Featuring insights from Chloe Duckworth, CEO of Valence, and Jacob Bailon, Director of Engineering at DATAMARK
How Sentiment Analysis and Real-Time AI Are Reshaping Agent Experience in the Contact Center
Sentiment analysis isn’t a new concept in the contact center space. While traditional sentiment tools often focus on positive, neutral, and negative scoring, DATAMARK prides itself on taking a deeper approach.
Working alongside partners like Valence, we’ve shifted toward behavioral analytics, tracking tone, emotion, and conversational flow to understand why a customer feels a certain way and how to better design the customer journey from end to end.
The Role of AI-Driven Sentiment Analysis
AI transcription tools like DataScribe have been changing the game for quite some time. With real-time transcription, issue and resolution summarization, and tone detection, agents receive real-time support. But it’s the sentiment layer that elevates these tools to the next level.
As Chloe Duckworth, CEO of Valence, explains:
“AI is rapidly redefining industries, outpacing any singular software advancement in its history. There are a lot of companies working on the alignment problem with AI, but Valence’s goal is to seek emotional alignment between AI and humans. In BPOs, we see the real human impacts of miscommunications and emotional disconnects every day. Valence exists to use AI to improve agents’ customer understanding and confidence during a call, while proving business value to the organization in improving their revenue potential as an AI-first company.”
While many sentiment tools lean on emojis or color-coded scores, our approach digs deeper, translating emotional cues into actionable insights for our agents and supervisors.
Going Beyond Language: Real-Time Cultural Awareness
An overlooked strength of sentiment analysis is that it helps agents navigate cultural and linguistic barriers. As Jacob Bailon, Director of Engineering at DATAMARK, puts it:
“It’s not just about analyzing emotional quotient. It’s the ability to take tone, cadence, phrasing, and context and produce a measure that’s more meaningful to me as an agent.”
This is crucial when supporting customers and agents globally. Subtle inflections in English or Spanish, for instance, can take on different meanings depending on the speaker’s background.
That’s where the combination of DataScribe and Valence AI adds real value. Together, they interpret tone and behavioral cues in the moment, allowing agents to de-escalate early and respond appropriately, whether a customer is frustrated or just emotionally charged.
Empowering Agents, Proactively
With growing pressure to balance customer satisfaction and operational efficiency, sentiment tools like DataScribe are playing a more strategic role in frontline performance.
A leading retail client partnering with DATAMARK reported the following improvements:
But one of the most significant unrecognized benefits? Greater agent confidence.
Sentiment analysis helps supervisors and agents identify rising frustration early, enabling coaching and escalation mid-conversation.
Jacob puts it this way:
“One of the most important benefits of sentiment analysis is that it gives supervisors and quality assurance teams the ability to proactively manage performance and escalation risk before it becomes an issue. That’s something we didn’t have five years ago. Now we can intervene in real time, not retroactively.”
Emotional Context = Strategic Insight
When AI tools don’t account for tone or context, automation can easily miss the mark. Chloe Duckworth shared an example from one of Valence’s restaurant and hospitality clients where AI-powered drive-thru systems escalated half of all calls to human agents, not because customers were angry, but because the software misread enthusiastic or casual profanity as aggression.
“One drive-thru had a 50% escalation rate to human agents, and they ended up losing more money by implementing AI agents, just because it was such an escalation process from their poor sentiment analysis.”
It’s a powerful reminder: how something is said can matter just as much as what’s said. When emotional nuance is missed, it creates friction for customers and costs for businesses.
Closing the Cultural and Perception Gap
When regional accents or language variations are perceived, tensions can rise before the conversation begins. Tone, pacing, and phrasing can often be misinterpreted.
That’s where behavioral sentiment analysis steps in. It helps agents pick up on emotional signals as they happen, builds trust during difficult conversations, and gives agents the confidence to respond thoughtfully, not reactively. Over time, this helps shift the narrative around offshore support by proving that empathy and understanding transcend geography.
Ready to Apply AI Sentiment Analysis in Your Contact Center?
Customers want to be heard. They want to feel understood, not just in the words they use but in the tone behind them. They want to know the person on the other end of the call gets it and that they’re being listened to in a way that leads to meaningful resolution.
That’s why we’ve made sentiment analysis a core part of our AI strategy at DATAMARK. Not to replace agents, but to empower and augment them to the next level.
If you’re rethinking how your contact center supports its agents and connects with customers, we’d love to help. Book a complimentary consultation with our team and explore what’s possible when AI and emotional intelligence come together.
FAQs About AI Sentiment Analysis in Contact Centers
Rule-based sentiment analysis uses predefined keywords to classify sentiment, while machine learning models learn from data to identify positive or negative sentiment more accurately. Aspect-based sentiment analysis examines specific elements like product features rather than overall sentiment alone. Fine-grained sentiment analysis provides detailed emotional gradations beyond simple positive, neutral, or negative classifications. Modern AI-powered sentiment analysis combines these approaches with natural language processing for real-time accuracy.
Sentiment analysis models process textual data using natural language processing to identify sentiment expressed within interactions. AI algorithms analyze word choice, phrasing, context, and patterns to assign sentiment labels and generate scores. Traditional sentiment analysis counts positive or negative keywords, while ML sentiment analysis considers tone and contextual meaning. The total sentiment score reflects overall sentiment detected, weighted by emotional strength for accurate classification and interpretation.
Organizations use sentiment analysis to monitor social media platforms for brand perception and public sentiment trends. Product teams analyze sentiment in customer reviews to identify improvement opportunities. Marketing departments perform sentiment analysis on campaigns to understand customer sentiment and adjust messaging. Support teams prioritize urgent issues based on negative sentiment detection. Opinion mining from online reviews helps businesses understand sentiment over time and track responses to product or service changes.
Sarcasm, cultural nuances, and regional expressions often confuse sentiment classification models, leading to misidentified sentiment. Context-dependent language, where identical phrases carry different meanings, creates accuracy issues. Accurate sentiment analysis requires training on diverse datasets representing your specific customer base and terminology. Real-time analysis adds complexity as systems must classify sentiment instantly. Emotion analysis requires sophisticated AI models to distinguish frustration from anger or enthusiasm from aggression effectively.
Track operational improvements like reduced escalation rates, faster resolution, and decreased churn from better sentiment understanding. Monitor agent performance, including confidence levels, handling efficiency, and quality scores, improving when leveraging AI tools. Measure customer experience gains through satisfaction scores and positive sentiment increases. Calculate cost savings from proactive intervention, preventing negative situations before escalation. Insights from sentiment analysis should translate to measurable business outcomes demonstrating AI value in operations.
