Leveraging Aggregated Data for Strategic Insights
Over time, aggregated sentiment analysis data can be utilized to identify trends and patterns in customer emotions related to specific products, services, or processes. By analyzing how customer emotions evolve, businesses can proactively address issues and enhance their offerings to better meet customer needs.
This comprehensive understanding of customer emotions enables companies to make data-driven decisions, optimize their approaches, and ultimately build stronger, more resilient customer relationships.
Decode & Evaluate Customer Sentiments
Each conversation undergoes sentiment analysis, then produces a comprehensive overview of its emotional tone. This includes a total numerical score ranging from -100 to +100, along with separate scores for both the agent and the customer.
The analysis provides a detailed explanation and reasoning to justify each score. This also includes highlighting specific phrases within the conversation to help visualize what contributed to the overall sentiment assessment.
Identify & Address Key Issues
Whether you utilize the sentiment heatmap or filter aggregate data by sentiment labels or score ranges, these tools enable companies to spotlight calls with potential issues for immediate review. By prioritizing conversations that reflect recurring problems, you can swiftly address and resolve the most pressing concerns. Additionally, this functionality helps managers develop targeted training plans by highlighting common themes and issues, ensuring a more focused approach to improving customer experience.
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Sentiment Analysis FAQs
No. Sentiment scoring is based on the contents of the transcription. We have found that sentiment scoring based on text is more accurate than tone of voice because it captures the specific words and phrases used, providing clear context and meaning. Whereas tone of voice can be ambiguous and influenced by various factors, leading to potential misinterpretations.
Yes. Alerts can be automatically sent out to specific email addresses based on certain criteria. For example: Send an alert to john.smith@acme.com if a call has a sentiment label of “Very Negative.”
MiaRec’s Generative AI-powered Sentiment Analysis scores calls based on the entire context of a conversation. For example, if 99% of a call is negative but the agent resolves the customer's issue at the end, the call would be scored as "Positive."
Please note that sentiment scoring may vary depending on how the sentiment analysis prompt is customized.
- You can customize the Sentiment Analysis prompt in MiaRec by going to the Administration Tab > AI Assistant > Prompt Library > Sentiment Score
- Scroll down to “System Instructions”
- Edit the instructions (prompt) accordingly
