Datamark Case Study “From Emotion to Action: The Role of Sentiment AI in Contact Centers”
Valence was recently featured in a case study with Datamark, discussing the ROI of emotion AI in a BPO setting.
Datamark’s recent case study shows how AI-driven sentiment analysis is transforming contact center performance—turning each customer interaction into a chance to build loyalty. Traditionally, contact centers measured success by resolution times and efficiency metrics, but these metrics often overlooked the emotional component that shapes customer perceptions.
Sentiment analysis has been part of the contact center toolkit for years, but Datamark—partnering with Valence—is taking it beyond basic “positive/neutral/negative” scoring. By combining behavioral analytics with real-time AI transcription tools like DataScribe, the approach captures tone, cadence, phrasing, and conversational flow to understand why a customer feels a certain way and help agents guide conversations toward better outcomes.
Unlike sentiment tools that rely on emojis or color codes, Datamark’s approach turns emotional cues into actionable insights for both agents and supervisors. This depth of analysis also addresses cultural and linguistic nuance—recognizing that subtle differences in phrasing or tone in languages like English or Spanish can shift meaning depending on regional background.
As Jacob Bailon, Datamark’s Director of Engineering, explains:
“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.”
“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.”
This proactive approach boosts agent confidence and helps close perception gaps in offshore support—showing that empathy and understanding can transcend geography.
The technology also prevents costly misreads. For example, poor sentiment analysis in one restaurant drive-thru AI system escalated 50% of calls to human agents—not because customers were angry, but because the AI mistook enthusiasm or casual profanity for aggression. That’s why capturing emotional context is critical to avoiding unnecessary escalations, customer frustration, and lost revenue.
The bottom line: customers want to feel heard—not only through their words, but in the tone and context behind them. For Datamark, sentiment analysis is not about replacing agents, but empowering them with AI that enhances emotional intelligence, cultural awareness, and real-time responsiveness.
