How AI Speech Analytics Improve Customer Service in Call Centers?

October 18, 2021

How AI Speech Analytics Improve Customer Service in Call Centers?

What is AI Speech Analytics in Call Center?

The process of speech analytics involves analyzing conversations that are captured by call centers to identify patterns, trends, and anomalies within them.

This helps companies improve their services and products through a better understanding of what they need to do to satisfy their customers or users.

The goal of this artificial intelligence technology is to provide real-time insights into how people interact with each other during calls so as to make improvements in the way agents handle these interactions.

Why Do We Need Speech Analytics?

We need Speech Analytics because it can help us understand our customer’s needs more effectively than we could before.

It also allows us to see if there are any problems occurring at all stages of the agent and customer interaction in real time.

There are two types of data analyzed using speech analytics: audio-based data and text-based data. Audio-based data includes things like voice tone, volume level, pitch, speed, pauses, silence, background noise, and many others.

Text-based data include things like words spoken, phrases used, grammar mistakes made, and even emotions expressed.

Both kinds of data are important for providing deep insight into the quality of service provided by call center employees.

With our world depending on artificial intelligence more than ever, speech analytics has become an essential part of business operations, especially within the contact center.

Customer service teams and professionals use speech analytics every day to enhance their workflows and processes.

Speech Analytics with Human Verification

Speech analytics software greatly improves contact center performance when combined with human intervention.

Apart from contact center AI technology, human agents can listen to recordings and transcribe them, while spotting errors and inconsistencies just as effectively.

However, without proper training and experience, humans cannot always detect certain issues accurately. To ensure accuracy, a team of experts must be involved in verifying transcripts produced from automated systems.

They will then go back over those transcripts manually to check for inaccuracies and correct them accordingly.

In some cases, however, manual verification may not be enough.

For example, when dealing with complex situations such as legal matters, medical emergencies, etc., it might be necessary to have a second set of eyes review the transcriptions.

That way, both parties can get accurate information about the situation.

Voice Analytics vs Speech Analytics

There are three main differences between voice analytics and speech analytics.

First, voice analytics only looks at recorded voices; whereas speech analytics takes into account everything that happens throughout a phone call.

Second, voice analytics uses machine learning algorithms to analyze the content of the recording, but speech analytics relies heavily on natural language processing techniques.

Third, voice analytics does not require extensive knowledge of NLP, which makes it easier to implement. Contact center managers should consider these factors and make sure your company is ready for its full potential.

Is AI Used in Voice Recognition?

Yes! Artificial Intelligence is being used in Voice recognition today, which can be referred to more broadly as Voice AI…

The most common form of this type of voice-enabled technology is called Automatic Speech Recognition. ASR converts sound waves into digital signals so they can be processed by computers.

This process helps machines recognize what people say.

When you speak into your smartphone or computer, the microphone records these sounds, known as human voice commands, and sends them through the device’s processor. Then, the processor translates the sounds into text.

What is Machine Learning?

Machine learning is a field of study concerned with how computers learn without explicitly programmed rules.

ML involves developing programs that teach themselves, such as voice analytics, based on examples.

It’s also known as “artificial general intelligence” because it allows computers to perform tasks like reasoning, problem-solving, planning, decision-making, and even creativity.

How can AI Speech Analytics Improve Customer Service?

Today, many businesses use speech analytics to improve their customer support processes.

Customer conversations are often long-winded and complicated. By using an intelligent system to interpret customer issues, requests, and sentiments, companies can save time and money.

Customer calls typically involve multiple steps: first, the caller identifies himself/herself, then asks a question, and finally provides feedback regarding his/her request.

With speech analytics, all of these stages can be analyzed simultaneously.

If there are any problems during the conversation, the system can alert the agent who handles the case immediately.

Agents can take action before the end of the call if needed.

Speech analytics and customer sentiment analysis work together to provide better customer experience services.

Companies need to know whether their products and services meet expectations to further understand customer insights and customer engagement.

But traditional methods of collecting data aren’t very effective. A lot of effort goes into gathering data, analyzing it, and interpreting results.

Using speech analytics, companies can collect real-time data directly from consumers.

And since it doesn’t rely on written responses, it eliminates the risk of misinterpretation, maximizing contact center efficiency.

Speech Analytics Benefits

Some of the benefits of a speech analytics program include:

• Better quality control – By listening to customers’ calls, analysts can spot problems before they happen. It can improve agent performance as if an agent says something wrong during a conversation, he/she can immediately fix it.
• Avoid non-compliance – Reduce compliance issues through speech analytics by detecting when agents deviate from scripted answers. For example, if an agent makes a mistake while answering questions about pricing, the software could detect the error and send out a notification to the supervisor.
• More efficient workflow management – Analysts can easily identify where there are gaps in the system through actionable insights. Once identified, the entire team can work together to find solutions.
• Improved productivity – Since analysts don’t need to spend time reviewing each call individually, they’re able to focus on other important projects such as their customer service efforts.
• Increased efficiency – Because analysts aren’t spending hours listening to every single call, they can handle multiple conversations simultaneously.

Customer Service-Related Speech Analytics Tools

• Automated phone menus for banking transactions
• Voice recognition systems that allow users to search online databases without typing
• Chatbots that answer common questions like how much is my balance? What’s my current interest rate? How do I make payments?
• AI chatbot assistants that help people navigate websites
• Inventory tracking systems that automatically update stock information
• Call centers that use automated IVR systems to route incoming calls

Speech Analytics Tool Scenarios

• If you have a problem with your credit card is declined, then you will be able to find out why from the conversation transcript. You may not know whether the issue was caused by the bank itself or something else entirely. In addition, when using speech analytics, you can solve customer issues by learning about customer satisfaction levels based on different factors such as time spent talking, the number of questions asked, etc.
• A good example would be a bank where customers need to provide personal details before opening an account. Instead of having employees ask each question individually, the bank could automate the entire process using this form of speech analytics. Through AI-powered conversations, the customer can say something along the lines of “I want to open an account,” and the interactive voice response system would understand the request and proceed accordingly.
• The same principle applies to any business scenario where human interaction plays a role. The more natural the interactions become between humans and machines, the better off we all are.

Conclusion

Speech analytics has been around since the early 2000s but only recently started gaining traction within the industry due to its ability to enhance existing processes and create new ones.

As technology continues to advance at lightning speed, so does the way businesses interact with consumers. By leveraging speech analytics, organizations can improve these experiences while also improving overall contact center performance.

With the rise of artificial intelligence, companies must adapt quickly to stay competitive. This includes adapting to changes in consumer behavior and expectations.

One area that is ripe for innovation is the interactions with customers. To learn more about speech analytics, give us a call today!