint, a bad review, or a lost customer. With sentiment detected automatically, they'd be able to identify which conversations were negative and take action — coaching an agent, fixing an AI Agent's behavior, or following up with the customer — instead of relying on manual spot-checks or waiting for something to go wrong externally.
N
Nabilah Binti Salleh
updated the status to
in progress
A
Alyaa See
Merged in a post:
Sentiment analysis
L
Luis Araujo
Companies are increasingly concerned about whether they are providing good service and good service through their digital channels. The way to know this is to measure sentiment in each conversation and generate a dashboard that allows us to understand the level you are at and analyze the cases identified as critical and thus improve care processes.
I see it as very important to have that functionality for enterprise plans at least.
N
Nabilah Binti Salleh
Merged in a post:
AI: Conversation tone assessment.
M
Max Egorov
Business problem:
At the moment it is really hard to assess client's mood during the conversation and assess it's change over time.
What we'd like to have is a way to measure the conversation tone using a scale, and identify client's conversation tone (From really happy to really frustrated).
On business side, we'll use it to:
- In the moment of conversation
-If client is frustrated, escalate the conversation to senior manager and increase SLA
-if client is happy, suggest him to leave positive review
- In time
Measure the mood drop/increase rate over time and identify positive/negative cases for further research and taking action.
Amalia Putrieka
Hi Imad Ahmed, thank you for suggesting this!
To better understand how we can meet your needs, could you please clarify:
- Could you provide examples of situations where sentiment analysis would be particularly helpful for you?
- What specific actions or notifications would you like to receive if a conversation turns negative?
This will help us understand your needs better. Hope to hear from you soon! :)