Call Analysis: An Objective Measure of Emotions, From Individual Calls to the Complete Picture

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Emotion Analysis and Speaker Characteristics

Losing your "Gut Feeling" When Business Grows

When a business is small, the manager personally knows the customers, listens to most calls, and has a good feel for the organization's pulse. They know when a customer is satisfied or frustrated and when a representative needs guidance or a quick refresher. But when the business grows, reality changes. There's no ability to know the emotions of all representatives and customers.

Management requires attention to countless details, and listening to all calls becomes an impossible task. Managers reach a situation where they listen, at best, to only about 2% of calls, creating a sense of helplessness. The "how" and "why" of the conversation are lost, and the ability to manage based on deep emotional understanding disappears.

Losing your "Gut Feeling" When Business Grows

Is This Exactly the AI You've Been Looking For?

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 by positive and negative, 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.

How It Works:

Quantitative Scores and Visual Timeline:

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.

Objectivity and Consistency:

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.

Is This Exactly the AI You've Been Looking For?

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