For a company aiming to engage customers with personalized experiences, data is invaluable. But how can they gather data from phone conversations between customers and call center agents? Speech analytics in the call center addresses this challenge by effortlessly transforming unstructured voice data into actionable insights.
Despite the rise of text channels, voice communication remains the primary mode of interaction. This underscores the importance for modern customer service centers to possess tools for voice recognition and transcription into text for analysis. Hence, our discussion today revolves around speech analytics in call centers.
What is call center speech analytics?
Speech analytics is a technology that utilizes artificial intelligence (AI) and machine learning to analyze audio recordings of phone conversations between customers and call center agents. Speech analytics identifies words and analyzes audio patterns to detect the emotional tone of voice, track operator productivity, and evaluate call quality.
Note: if you’re curious about modern technologies, you’ll likely find the article “Will AI Replace Call Center Agents?” fascinating.
Are speech-to-text transcription and speech analytics the same thing?
Voice transcription, also known as speech-to-text recognition, and speech analytics technologies are connected but not identical. Voice transcription converts spoken language into written text by utilizing algorithms and technologies to recognize speech and transcribe spoken words into text.
On the other hand, speech analytics extends beyond mere transcription. It involves analyzing recorded conversations to extract valuable insights. Techniques such as natural language processing, sentiment analysis, and identifying keywords, context, and emotional tone are employed in speech analytics. This allows for the detection of trends, patterns, and customer sentiments within extensive audio data sets.
What is speech analytics used for in call centers?
Speech recognition technology enables the analysis of phone conversations. By combining speech and text analytics, companies can extract practical insights to enhance future customer interactions and develop communication strategies.
The advantages of speech recognition and analytics can be illustrated with a practical example. Imagine a call center with 25 agents working in two shifts. If each hour of their 8-hour shift includes an average of 45 minutes of conversation, the center collects about 300 hours of audio material per day. Processing this volume of data would require approximately one specialist a month. This is where speech analytics becomes invaluable.
Simple voice-to-text conversion
Yes, simple voice-to-text transcription can be incredibly valuable, as people typically process textual information faster than audio. Reading transcriptions of audio conversations in call centers helps monitor service quality, identify agent errors, pinpoint script weaknesses, improve training programs, and more.
Keyword in voice recognition
The next level of speech analytics involves identifying keywords and phrases used by both the customer and the call center agent during the phone conversation. Firstly, these words and phrases act as navigational markers in the stream of audio data. They can be used to analyze the completeness and quality of information provided, search for key moments in the conversation, and monitor script adherence. Thus, speech recognition helps identify areas where customers may feel disappointed, concerned, or dissatisfied.
Machine pattern analysis of speech
Advanced speech analytics, using artificial intelligence tools to recognize specific topics in conversation, intonation, speech patterns, and more. Leveraging state-of-the-art machine learning and natural language processing (NLP) technologies, speech analytics at this level provides a deeper understanding of customer experiences and expectations, identifies and resolves problematic interactions between customers and support services.
Real-time speech analytics
Real-time speech analytics enhance interactions between call center agents and customers. For example, real-time topic detection technology can provide agents with recommendations for relevant products or solutions based on key phrases and words mentioned in the dialogue.
With the trend towards self-service growing, it is anticipated that speech recognition and language analytics technologies online will soon find wide application in the field of voice chatbots.
Speech analytics: 5 benefits for call centers
In call centers, speech analytics offers five key advantages:
Customer effort score (CES) reduction
Speech analytics allows identifying areas that require significant customer effort. Typically, a high CES level indicates how easy or difficult it was for customers to achieve their desired action or problem resolution. Speech analytics enables:
- identifying words, phrases, and emotions indicating disappointment and complex customer experience moments (CX);
- identifying problematic areas in products, services, or processes;
- determining inefficient and time-consuming service stages.
For example, if customers articulately point out certain difficulties in interacting with the business during conversations with call center agents, the problem can be identified through key words. This information should be used to improve the specified aspects.
You can find more information about Customer Effort Score calculation in our article “What is Customer Effort Score (CES)?”
Service quality control
Speech analytics in the contact center quality assurance powerful tool. This is especially relevant now, as remote work in call centers is a global trend. An agent interrupts the customer, makes long pauses in the dialogue, deviates from the script, uses a lot of filler words, or inappropriate language. Instead, imagine software that monitors 100% of phone conversations in real-time and prompts agents to adhere to customer service standards and scripts for exceptional service.
Real-time speech analytics also allows contact centers to defend against social engineering threats by tracking when personal data is requested and flagging potentially risky requests to managers for verification. This can be particularly useful in industries such as banking, insurance, and healthcare, where customers and call center agents deal with sensitive personal data.
Improving Employee Experience
In modern contact centers, there is a trend towards multitasking for agents, which often leads to quick burnout and high turnover. Speech analytics as a solution for process automation enables the easing of the agents’ workload by automating tasks such as data entry, quick information retrieval from the knowledge base, and real-time prompts. By simplifying interaction with the agent, speech analytics enhances their Employee Experience, speeds up agent adaptation time, and contributes to their retention.
By the way, the issue of burnout among remote call center agents is currently relevant and quite widespread. Therefore, we highly recommend reading our article “How to Work in a Call Center Remotely and Avoid Burnout in the First Month?”
Customer satisfaction
As we have already noted, call centers generate a huge amount of unstructured data, but without proper software for listening to and analyzing this data, it is of no use. Unlike listening to individual conversations in small samples, speech analytics provides a comprehensive picture of the interaction between customers and the business. Speech recognition tools in call centers structure and provide information about:
- market trends and customer needs;
- root causes of problems or complaints;
- patterns of customer disappointment reasons;
- feedback analysis.
Speech analytics can be integrated with other systems such as call center CRM, ticketing systems, or data analytics. This allows companies to consolidate various sources of information and gain a complete picture of customer interaction. Thus, the use of speech analytics contributes to improving NPS and ensuring customer satisfaction at every touchpoint.
Cost reduction
Speech analytics software processes hundreds of hours of phone call recordings in a matter of minutes. This allows for a reduction in the number of employees responsible for quality of service. Additionally, speech recognition software significantly reduces the time agents spend processing and post-processing calls. With the help of speech analytics, the number of agents can be reduced by increasing their work efficiency and decreasing the training period for new operators.
Conclusion on speech analytics in call centers
Speech analytics is a powerful tool for optimizing call center operations. Its application allows companies to extract valuable information from conversations, improve the quality of customer service, and make data-driven decisions. Through automatic conversation analysis, trend and pattern identification, quality control, and individualized customer approach, speech analytics opens up new opportunities for optimizing business processes in contact center services.
