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CX Insights and Visibility
with AI Sentiment Analysis

Get actionable customer sentiment and unlock the voice
of the customer. Automate tagging, intents, and
perform sentiment analysis for every support ticket.
Get actionable customer sentiment
and unlock the voice of the
customer. Automate tagging, intents,
and sentiment for every support ticket.

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Emotion and sentiment analysis
of every interaction

Our AI Sentiment Analysis solution provides instant visibility into the emotional tone of customer interactions. Leveraging advanced natural language processing algorithms and machine learning, our solution can identify positive or negative sentiment as they unfold, allowing your support team to respond promptly to customer needs.
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Automated ticket tagging and prioritization

Highly accurate product tags and intents are applied to every ticket automatically. Get rich reporting and analytics on top problem areas and reasons of contact. Drill down reports by sentiment, CSAT, account, revenue, and many more dimensions.
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Customizable Alerts and Proactive Notifications

Our AI Sentiment Analysis solution provides customizable alerts and notifications for critical sentiment shifts or escalating issues. With our sentiment analysis tool, you can tailor notifications to suit your business priorities, ensuring you never miss an opportunity to turn a negative experience into a positive one. In-built customization of alert thresholds based on the severity of sentiments in support tickets.
Transform your customer
support operations
45% FASTER
response times
60% REDUCTION
in escalations
35% TICKETS
responded with AI

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AI sentiment analysis tools enhance customer support by enabling businesses to analyze large volumes of customer interactions across social medial platforms, such as emails, chat transcripts, and social media posts, in real-time. These tools automatically detect the sentiment expressed in these interactions, whether positive, negative, or neutral, allowing support teams to prioritize and address customer issues more effectively. By identifying negative sentiments early, businesses can proactively intervene to resolve customer concerns, prevent escalations, and improve overall customer satisfaction with sentiment analysis system. Additionally, sentiment analysis provides valuable insights into customer preferences, pain points, and trends across social media platforms, enabling businesses to tailor their support strategies and product offerings accordingly, leading to enhanced customer experiences and loyalty.
Reducing reliance on CSAT (Customer Satisfaction) surveys in favor of sentiment scores is important because sentiment scores offer real-time insights into customer satisfaction levels without relying on post-interaction surveys. While CSAT surveys provide valuable customer feedback, they are often limited by low response rates, bias, and delayed feedback. In contrast, sentiment scores derived from AI analysis offer immediate feedback by analyzing customer interactions, such as emails, chats, and social media posts, allowing businesses to promptly address issues and improve customer experiences. By leveraging sentiment scores, businesses can gain a more comprehensive understanding of customer sentiment across various touchpoints, enabling them to proactively enhance satisfaction levels and drive long-term loyalty.
Sentiment analysis algorithms predict escalations in support tickets by data mining and analyzing the language and tone used in customer interactions to identify indicators of dissatisfaction or frustration. These sentiment analysis model algorithms can detect patterns such as negative sentiment, urgency, or escalation cues within the text of support tickets. By continuously monitoring and analyzing these cues, the algorithms can accurately predict which support tickets are more likely to escalate into significant issues or complaints. This allows support teams to prioritize and intervene in critical cases promptly, preventing potential escalations and resolving customer concerns proactively.
Generative AI sentiment analysis involves employing artificial intelligence algorithms to analyze text data and generate expressive content that reflects sentiments, emotions, or opinions conveyed within the input text. Unlike traditional sentiment analysis methods that classify text into predefined categories (positive, negative, neutral), generative AI goes a step further by creating new text that captures the nuances and complexities of human emotions. This approach allows for a deeper understanding of the underlying types of sentiment analysis and enables more nuanced responses or insights.
Yes, the solution seamlessly integrates with existing customer support systems and ticketing platforms, enhancing text analytics based sentiment analysis capabilities without disrupting established workflows. It can power sentiment analysis within Zendesk, Salesforce, Intercom, Freshworks, and many others.
Works with tools
you already use
Works with tools
you already use

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