How to Use Speech Analytics in Your Call Center 

Every day, you collect tons of data while interacting with customers. Each call creates more and more information that you could learn from to empower your business.

How? Through speech analytics.

Before, during, and after each call, you can use data points, assumptions, and patterns to better serve your customers and get one-up on your competition. By detecting what happens in each call (and even the key presses before an agent intervenes), call centers can totally transform their customer journeys.

Let’s learn the exact benefits of speech analytics and how you can leverage them at different phases of each call.

What Are Speech Analytics?

Speech analytics are records, graphs, and collections of conversations that inform business decisions and help call center agents improve customer satisfaction. Using statements picked up during customer calls, data is translated into usable information that can influence training, processes, and workflows.

Speech analytics, one of the less marketed or adopted call center analytics, has been somewhat revolutionized by the introduction of artificial intelligence (AI) and machine learning in modern contact center solutions.

Also known as voice analytics, speech analytics consists of three main components:

• Customer sentiment: Speech patterns can indicate frustration, anger, or satisfaction in a customer’s voice.
• Keywords and phrases: Identify specific terms used by customers and contact center agents to pinpoint areas of confusion or highlight key topics.
• Agent milestones: Ensure agents adhere to scripts and company or industry policies.

Speech analytics examples and use cases

Emotion detection

When customers get frustrated with having to repeat themselves, their negative tones are detected, and the speech analytics software triggers the next action.

This action could be an alert on a supervisor’s dashboard, an on-screen prompt for the agent, or a notice that flags the call for follow-up at a later date.

Keyword or key phrase detection

If a key phrase like “renew my contract,” “I’m thinking of leaving,” or “Is there a better option” is detected by speech analytics tools, these can be flagged for various outcomes.

If account renewal is being discussed but your agent is a technical analyst, the customer might be better served by another agent. But your agent might think it’s on them to complete the call. In reality, you have specialist agents trained to deal with churn and retention, so the speech analytics technology can direct the call to the more equipped retention rep to handle the call.

Related: How Do Call Center Transcriptions Work?

Compliance assurance

In the case of contact center compliance, you could be dealing with HIPAA, PCI DSS, FINRA, or non-discrimination compliance. Whatever you need to protect your business from, speech analytics will always be scouting for potential vulnerabilities and non-adherence. 

One case could be if a new, untrained agent uses a word they are unaware is derogatory; rather than letting this go unnoticed and risk offending a customer, keyword detection flags this for the agent, supervisor, or compliance manager to get on top of any potential situation.

Likewise, if an agent starts to read back card information, going against your process, as agents shouldn’t see the actual card numbers, an instant red flag (literally in the software and figuratively) gets raised to understand why the agent has your customer’s card details.

These are some examples of speech analytics that are used in the real world. Let’s now learn how to best implement and use your call center speech analytics. 

How to Best Use Your Call Center Speech Analytics

When using speech analytics holistically, you’re covering all bases and making the most of the software at your disposal. 

You can use speech analytics before, during, and after calls to identify call trends, manage customers in real time, and create an environment of proactive coaching and feedback.

Before the call

By identifying call routing trends and analyzing past customer interactions, you get an understanding of peak call times, call volumes, common customer issues, and preferred agent skills. If you know the likely reason for each customer call, you can assign the most appropriate agent to handle each query.

You can use intelligent call routing by way of an interactive voice response (IVR) system to better understand what customers need, as they will literally say their reason for calling.

For example, instead of routing all customer service calls to your helpdesk, where they are asked for their reason for calling and manually transferred, customers are greeted with a menu that asks their reason for calling, allows them to say their issue, and knows where to send the call.

This automated process is quicker, and customers get through to the most skilled agent for their issue.

When combined with natural language processing — that’s the smart AI part — your IVR can even start handling calls for you. For routine inquiries like opening times and bill payments, there’s often no need for human intervention.

For example, think about the potential time, resource, and cost savings versus needing to handle a basic inquiry today. Even with compliance procedures to consider, several minutes per call could be saved if you’re able to successfully remove the human from the process.With conversational AI, you’ll be able to offer open-ended prompts and automate routing intelligently. It could look something like this:

It could be as easy as that, thanks to speech recognition. And you’re safe in the knowledge that phone calls are not only recorded but tracked for all kinds of emotions, keywords, and compliance.

Related: How to Use Conversational Analytics Software Effectively

During the call

What if you had a tool to detect when customers get frustrated or when agents miss opportunities to upsell a product? Sounds pretty good, right?

That’s exactly what real-time sentiment analysis does. It literally analyzes customer sentiment.

Speech analytics solutions can detect emotions in a customer’s voice and flag frustrated or angry callers. This allows supervisors to intervene and provide real-time support to agents handling these calls, ensuring a better customer experience.

In positive customer conversations, where there’s an opportunity to upsell or cross-sell, speech analytics can help identify missed keywords. Not only does this identify knowledge gaps and areas where scripts or training materials need improvement, but it also means you can flag the upsell opportunity mid-call.

You can improve agent productivity by notifying agents they missed a keyword. This way, they can mention the superior product during the call instead of missing the sale or having to call the customer back to offer them a new product.

Agents provide exactly what the customer calls for. Your business benefits from extra revenue and profit. Everyone’s a winner.

The same helpfulness applies to compliance monitoring. Using keyword detection for quality assurance and quality management, you can ensure agents adhere to regulations and company policies by identifying instances where specific phrases or topics are used inappropriately.

You’re derisking your business every time you use speech analytics — which is in every single call.

After the call

You know all those call recordings you have? It’s impossible to go through each one and separate good and bad calls, isn’t it?

With speech analytics doing the sorting and grading, you can save hours per day (weeks per year) on simply identifying calls with room for improvement.

When you have time to objectively evaluate agent performance metrics, like adherence to scripts, empathy, and active listening skills, you can better diagnose low first-call resolution rates by analyzing topics mentioned by callers. 

Rather than accepting that agents are humans and will make mistakes, you can safeguard processes and embrace continuous learning.

As a side benefit, you can also identify common roadblocks and use this data to improve internal processes and develop knowledge base articles for future reference. 

Every output from your speech analytics can reveal positive and negative feedback within conversations. Use this to understand customer pain points and areas of satisfaction to improve overall service.

Using speech analytics, you can introduce proactive call center coaching after the fact. This means you can highlight areas where agents frequently struggle. This data can identify training areas and customer needs, allowing you to proactively coach agents before they encounter those situations on live calls. 

If you run an omnichannel contact center using email, web chat, SMS, social media, etc., you can extend the power of speech analytics to text-based conversations. This is often one of the biggest reasons businesses choose a contact center versus a call center.

Call Center	Contact Center
Voice calls only	Voice plus email, live chat, social media, and video
Call reports only	Multichannel analytics
Speech analytics only	Extends speech analytics to text-based conversations in web chat, SMS, social media, etc.
Disjointed customer experience	Connected customer experience
Lacks future expansion capabilities	Able to connect to future media channels

Make Better Decisions With Nextiva

When you move from merely collecting data to using data, your business can start digging deeply into areas that excite and frustrate your customers. With speech analytics, you can turn countless hours of call recordings into actionable insights.

Be it for something as simple as changing the order of your IVR menu or as far as a total overhaul of your customer journey mapping, lean on the data collected in every customer call.

On the surface, it seems like you’re creating a better customer experience, which is true. But what you’re actually doing is optimizing agent performance and impacting your bottom line as well.

With the side benefit of creating a database for continuous training and compliance derisking, it’s hard to ignore what speech analytics can do for your business.

You’ve got all this data anyway. Why not make use of it?