Voice Analytics 101: How it Works and Best Practices

Through voice analytics, businesses can dig deeper into customers’ conversations and get relevant insights. It’s getting important for companies that heavily use voice channels for customer communication.

In this blog post, we explore how voice analytics empowers businesses to adapt to their target audience and proactively improve operational performance. 

What is Voice Analytics?

Voice analytics is a technology that captures and analyzes spoken conversations from customer service or sales calls to extract meaningful insights. It uses automatic speech recognition (ASR) and natural language processing (NLP) to convert words into text, evaluating the tone, emotions, and intent behind the conversation. 

It enables organizations to better understand customers by identifying key patterns, sentiment, and contextual cues and improve service in real-time. 

It helps enhance customer support services, monitor agent performance, and maintain compliance. Businesses can also get data like:
 

» Average Handle Time: It tracks the call duration to identify inefficiencies in each conversation to help reduce handling times

» Sentiment Data: It captures the emotional tone of the interaction, identifying whether it’s positive, negative, or neutral by analyzing tone, pitch, volume

» First Call Resolution: It tracks if the caller’s issue was resolved on the first contact by monitoring keywords and conversation flow

» Customer Satisfaction Score: By monitoring sentiment throughout the conversation, the software can help identify negative and positive experiences

How Voice Analytics Software Works?

Call center voice analytics tools use natural language processing and machine learning to convert audio data into actionable insights. Here’s voice analytics work in steps:

1. Data Collection: It records customer-agent conversations and converts the audio file into text
2. NLP & ML: Natural language processing and machine learning models analyze the transcribed text to detect patterns, keywords, trends, and anomalies
3. Sentiment Analysis: It evaluates customer sentiment to provide insights into customer emotions
4. Real-time Analysis: The tool derives real-time insights, allowing agents to adapt their approach and resolve customer issues 
5. Data Reporting: The software reports on call metrics like CSAT, AHT, and FCR, providing insights to train agents and improve customer service

The software organizes the information and provides agents with contextual insight into the customer pain points, sentiment and intent in the conversation.

Key Components of Voice Analytics 

Call center voice analytics uses a mix of various technologies to dissect voice data and uncover contextual nuances, patterns, and sentiments that may not be apparent. Let’s look at the various components. 

1. Speech Recognition & Conversion

Voice analytics convert spoken words into text using the following functionalities:

• Automatic Speech Recognition (ASR): It uses algorithms to convert audio data into written text
• Phonetic Analysis: It analyzes and identifies pronunciation patterns
• Word Spotting: It detects specific words or phrases relevant to the businesses or customer experience

2. Natural Language Processing

For a meaningful transcription, it’s important to understand the nuances of spoken language. Here are its sub-processes: 

• Syntax Analysis: It ensures a proper grammatical structure of the transcriptions
• Semantic Analysis: It helps identify the relationship between the words to understand the context of the conversation
• Pragmatic Analysis: It analyzes the intent behind the interactions using audio patterns

3. Emotion Analysis

Capturing the emotion in the conversation helps the agent adjust their approach to the caller’s emotions.

• Acoustic Analysis: It evaluates the caller’s tone, pitch, volume, and speed to interpret their emotions
• Lexical Analysis: It analyzes the words to detect the speaker’s feelings like satisfaction or frustration
• Speaker Diarization: The feature separates the different speakers in the audio stream 

4. Real-time Data Analysis

As the conversation continues, it’s important to derive insights quickly to help agents improve their performance. 

• Real-time Analytics: It automatically processes voice data as it’s captured to gain immediate insights
• Audio Indexing: It organizes and tags audio files to easily retrieve the files for analysis later
• Database Management Systems: It stores and manages large voice data volumes for easy access

5. Voice Biometrics

Whether audio or text data, security is important when collecting customer information. Voice biometrics secures the data to ensure confidentiality.

• Speaker Verification: It uses voice characteristics to identify the caller’s identity for secure authentication
• Voice Encryption: It encrypts the voice data from unauthorized access to comply with privacy regulations

How Businesses Can Leverage Voice Analytics?

You can implement voice analytics for different business use cases like:

1. 1. Customer Service: You can use the tool to monitor calls in real-time and identify issues like frequent complaints, unresolved queries, or long wait times
   2. Sales: The system can analyze sales calls to ensure the interactions personally resonate with potential customers
   3. Compliance maintenance: It ensures that agent-customer interaction complies with industry regulations

• Market Research: Using voice analytics software when conducting phone surveys can help monitor customer reactions, preferences, behavior, and satisfaction with a product or service

Exotel is an AI-powered communication platform that also offers powerful voice analytics features. Its Conversation Quality Analysis offers real-time data, sentiment analysis, and seamless integration so that you can better understand customers and improve contact center operations.

Difference Between Voice & Speech Analytics

Speech analytics identifies “what” is being said in the conversation, while voice analytics focuses on “how” it’s said. Here’s a simple breakdown of the differences between the two.

Benefits of Voice Analytics 

Businesses that rely on customer experience can greatly benefit from voice analytics software with its ability to analyze large volumes of voice data and generate meaningful insights. It can help businesses: 

⇒ Improve Customer Experience: Gauging customer sentiment helps agents identify dissatisfied or frustrated customers, offering real-time guidance to tailor their approach for each customer

⇒ Prevent Customer Churn: The tool provides real-time insights into customer sentiment, allowing early identification of dissatisfied customers. This allows agents to resolve issues and convert churning customers into satisfied customers proactively.

⇒ Ensure Compliance: The tool helps proactively analyze compliance issues by monitoring agent-customer interactions and mitigating risk

⇒ Enhance Operational Efficiency: Automating the call transcription process reduces manual effort, freeing up call center agents’ time to focus on making strategic decisions 

⇒ Improve Agent Performance: AI voice analytics software provides real-time feedback, helping agents personalize their interactions and ensure high-quality service

⇒ Improve Products/Service Quality: Analyzing vocal feedback for a new product/service survey can help recognize issues that inform the need for improvement

⇒ Make Customer-Centric Decisions: Insights derived using voice analytics can help evaluate customer pain points, needs, and demands to make customer-focused decisions

⇒ Evaluate Brand Image: The insights from customer emotions and language can help evaluate business performance and image in real time, allowing brands to address negative sentiment to improve brand image

⇒ Improve Employee Training: Voice analytics insights can help design agent training programs that enhance agents’ skills and performance. Additionally, continuous monitoring will help you identify which agents are improving or underperforming and tailor their training accordingly.

Best Practices for Implementing Voice Analytics in Contact Center 

Voice analytics software can improve call center operations and customer support experience. Here are some tips to ensure a seamless implementation:

⇒ Identify Business Goals: Consider the volume of calls you want to analyze or your project goals. This will help you determine whether you need voice or speech analytics solutions. 

⇒ Pilot Test: Conduct a pilot test to evaluate the tool in a controlled environment to make necessary adjustments before using it for a full-scale project

⇒ Consistent Monitoring: The tool can help you monitor call quality to identify areas for improvement. The insight can offer learning opportunities and help you understand what training the agents can benefit from.

⇒ Establish KPIs: Define and benchmark the performance indicators to continuously evaluate performance and call center success

⇒ Integrate With Existing Stack: Ensure seamless integration of the tool with your contact center’s existing system to ensure cohesive data flow for agents to use 

⇒ Ensure Strong Data Security: Encrypt and anonymize data and maintain GDPR compliance to adhere to data privacy and security standards. 

What are the Challenges in Implementing Voice Analytics? 

While voice analytics offer meaningful customer insights, some challenges can arise when implementing call center voice analytics. Here are some of these limitations:

Data Privacy

Contact centers often handle sensitive and personal data. Businesses need to comply with regulations like GDPR and CCPA, which require anonymization, confidentiality, and restricted access to customer data. 

Failing to comply with privacy regulations can lead to loss of customer trust and legal ramifications. 

Data Quality

The accuracy of voice analytics depends on the quality of the audio data. Background noise, accents, diverse languages, cultural nuances, or jargon can lead to misinterpretation and unreliable insights. This may affect decision-making.

False Sentiment Analysis

The AI-powered tool may not always accurately detect customer sentiment. For example, the tool may consider a raised voice to be anger when the caller may have to speak loudly due to background noise. Sarcasm may also be misinterpreted, providing false insights into customer sentiment. 

Voice Analytics: Key Takeaways

AI voice analytics can transform contact center operations by improving agent performance, enhancing customer support quality, and boosting customer satisfaction. Adapting the tool enhances businesses’ ability to listen to customers’ voices and personalize customer engagement at every touchpoint.

Exotel is an AI-powered communication platform that empowers businesses to manage customer interactions through contact center applications. Its Conversation Quality Analysis offers AI-driven voice analytics providing real-time insights into customer sentiment and agent performance. Book a demo today to transform your customer experience with Exotel. 

FAQs

How to Use Voice Analytics in your Contact Center? 

Some practical ways to implement voice analytics in contact center is monitoring agent performance, quality assurance, call routing based on caller’s needs, and evaluating customer satisfaction. 

How Can Voice Analytics Help With Quality Assurance?

Call center voice analytics automatically monitors customer interactions to flag non-compliance and report positive and negative experiences.