How to Measure Customer Satisfaction in a Call Center: Tools and Strategies

Smith, Emma

Publish: Wednesday, Jul 05

Key performance indicators (KPI) have long been an essential tool for businesses to measure and improve their inbound call performance. These KPIs include metrics such as call volume, missed calls, answered calls, and call duration.

However, in addition to these traditional KPIs, it’s also important to consider quality metrics that provide a more comprehensive view of the customer experience. Through surveys, we can measure call quality and service level. However, it must be taken into account that they have certain drawbacks, such as the low response rate compared to other methods and the difficulty of preparing the questionnaires.

For these reasons, Fonvirtual goes even further by integrating artificial intelligence into the analysis of satisfaction. In this article, we say a little more.

AI to measure customer satisfaction

Our tool uses artificial intelligence to identify customer and agent emotions during phone calls. Analyze the language used, the silences, the tone, any interruptions and determine the level of attention and satisfaction of each of the parties. Both liked and dissatisfied, using natural language processing techniques and the use of large language models. At the end of the call, it generates a summary of these measured metrics and provides a verdict that concludes the agent and customer attitude quality analysis.

This tool gives agents a better understanding of the customer’s emotional state in real time, allowing them to tailor their responses and solutions based on the customer’s emotional needs. For example, you will be able to identify when your attitude is less proactive and you can act accordingly by changing the tone or the heat of the conversation.

Benefits of Sentiment Analysis

AI integrates with your call center software which generates several advantages for companies. First, it allows them to gain important customer satisfaction data and analyze trends and patterns. This data is useful for identifying common issues, areas for improvement, and adjusting customer service strategies to better meet needs.

Additionally, the tool can help identify the most dissatisfied customers and proactively resolve their issues. Customer service supervisors can use the information provided by the tool to quickly identify dissatisfied customers on the call and find out the reason for their dissatisfaction. This allows them to solve their concerns before they become more serious problems and to assess the quality of the team’s care.

Other AI features in calls

Advances in artificial intelligence, particularly in natural language processing (NLP) and large language models (LLM), have enabled the development of speech recognition systems that automate the task of transcription with high accuracy. These systems not only recognize each word, but also analyze the context of the conversation and attempt to identify sentences that have meaning beyond their literal meaning. By automatically transcribing calls, it is possible to track customer satisfaction by putting what is said in writing. In addition, we can take advantage of real-time transcription in any language, which facilitates its analysis in multilingual contexts.

However, that’s not all. Thanks to Artificial Intelligence, you will be able to get the most out of your conversations, since you will be able to extract all their value. With Fonvirtual’s AI, you will be able to get the following information from your calls in real time:

  • Detailed summary of each call.
  • Automatic tagging of covered topics.
  • Detection of the language used in the conversation.
  • Identify the gender of the participants.
  • Sentiment analysis.

On the other hand, summaries generated in real time by artificial intelligence offer a clear and concise view of your customers. This allows you to identify opportunities for improvement, assess your team’s performance, and make informed decisions. In addition, the summaries also determine the attitude of the agent who answered the call and of the agent who answered the call, which makes it easier to objectively evaluate the performance of your customer service and to measure the satisfaction of your customers.


Integration of artificial intelligence in the CRM

The integration of artificial intelligence in the CRM, combined with a virtual telephone switchboard, offers significant advantages for companies. By having quick and easy access to centralized, analysis and monitoring of customer satisfaction are simplified. Agents can view detailed call summaries, topics covered, and relevant metrics without having to search through different systems. In addition, by linking information from artificial intelligence to existing data in the CRM, and taking advantage of the functionality of a virtual switchboard, a more complete picture of the customer is obtained, which facilitates the personalization of interactions and improves the customer experience.

Automation of tasks and processes is another key benefit of integration. Using speech recognition and natural language processing, the system can automatically tag topics, detect languages ​​and genres, and analyze sentiment. This saves agents time and effort, allowing them to focus on higher value tasks and improving operational efficiency.


Traditional key performance indicators (KPIs) have been useful for measuring and improving call performance, but it’s important to consider quality metrics to get a more complete picture of the customer experience. Fonvirtual uses artificial intelligence to measure customer satisfaction during calls, identify emotions and analyze language, tone and attention. This gives agents a better understanding of the emotional state of the customer and allows them to adapt their responses in real time. AI also enables automatic call transcription and provides detailed summaries, topic tagging, sentiment analysis, and effective CRM integration. These advancements improve the personalization of interactions, facilitate performance evaluation and informed decision-making, and automate tasks to improve operational efficiency.

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