Evaluating the Quality of Calls in a Telecommunications Call Center

Telecommunications is an industry where customer service is paramount. In this sector, call centers play a vital role in interacting with customers.

But how can you ensure that calls to these centers are of the highest quality? Let’s explore how to evaluate the quality of calls in a telecommunications call center.

What Does Quality Mean in a Call Center?

Quality in a call center is not limited to the signal of the call, but encompasses aspects such as response time, the agent’s ability to solve problems, their courtesy, and the overall customer satisfaction. In addition to metrics like FCR (First Call Resolution) and AHT (Average Handle Time), it’s important to consider indicators relevant to the type of product or service being offered.

Defining quality in a call center is not an easy task, as it involves several key elements. At Upbe, we believe that quality in a call center refers to the ability to have efficient and productive processes that generate positive impacts on customer satisfaction. This implies minimizing waiting times, tailoring messages and actions according to customer needs, and having well-trained agents who are involved and committed to providing the best attention. In short, quality focuses on providing a satisfactory and personalized experience for customers.

Methods for Evaluating Call Quality

The call center agent is the core of any call center. Since they are the most involved in customer interactions, their performance directly affects the customer experience. Given this importance, it is crucial to track their productivity.

There are several tools and techniques to evaluate agent performance and, consequently, the quality of calls in a call center, including:

Call Recording and Analysis

It is very common for call centers to record and analyze calls to evaluate agent performance and customer satisfaction. In advanced call analysis, artificial intelligence and machine learning are applied to clean up and enhance audio recording, then it is transcribed using a transcription engine, turning it into unstructured text, and finally information is extracted and meaning is derived using various technologies.

Customer Satisfaction Surveys

Surveys provide direct feedback from customers about their experience and can offer valuable insight into call quality. In customer satisfaction surveys, it is essential to consider the type of survey according to the medium used, such as face-to-face, telephone, or online surveys. It is also important to distinguish between open-ended and closed-ended questions. Some common question examples for inspiration include rating the service received, suggestions to improve the experience, level of satisfaction, likelihood to recommend the service, improvements in the process, and product features.

Key Performance Indicators (KPIs)

When evaluating agent performance in a call center, it is crucial to select the right key performance indicators (KPIs). KPIs provide quantitative metrics that help measure service efficiency and quality. Some essential KPIs include response rate, average call handling time, call abandonment rate, first call resolution rate, and customer satisfaction rating. By regularly monitoring these KPIs, clear goals can be set, additional training can be provided, and feedback can be given to agents, resulting in optimal performance and a satisfying experience for customers.

Challenges in Evaluating Call Quality

Evaluating call quality can present some challenges, such as:

Unrepresentative Samples

Many centers are challenged to choose a representative sample of interactions for their evaluation. Manual and random selection of calls can be restrictive and biased, not providing a holistic view of agent performance.

Faulty Transcriptions

Conversation analysis may face the challenge of faulty transcriptions that contain partial words and do not reflect people’s natural speech. These phonetic transcriptions make it difficult to understand the conversation as a whole and lack context. While they can capture keywords, they do not provide a clear view of what is working well or poorly in the customer experience. It is important to seek conversation analysis solutions that offer accurate and contextualized transcriptions for a more complete understanding of interactions and to improve the customer experience.

Tone

Tone does not offer specific details or context, it only indicates the emotional state at that moment, without providing wider information about the situation. It is important to complement tone analysis with other tools and metrics to get a more complete understanding of the customer experience.

False Positives and Negatives

Customer conversation analysis faces the challenge of false positives and negatives. These are results that can significantly affect outcomes by incorrectly detecting keywords.

How to Overcome Challenges in Call Quality Evaluation Thanks to AI

To solve the sample limitation, it is essential to record and monitor 100% of calls using an automated voice analysis system that classifies and filters conversations efficiently. This approach frees agents and managers from laborious processes, provides a total view of call quality, and ensures that decisions are made based on objective data rather than subjective observations, enhancing conversations and effectiveness throughout the organization.

A first step to overcoming challenges in call quality evaluation thanks to AI is to use ASR (Automatic Speech Recognition) technology. This technology replaces faulty transcriptions by providing complete transcriptions and using artificial intelligence to fill in the gaps in the phonetic transcription. ASR transcription allows problems in conversations to be discovered and broader insights to be obtained.

Second, it’s important to use a transcription engine specifically developed for the call center environment, like the one offered by Upbe. This engine understands the complexity of calls in a call center, which improves the quality and accuracy of transcriptions.

Third, since tone alone does not provide enough context, AI models focus on detecting the hidden context in the underlying emotions throughout the conversation. Upbe is able to place tone perceptions in a broader context, providing information on why the customer is upset and what the agent is or isn’t doing to solve it. This kind of knowledge is crucial for making informed business decisions.

Finally, it is important to use categories based on machine learning. These categories group phrases and statements that represent the same concept, and are constantly enriched with relevant information. It is crucial to take time to audit these categories to ensure accuracy and eliminate false positives and negatives.

Success Case: Improving the Quality Audit Process with Upbe in the Telecommunications Sector

In our commitment to optimize internal processes, we identified a significant opportunity for improvement in the area of quality audits for a client in the telecommunications sector, specifically in daily or weekly mandatory listening. Our main goal was to increase the efficiency of this process and reduce the time spent on audits.

To achieve this, we implemented Upbe’s technology, which allowed us to optimize the workflow of the audits. We set up a quality template in Upbe, where we defined the evaluation criteria and established the elements and rules we consider fundamental for our clients.

Once all the calls were downloaded, we processed them automatically in Upbe, achieving to unify several recordings into a single call, ensuring consistency and uniformity throughout the process.

Thanks to the incorporation of Upbe into quality audits, we achieved a considerable reduction in the time dedicated to auditing. Now, each call is audited in much less time, which has resulted in a decrease of more than 50% in the time invested in each audit.

This achievement has allowed the time dedicated to quality audits to be reduced by approximately half of the weekly hours compared to the previous situation. This implies a significant saving in the organization’s structural costs and, in addition, auditors can now use that freed-up time to perform tasks that bring greater value to the company.

The evaluation of call quality is crucial to improving customer service in a telecommunications call center. Thanks to AI-driven call analysis, call centers can increase customer satisfaction and improve their overall performance.

Do you want to know more about how to improve the quality of calls in your telecommunications call center? Contact us and discover how we can help you.