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Sentiment Analysis

Use advanced machine learning and artificial intelligence to detect and visualize customer sentiment based on keywords and phrases used by customers and agents.

Visualization Saves Time

Since humans can process visual information 60,000 times faster than text, color-coding the customer sentiment in the call transcript saves a lot of time.

Quickly Identify The Problem

Was it a difficult customer? Did the agent handle it poorly? MiaRec lets you quickly identify the balance between the agent and customer sentiment.

Flag Calls For Follow-Up

Whether it is a customer issue that requires attention or the agent who needs guidance or training, you now have tools that let you flag calls for follow-up.

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Automatically Scoring Customer Sentiments

MiaRec Voice Analytics expresses customer sentiment in two different ways: through a numerical score with its associated emoji and through visualization of colors within the keyword list and transcript. 

The MiaRec speech engine analyzes identified keywords for positive or negative customer sentiment. Depending on the number of times a positive or negative keyword is mentioned either by the customer or the agent, a customer score, an agent score, and the customer sentiment score (the average of the two) is automatically tabulated and symbolized by the appropriate emoji. 

How It Works

Positive keywords, such as "thank you" or "this is helpful", are color-coded in green in the transcript as well as in the keyword list (or any other positive color you like), while negative keywords, like "upset", "angry", or "I expected more", are in shown in orange or red.

This allows you to see at a glance how a call is trending. If a customer is angry at first, does the agent resolve the problem quickly or does it escalate? With MiaRec's visual Customer Sentiment analysis, you can immediately see it without reading the entire transcript, saving you valuable time. 

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Track Sentiment Over Time

Track sentiment analysis scores and trends overall, by business unit, location or agent and compare against historical data.

Learn More About Sentiment Analysis and more

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