Voicenter's emotion and impression analysis engine gives managers back control by objectively quantifying the emotional state for all statements made in 100% of calls. The process begins at the individual call level, where the system deciphers a rich mosaic of over 100 types of emotions and conversation traits, sorted into positive and negative categories, far beyond just 'anger' or 'happiness'.
The system summarizes all these emotions into an overall sentiment score and produces a visual timeline that allows identification of precise emotional turning points. But the true power is revealed when this deep analysis is applied across thousands of calls.
The system automatically builds an objective "soft skills" profile for each representative, while simultaneously identifying chronically frustrated customers. At the strategic level, all data converges into a management overview for the entire call center, showing trends and averages. In this way, the system provides a multi-dimensional picture, from micro to macro, of the organization's emotional pulse.
The algorithm assigns quantitative scores to each segment of the conversation for sentiment level and emotions. This data is displayed on the conversation timeline visually, allowing the manager to see exactly where emotional turning points occurred.
The analysis eliminates human biases and ensures consistent evaluation of all conversations, regardless of fatigue or mood of the human listener. The data is available via API for setting up automatic alerts, for example, sending an alert to a manager if the frustration score in a conversation crosses a certain threshold.
The true power of the Voicenter AI platform lies in the tight integration between its components, creating a cumulative effect of value, similar to a flywheel.
The system identifies in real-time a conversation where the customer's "frustration" score is climbing dangerously. A manager receives an automatic alert. Instead of listening to the entire conversation, they look at the visual timeline and immediately identify the breaking point: at 02, after the representative said "There's no such option in the system," the customer's frustration score jumped from 3 to 8. With this precise information, the manager can intervene immediately, save the customer, and give the representative specific and clear feedback right after the call ends.
Instead of general feedback like "You should have been more patient," the manager sits with the representative for a monthly coaching session and presents their soft skills profile. "Roy, I see that this month your average 'empathy' score is 8.5, which is excellent and above average. However, your 'proactivity' score is 5.2. Let's listen together to two or three conversations where you could have offered the customer an additional solution, and think about how to improve this next month." This is precise, respectful, and measurable coaching that leads to real performance improvement.
The manager looks at the management dashboard and sees a worrying trend: the average "self-confidence" score of all service representatives drops by 30% whenever calls about the newly launched product come in. The problem isn't a specific representative, but a widespread knowledge gap. Following this insight, the manager organizes focused training on the new product for the entire team. Thus, a strategic management decision is made based on emotional data, not just on "gut feeling" alone.
