Call Center Monitoring Software

3 Speech Analytics Myths You Should Stop Believing

The number of misconceptions about speech analytics (SA) are surprising, especially considering that even people who currently use SA technology believe the myths. While the learning curve for SA is steep, understanding that the following notions are false will be a big help. When you know how SA does and does not work, you’ll be able to better utilize to its potential.

Myth #1: It’s Not Necessary to Listen to Actual Calls

It’s not possible to simply guess at the voice of the customer and you won’t be able to set useful SA settings if you don’t first listen to real recorded calls. The only way to figure out which business intelligence will be most useful to your company is to listen to a random selection of recordings from a specific queue. As you dissect recordings, you should do the following:

  • Map call types for specific objectives.
  • Listen to how things are said, not just what is said.
  • Create a ranking of keyword categories.
  • Identify issues that could cause trouble with the software (transfers, hold music, pre-recorded messages).
  • Interpret dialects, accents and colloquialisms.

While listening to numerous calls from beginning to end is tedious, it’s the best way to figure out which words and phrases are most meaningful when it comes to customer intent and outcomes.

Myth #2: Speech Analytics Are a Set-and-Forget Solution

SA make it easy to locate keywords and phrases, but that ability alone isn’t going to give you valuable insight. SA software has to be optimized in order to provide useful and actionable intelligence, taking into account things like the relationship between content and context or specialized language for certain industries. Furthermore, the data you glean from SA won’t be helpful if it isn’t accurate. In order to make sure that the software is at its optimal level of accuracy detection, the speech engine has to be calibrated, tested and tuned until perfect. To do this, search results have to be audited several times over until the speech engine is in harmony with your business objectives. The setup and interpretation phases may be time-consuming and even challenging, but they’re necessary.

Myth #3: Anybody Can Manage Speech Analytics

SA may run automatically, but the software has to be managed on a regular basis, too. There are a range of tasks that must be completed in order to keep SA performing well. While you may have a staff member who’s qualified to take on this role, it’s important to know what’s expected of the person in charge:

  • Software setup
  • Server and database management
  • Compilation, analysis and delivery of key findings
  • Content review

One or more people can take on this role. Since the various steps call for different skill sets, some companies opt to split up the role into three parts

  1. Administrator for setup and management.
  2. Business Analyst for choosing and delivering key findings.
  3. Interactions Monitoring Analyst to review content.

Whoever is in charge of SA has to analyze and communicate the findings, explaining why and how changes should be made.

In order to get the most out of SA, it’s important to control your expectations. SA software doesn’t rely on omniscience. Instead, it’s a smart, calibrated filter that shows you the parts of calls that relate to your specific business issues. SA software provides the tools you need to uncover actionable business intelligence. Without it, it would be nearly impossible to find these subtle points of a conversation. With it, you can apply sophisticated strategies to work toward your business objective.

How Speech Analytics Benefit the Contact Center

Speech analytics tools help companies understand more about their customers so that contact center agents can act on unstructured customer data collected during interactions. Today, speech analytics software goes beyond quality assurance to measure deeper, undisclosed customer information. When used correctly, speech analytics can give companies a competitive edge.

Identifying Customer Emotions with Speech Analytics

There’s a bright side to negative emotions like frustration and anger. When someone contacts customer service, something positive can result from the interaction, whether it’s improved customer satisfaction or insight from a difficult call. Speech analytics tools search unstructured data from customer conversations to detect emotion and stress levels by analyzing things like word choice and speech tempo. Conversations are analyzed in real-time to deliver data about calls from the start of the conversation through resolution.

4 Benefits of Speech Analytics

Speech analytics can improve a customer’s experience, inform contact center agent training, increase revenue and limit customer attrition.

1. Improving the Customer Experience

Improving the customer experience is one of the main reasons why companies use speech analytics. The software analyzes customer interactions to find reasons for the call that may be vague. Mentions of company products and competitor names and products can also be detected. Contact center agents are able to quickly understand what the customer needs so that they can start meeting those needs.

2. Managing Contact Center Agents

Speech analytics software helps to coach and monitor contact center agents. By tracking performance issues via speech analytics software, management can figure out where service quality needs to be improved. This influences both agent coaching and process updating, and is especially useful for lowering call volume and improving first call resolution. Managers are able to track things like how well calls adhere to scripts, call length, agent politeness and if regulations were met.

3. Increasing Revenue

Up-sell and cross-sell opportunities can be identified thanks to speech analytics software, which can lead to more sales. The software can also be used to learn how up- and cross-sell attempts affect customer satisfaction based on certain demographics.

4. Reduce Customer Loss

Customer attrition rates often go down after a company implements speech analytics. The software can determine when a customer is about to leave and then the agent can take the opportunity to personalize the service in an effort to get the customer to stay.