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Good communication with the customer starts with analyze the conversations. Knowing the language used by agents to improve it is key to ensure excellence in the quality of contact center care. Nowadays, quality auditors have intelligent technologies available that allow monitoring to be carried out in a much more exhaustive and efficient way, in less time and in a more cost-effective way. We are talking about Natural Language Technologies (NLT); tools that use intelligent natural language processing (in English, Natural Language Processing) to understand, analyze or process interactions.
How do Natural Language Technologies help improve the quality of care?
In the contact center, NLTs have two key applications: Smart bots And the speech analysis tools (Speech Analytics), which combines language processing with data analytics. In turn, this technology can be applied at all points in the customer's life cycle, improving the Total Experience offered by the company.
NLT to detect empathy
Agent empathy is a key value in any instance of customer service. But evaluating the level of empathy without an intelligent system to help us can be complex and imprecise. Speech Analytics uses NLU (Natural Language Understanding) per understand the intention behind the words and detect if the agent uses empathetic expressions.
To show this with an example: analysts define expressions such as “please”, “thank you”, “could”, as indicators of empathy. Natural language processing technologies don't just recognize those words. In addition, They extract that intention from any other expression that semantically looks like these.
Personalized training for agents
Integrate Speech Analytics in quality management, it allows us to automatically identify which are the interactions with low resolution levels; or which agents have a gap in their skills that prevents them from providing excellent care.
Instead of auditors having to listen to hundreds of conversations and grade one by one the aspects to be evaluated, the “listening” is performed by the NLT automatically. Then, auditors only need to go to those where a problem was identified, to help the agent resolve it.
This makes it possible to identify if there is a problem in the onboarding process, if, for example, the same problem is detected in the conversations of several agents. At the same time, when there are agents with excellent levels of care, supervisors can use those interactions as examples for the entire team, encouraging collaboration.
Compliance
One of the most effective uses of NLT is in the service policy control (what we call”Compliance” or “Adhesion”). Non-compliance with certain protocols or regulations can be very costly for a company. At the same time, it is unprofitable to have a team of people dedicated exclusively to supervising that agents don't miss any steps or duly state the terms of a contract. What usually happens in companies is that a percentage of calls are controlled, which always has a margin of error.
With NTL, You go from auditing a random part of interactions to auditing them all with a single click. And this is not only in historical analysis, but also in real time. Language processing technology marks whether an interaction complies with the protocol at the moment; and it can even tell the agent what steps to take depending on what the customer answers.
This ensures an optimal level of adherence to policies. In turn, if It greatly accelerates agents' learning curves when it comes to protocols. This reduces onboarding and training costs. With the help of artificial intelligence, the agent is ready to provide quality care in a few days. And an agent who feels confident, prepared and supported will undoubtedly show greater enthusiasm and empathy for the customer, and this has an impact on the Total Experience.
Learn more about the uses and applications of Natural Language Technologies in our webcast.
