This article is a contribution from Aifred Selwyn Rogacion, VP of Quality Analytics and Insights at Qualfon.
For years, many companies treated Quality Assurance (QA) like a post-game scorecard, something you check after the fact to see if you passed. That is no longer enough. Today, quality is about more than just compliance, especially as traditional (manual) QA typically reviews less than 5 percent of total customer interactions, leaving most experiences unseen. In part one of this post, Aifred Selwyn Rogacion, VP of Quality Analytics and Insights at Qualfon, shares his thoughts on what it takes to set up a modern quality analytics program.
Move From Reactive to Proactive
Traditional QA frameworks were created to ensure consistency, fairness, and adherence to standards at a time when interaction volumes and channels were more manageable. Manual evaluations have long played a critical role by applying human judgment, context, and empathy to customer conversations, enabling meaningful coaching and performance development.
As customer expectations and interaction volumes have grown, relying on manual reviews alone has become increasingly challenging. With only a portion of interactions reviewed, valuable CX insights can take longer to surface. Analytics helps optimize and scale the existing QA process by extending visibility across voice, chat, email, and social channels. By combining automated analysis with targeted human review, organizations gain a more complete understanding of performance. Trends surface earlier, and QA teams are empowered to focus their expertise on where it delivers the greatest impact, such as coaching, root cause identification, and continuous improvement.
"In a world where every interaction shapes your brand, measuring compliance isn't enough. You must measure impact."
Turn Data into Direction
We all understand every customer conversation contains value, but the challenge lies in uncovering it. Modern quality analytic capabilities capture more than procedural accuracy; they detect sentiment, emotion, effort, and loyalty signals hidden in every exchange. Using advanced analytics (i.e., descriptive, predictive, and prescriptive), businesses can understand not just what happened, but why, and what to do next.
This means identifying which interactions predict churn before it happens, learning which agent behaviors create loyal customers, and connecting the dots between service delivery and business performance. This means instead of a 95% QA score that hides customer frustration, analytics reveals whether those “perfect” interactions actually improved satisfaction, loyalty, or repeat-purchase intent.
"In practice, it means moving from quality scores to quality signals."
Enhance Human Insight with Automation and AI
As customer interactions multiply across voice, chat, email, and social media, the need for broader visibility and faster feedback grows. Manual QA continues to be vital for its depth of understanding and human judgment, but technology now allows us to expand that insight and accelerate how we act on it.
Automating a quality monitoring process amplifies what teams can do by evaluating a higher percentage of interactions—sometimes up to 100% interaction coverage. It’s important to note that this doesn’t replace the human element; it enhances it.
Using these automations, organizations gain faster visibility, more consistent analysis, and actionable CX insights in near real time. The combination of automation and human expertise creates a quality system that learns continuously, identifies trends early, and helps leaders make data-informed decisions faster than ever.
About the Author
Aifred Selwyn Rogacion is a senior executive with decades of experience spanning Operations, Quality, and Analytics, supporting organizations across nearly every major business vertical in complex, global environments. A Master’s degree holder and Lean Six Sigma practitioner, Aifred has led and developed high-performing teams through large-scale transformations focused on operational excellence, governance, and performance optimization.
Currently serving as Vice President for Quality Analytics and Insights at Qualfon, he sets enterprise strategy and builds scalable quality and analytics capabilities by aligning teams around a shared vision and disciplined execution. Known for simplifying complex operational processes through advanced analytics and structured problem-solving, Aifred has empowered teams to help organizations and enterprise clients improve efficiency, consistency, and customer outcomes while reducing cost and variability. He strongly believes that sustainable results come from investing in people—training teams, fostering ownership, and positioning quality as a strategic enabler rather than a compliance function.
Connect with Aifred on LinkedIn.
