What is speech analytics and how can it benefit your business?

Customer interactions are rich with insights that can help you improve customer satisfaction and meet their needs more effectively. However, unless you use speech analytics, you probably only gain a fraction of the available insights.

In this post, we'll explore how speech analytics works, its benefits, and its real-world applications. We'll also see how platforms like Twilio help businesses get up and running with speech analytics to service customers better.

Let’s start by defining the difference between speech analytics and voice analytics. Then, we’ll dive into the inner workings of speech analytics.

What is speech analytics?

Speech analytics examines recorded calls and focuses on the words spoken, helping businesses gain a clearer picture of customers’ needs and preferences.

What is voice analytics?

Voice analytics, which is different from speech analytics, focuses instead on how the customers say the words spoken, picking up on tone and emotion.

How does speech analytics work?

Speech analytics is a sequence of processes designed to extract meaningful insights from customer interactions. It begins with raw call data—a recording—and turns it into actionable feedback that a business can use to make informed decisions.

This involves the following processes:

• Call recordings: Capturing a customer call for analysis
• Transcriptions: Transcribing a voice recording into text, enabling customer service agents to sift through the content
• Transcript analysis: Identifying patterns, keywords, and sentiments that stand out through advanced, AI-based algorithms
• Feedback loops: Relaying insights back to customer service agents, guiding them as they refine their approach

While the mechanics of speech analytics are quite straightforward, the real value of speech analytics shines when we consider its impact. Let’s focus on the tangible benefits for businesses through speech analytics.

Benefits of using speech analytics in call centers

Once the frontline of customer interactions, call centers have transformed, emerging into digital contact centers that recognize that customer engagement happens across more channels than the telephone. Voice calls, text messaging, chat, email, and social media are all channels customers use to engage with businesses.

As businesses integrate speech analytics into customer operations, contact centers do more than resolve customer queries, gaining deeper insights into the behaviors and preferences of each customer. The benefits of integrating speech analytics abound. Let’s look at the top ones.

Enhanced customer experience

Conversations have nuance. Humans can pick up a certain degree of that nuance when skilled. However, as speech analytics applies AI algorithms for pattern recognition and sentiment analysis, we can gain significantly deeper insights.

When customer service agents apply those insights, they can tailor their responses better, leading to more satisfied customers.

Operational efficiency

Businesses can also apply speech analytics to aggregate conversations to surface trends and patterns across an entire customer base. For example, speech analytics can discern a common issue that multiple customers face, even though the customers describe their experiences in different ways. This type of insight may not be attainable through an individual, human-based call review.

By identifying recurring customer issues through speech analytics, your business can streamline its processes to ensure quicker and more effective issue resolution.

Training and development

By reviewing insights across customer interactions, your business can discover and highlight areas where customer service agents may need additional training or resources. For example, speech analytics may determine:

• Customers typically respond positively to an upsell offer if phrased one way over another.
• Customers typically have less satisfaction with a resolution of a specific issue when worded in a certain manner over another.

Management can take these insights to train agents, providing continuous improvement to their interactions and business processes.

Compliance and risk management

As you monitor calls and perform speech analytics, ensure that your customer interactions adhere to certain standards. For example, compare these against business baselines or even regulatory guidelines. Regardless, speech analytics helps you to reduce potential risks by guiding you toward regulatory compliance.

With these benefits in mind, it's clear why many businesses are eager to integrate speech analytics into operations. But how might you achieve this in real-world scenarios? Let's look at some practical examples and use cases to see speech analytics in action.

Speech analytics examples and use cases

Although the applications of speech analytics can extend to all industries and operational areas, its usefulness in customer contact and call centers is clear. Let’s highlight a few scenarios where speech analytics shines.

Sentiment analysis

Perhaps most commonly, speech analytics helps businesses perform sentiment analysis. By examining the words customers use, a business can determine a customer’s sentiment toward a product (or service or topic). For example, a customer may have a favorable view, an unfavorable view, or a neutral view.

Insights from sentiment analysis enable companies to address concerns proactively—before the concerns grow more severe—to promote a positive customer experience.

Churn prediction

Speech analytics can also help you identify signs that a customer might consider leaving. This aids businesses in predicting customer churn, which is vital when interested in customer retention.

As customers express dissatisfaction or raise inquiries about contract termination or pricing adjustments, your speech analytics engine can pick up on these trends and send alerts. In response, you can take steps to address the customers’ concerns to retain their business.

Sales optimization

In business, every customer interaction is a potential sales opportunity. Speech analytics, trained on AI/machine learning models to recognize patterns, can detect moments in a conversation when there's an opening to upsell or cross-sell. Businesses can use these insights to train agents to identify these moments and take action, resulting in boosted sales.

Issue resolution

A recurring issue encountered by a large number of your customers can indicate a silent business concern. Addressing one customer inquiry at a time, your customer service agents may not see the pattern behind support requests, and your business will be late to rectifying the issue. However, speech analytics can help you to pinpoint common problems, whether a flaw in your product or a gap in your service.

By giving you the insights to take a proactive approach to issue resolution, you’ll not only resolve individual complaints quicker but also implement broader operational improvements.

What to look for in speech and voice analytics software

Choosing the right tools is critical to building a high-performing business. With speech and voice analytics, the software and services you choose are no exception. To choose the right tool, consider whether the tool meets your current needs and whether it’ll adapt to future challenges.

Key features to consider include:

• Accuracy: Your speech analytics software must transcribe and analyze calls correctly. Any inaccuracies can lead to incorrect insights and, consequently, flawed business decisions.
• Real-time analysis: Up to this point, we’ve primarily focused on post-call analysis. However, some speech analytics engines can provide real-time insights during a live call. Insights such as these can be invaluable, giving immediate feedback to customer service agents who can subsequently adjust their approach to improve the customer experience.
• Integration capabilities: The software you choose should seamlessly integrate with other tools and platforms used in your contact center. However, adopting new tools always runs the possibility of introducing a wrinkle in your process. So the easier the integration, the fewer bottlenecks you’ll encounter to get up and running with your new tool.
• Scalability: As your business grows, so will the volume of your customer interactions. Expect to grow and choose tools that can scale adequately with that growth. The speech and voice analytics software you choose should be able to handle increased call volumes without compromising performance.

Speech analytics and Twilio: Powering the future of customer interactions

It’s clear that speech analytics has the power to transform customer interactions. Yet, the full potential of speech analytics materializes when paired with the right platform. Twilio Flex is a digital engagement center that centralizes your customer interactions across all channels while allowing you to leverage powerful integrations such as speech analytics.

Among the ways you can integrate functionality into Flex is by leveraging Twilio Programmable Voice. Its Voice API simplifies the task of integrating speech analytics, facilitating transcripts, and Voice Intelligence to turn every customer conversation into growth-driving insights.

The benefits of speech analytics are substantial but even more amplified when combined with these features from the Twilio platform.

Speech and voice analytics FAQs

What are the different types of speech analytics?

There are primarily two types of speech analytics that businesses often leverage:

• Real-time speech analytics operate during a live call. As a customer service agent speaks with a customer, the speech analytics software transcribes the conversation in real time and runs the text through an analytics engine, providing immediate insights. This enables customer service agents to adjust their approach or address specific issues during the interaction.
• Post-call speech analytics occur after the call has concluded. The speech analytics software transcribes and analyzes the recorded call to surface patterns, customer sentiment, and other issues. This type of analytics is particularly useful for customer service training and detecting and resolving recurring issues among multiple customers.

What technology is used in speech analytics?

A combination of technologies power speech analytics, many of which stem from AI. Broadly speaking, AI involves machines performing tasks that typically require human intelligence, such as problem-solving, perception, or reasoning.

Within AI, technologies used in speech analytics include:

• Machine learning (ML): This is a branch of AI in which computers use vast amounts of training data to learn patterns and predict outcomes. The more data fed into an ML model, the more its prediction accuracy and efficiency improve.
• Natural language processing (NLP): This is a branch of AI that enables computers to understand and interpret natural language from humans, either through voice or text. It also contributes to the formation of responses in a natural manner.
• Voice intelligence: This is ML and NLP applied to transcribed voice calls to extract meaningful insights from conversations, including summarization and sentiment analysis.

For a deeper dive into these technologies and more, check out Twilio’s AI glossary.