From conversations to insight.
Understand how employees work – not just what they achieve
A new data foundation for leadership
By nature, phone-based customer service is a human interaction. It is about relationships, empathy, and problem-solving – not just processes and metrics.
Yet most contact centers have traditionally measured success through classic KPIs such as Average Handle Time (AHT), First Contact Resolution (FCR), or Customer Satisfaction Score (CSAT). These are valuable metrics — but they only tell part of the story.
With AI and conversation analysis, we now gain access to a new layer of insight – one that makes it possible to understand how employees work, not just what they achieve. By transcribing and analyzing large volumes of customer conversations, organizations can identify patterns in tone, language, conversation structure, pacing, and empathy. This opens the door to supporting leadership tasks – such as coaching – in a more informed, objective, and nuanced way than ever before.
From subjective observation to objective insight
Today, coaching in the contact center typically involves a team leader listening to a limited number of customer calls, documenting observations, and providing feedback to individual employees. This approach is time-consuming, subjective, and difficult to scale.
With conversation data and AI-driven analysis, leaders and supervisors can gain a more objective and consistent data foundation for coaching and performance evaluation. This may include insights into:
- What drives strong versus weaker customer experiences
- How often employees use active listening or open-ended questions
- How tone and engagement evolve throughout the conversation
- How clearly conversations are concluded – and how customers respond
- How often and in what ways employees take ownership during interactions
As a leader, these insights can be applied at the individual, team, and departmental levels. For individual employees, they provide a fact-based view of strengths and development areas. Across teams, leaders can identify patterns – for example, if multiple employees struggle with structuring conversations or creating calm in stressful situations. Leaders can also compare top performers with the average and identify which conversational elements characterize the strongest customer experiences.
In this way, data becomes not a control mechanism, but a foundation for learning, development, and quality improvement.
Quality, culture, and responsibility
AI and conversation analysis hold tremendous potential — but they also come with responsibility and invite reflection. How do we preserve core values such as openness, trust, autonomy with accountability, and psychological safety — values that define the “Scandinavian customer service culture”? These are values that require particular attention when implementing AI in an organization.
We believe conversation analysis should be viewed and used as a tool to strengthen professionalism, well-being, and quality — not as a means of control. When this principle is upheld and properly anchored among employees, organizations can foster a development-oriented culture where data is not only used to evaluate performance, but to drive learning, reflection, and continuous improvement.
