How to improve service quality with natural language technologies


Good customer communication starts by analyzing conversations. Understanding the language used by agents is the key to guaranteeing contact center service excellence. Quality auditors now have intelligent technologies that allow them to conduct monitoring in a much more comprehensive and efficient way, in less time and more cost-effectively. We’re referring to natural language technologies (NLT); tools that use intelligent natural language processing (NLP) to understand, analyze or process interactions.

Natural Language Technologies y Speech Analytics

How do NLTs help improve service quality?

In the contact center, NLTs have two key applications: intelligent bots and speech analytics, which combine language processing with data analytics. In turn, this technology can be applied to all points of the customer lifecycle, enhancing the total customer experience offered by the company.

NLT empathy detection

Agent empathy is always a key value of customer service. But assessing the level of empathy without the help of an intelligent system can be tricky and inaccurate. Speech analytics uses NLU (natural language understanding) to understand the intent behind words and detect if the agent is using empathetic expressions.

For example, analysts define expressions such as “please“, “thank you“, “may I“, as indicators of empathy. Natural language processing technologies don’t just recognize these words. They also extract that intent from other expressions that semantically resemble them.

Natural Language Technologies

Personalized training for agents

Integrating speech analytics into quality management automatically identifies which interactions have low levels of resolution, or which agents have a skills gap in providing excellent service.

Instead of auditors having to listen to hundreds of conversations and score them individually , NLTs “listen” automatically. Then, auditors just need to look at those conversations with an identified problem to help the agent resolve the issue.

This makes it possible to identify  whether there’s a problem with the onboarding process, if, for example, the same problem is found in multiple agent conversations. At the same time, when agents have excellent performance, supervisors can use those interactions as examples for the entire team, encouraging collaboration.


One of the most powerful uses of NLTs is in monitoring service policies (what we call “Compliance” or “Adherence“). Failure to comply with certain protocols or regulations can be very costly for a company. That said, it’s not cost-effective to have a team of people dedicated exclusively to ensuring agents remember every step or properly state the terms of a contract. Typically, companies monitor a percentage of calls, which always have a margin of error.

With NLT, you go from auditing a random sample to auditing all interactions with a single click. And this isn’t just with historical analysis, but also in real time. Language processing technology checks whether an interaction complies with the protocol in real time; it can even tell the agent what steps to take based on the customer’s response.

This guarantees the highest  level of policy adherence. Plus, it greatly improves the agent learning curve, reducing onboarding and training costs. With the help of artificial intelligence, an agent is ready to provide quality service in  just a few days. And an agent who feels confident, prepared and supported will undoubtedly show greater enthusiasm and empathy towards the customer. This in turn has an impact on the total customer experience.

Learn more about the uses and applications of Natural Language Technologies in our webcast.

Webcast - Natural Language Technologies