Service quality is a critical component of the customer experience in call centers. But how can this quality be consistently measured and improved? This is where call analysis plays a crucial role.
Despite the valuable information that can be obtained from each customer interaction, only 17% of companies act based on these data. Advanced analytics can unlock this power, reflecting human motivations, interests, and needs in call center KPIs. However, many organizations do not fully leverage analytics technology, despite the potential benefits, such as generating predictions, reducing costs, and improving customer satisfaction.
In this post, we will explore how call analysis can elevate quality in call centers to new levels.
What is call analysis?
Call analysis is a process that uses technology to gather, analyze, and present information from calls between customers and call centers, incorporating elements such as voice recognition, text analysis, and artificial intelligence to better understand customer interaction.
In addition to tracking metrics like handling time, call volume, customer satisfaction, and wait time, call center analysis also takes into account the human component to influence call handling and the customer experience.
With the right tools and strategies, this data can help deliver an exceptional experience, increase brand loyalty, and improve overall efficiency, provided they are applied to real business challenges and used to measure and improve team performance in a contact center.
How does call analysis work?
Call analysis involves gathering important data through the tracking of phone interactions, which can range from identifying the source of a call to tracking web traffic.
Conversation analysis can be carried out in real time or post-call. Real-time analysis allows managers and supervisors to monitor interactions as they occur, while post-call analysis involves examining call recordings after they have been completed. In more advanced cases, artificial intelligence is used to analyze these transcripts in search of key words and phrases, allowing for accurate evaluation.
These data are visualized in reports to assess trends and performance in areas such as sales, marketing, and customer service. In addition, these analyses can be integrated with other tools, such as customer relationship management tools, to drive optimizations and make data-driven decisions.
Of course, calls are not the only contact path your customers use to communicate. Those with an omnichannel contact center that includes calls, social media, chat, and email have the opportunity to access even deeper analysis. The next step beyond call center analysis is business intelligence, which allows for aggregating all the information about customer behavior.
Benefits of Call Analysis on the Quality of Call Centers
The main benefit of analyzing calls is that it can scale the call center operation and achieve consistency. But not only that, some additional benefits are also:
Improves customer satisfaction
Call analysis provides essential insight into customers’ expectations and opinions about the service offered, allowing call centers to adapt their procedures to improve the user experience. Call reports are valuable for evaluating call center performance and improving customer experience.
Data collection
Effective handling of call data is essential for any business. Each company should look for an analysis platform that suits its KPIs and track important metrics throughout the customer journey. A suitable strategy is needed that includes comprehensive data storage and analysis software that quickly transforms data into actionable information. In addition, proper integration of contact center software with tools such as CRM, chat, and email is crucial for collecting quality data and providing relevant real-time information.
Agent training
By analyzing customer interactions, managers can identify areas for improvement and provide detailed and constructive feedback to agents, boosting their effectiveness and efficiency in dealing with customers. Data and metrics can be strong incentives when directed towards appropriate behaviors. Through measuring and reporting performance, it is possible to accelerate progress and achieve set goals.
Performance tracking
Call center supervisors use specialized reports to monitor and improve agent performance. These reports help identify areas for improvement and provide additional training when necessary. Analysis and machine learning are also used to predict the quality ratings of future calls, improving efficiency in evaluation and facilitating the identification of high-quality interactions and outstanding agents.
Cost reduction
Call analysis can also help reduce costs by identifying trends and patterns that can be used to optimize call center operations. Cross-channel analysis and customer information collection enable automated workflows and exceptional service provision.
Real-time action
Adopting real-time analytics in call centers is key to improving the customer experience. Cloud-based phone systems allow agents to access relevant customer data during interaction. It is important to use solutions that provide real-time information, combining customer feedback, call center performance, and business outcomes, to provide an exceptional experience.
Data-based decision making
It is crucial to have proper organization and an action plan to prevent data from becoming noise and making incorrect decisions based on incorrect data. Implementing call analysis technology can help avoid mistakes and maximize benefits. Each step requires careful consideration to get the most value.
How does AI help you improve call analysis?
The application of artificial intelligence to call analysis can bring exceptional sophistication, from seamlessly incorporating conversational data (calls) and metadata into a cloud platform, to generating meaningful insights based on large volumes of information.
AI’s ability to continuously learn is essential for refining call analysis. SaaS tools like Upbe, in addition to offering these capabilities, allow for:
- Increasing the ability to integrate more and better data: conversational data that can help improve agent capabilities, such as adherence to sales pitch.
- More complete call analysis samples: with Artificial Intelligence you can analyze 100% of your calls and thus eliminate biases in agent training and evolution, or understand widespread incidents and their impact.
- Understanding customer context: data related to the use of silences, speed and tone of conversations can be crossed with sentiment analysis, which can increase the ability to understand customers.
- Scaling the operation: AI can help automate analysis processes, reduce decision-making times, and even reduce cost structure to make the operation more productive.
To demonstrate, we offer an example of companies that have used Upbe to improve the quality of their call center services:
Success Case in Improving Quality by Applying Upbe in Call Analysis
One of our most recent success stories comes from the telecommunications sector, where we managed to revolutionize the customer service process of a prominent company. We took care of identifying call reasons and business impact attributes, and we carried out automatic audits of the customer service and retention service. These actions allowed the automation of tasks previously carried out by the quality department staff.
Each month, a total of 50,000 calls, or 336,330 minutes, were processed in the customer service department and 9,000 calls (84,436 minutes) in the retention department. As a result of our interventions, we were able to reduce by 20% the number of auditors in the customer service department. The retention department also experienced a significant reduction in the number of auditors. In addition, we managed to identify the reasons for customer churn.
This represented a total reduction of 70% in structural costs by automating 100% of the process using the company’s own internal data. These achievements are a clear demonstration of how our approach can improve the efficiency and effectiveness of customer service.
Final Thoughts
In conclusion, call analysis is a powerful tool for improving quality in call centers. We have seen how it helps to improve customer satisfaction, effectively train agents, and reduce costs, establishing a clear vision and a defined strategy in the call center. Customer experience is key, and companies that adopt analysis in their call centers can reap significant benefits starting right now. With the right solution and an agile strategy, it can be achieved.
Want to learn more about how call analysis can benefit your call center? Contact us and get more information.
To improve the quality of calls in a call center, it is essential to implement an automated voice analysis system to objectively monitor all calls. Using technologies like Automatic Speech Recognition (ASR) can improve transcriptions and make it easier to identify problems. It is crucial to apply the context of the calls in the analysis to obtain valuable information, and to use categories based on machine learning to extract meaningful insights.
A quality analyst in a call center monitors and evaluates interactions between agents and customers, using voice analysis systems and technology to record and transcribe calls. They analyze this data to identify trends and areas for improvement, and use the context of machine learning-powered conversations and categories to better understand customer experiences. In addition, they provide feedback and training to agents to improve their performance.
Quality control in a call center refers to the procedures and systems implemented to monitor, evaluate and improve the interaction between agents and customers. This involves the use of voice analysis technology to record and transcribe all calls. These transcripts are then analyzed to identify trends, areas for improvement, and compliance with service standards. Quality assessments are used to provide feedback to agents, and training is conducted to improve their performance based on these assessments.
Call quality refers to how an interaction between a call center agent and a customer is handled. This includes various aspects such as the agent’s ability to resolve the customer’s inquiry effectively, their level of courtesy and professionalism, response time, adherence to company scripts and policies, and overall customer satisfaction with the interaction.
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