A beginner’s guide to speech analytics

Speech analytics arms contact centers with customer service and sales strategies that are driven by consumer insight.

Speech analytics tools have become commonplace in contact centers. Its adoption is driven by an array of benefits, from fraud prevention to quality management to compliance with service protocols.

But while many customer service providers are reaping these advantages, others are still testing the waters. Some are curious about its capacity to transform call center operations, while some are struggling to integrate it into their current framework.
Regardless of your reasons for delaying adoption, this article will help you understand everything you need to know about this particular subset of data analytics: the basic definitions and the most important uses.

Let’s start with the definitions.

Because of its myriad applications, no single definition of speech analytics exists. But in a contact center setting, it’s the process of analyzing recorded calls to understand customer interactions.

To give you a visual, the analytics tool is usually composed of a speech processor that converts voice to data. The same analytics software then indexes these data to make them searchable. Of course, there’s an interface that allows a user to access the database and apply specific analysis functions.

This enables managers to turn customer insight into actionable steps that enhance customer support and sales techniques. As a form of data analytics, this tool lets managers understand the topic(s) being discussed during a call and identify the emotions that may have been expressed, which are deducible via voice patterns.

What are its uses?

1. Monitor agents’ compliance.

A speech analytics tool will flag down interactions where your protocols have been breached. Of course, you’ll need to look closer at these calls for better monitoring. In doing so, you can identify who among your agents are being unproductive or not proactive. You can then re-train those agents and design skill-boosting classes to target their needs.

2. Evaluate performance quality.

Many call centers have crude evaluation processes. Some randomly pick calls to evaluate, which makes the entire process prone to errors and loopholes. An automated software can crawl through all interactions to subject them all, without exceptions, under the same rigorous quality assurance process. Moreover, you can compile best practices so you can implement a stronger and more effective way to convert customers.

3. Resolve disputes.

Operating in the customer service industry, you probably already know that every single concern a customer raises is unique. Some may not be covered by your customer service manual, and some may involve management-level decisions. Using an automated analytics software that takes into consideration the customers’ emotions, you’ll be able to implement direct solutions.

4. Gather customer feedback.

At the heart of every data analytics process is customer insight. If a call center truly understands the issues customers usually face, they can improve their business process. You can calibrate your analytics tool so they can analyze patterns in interactions.

Speech analytics allows a contact center to make sense of voice-based interactions so they can improve future transactions with customers. Championing the customer’s preferences, brand managers will be able to come up with strategies that simultaneously facilitate conversion and uplift the customer experience.