Don’t Rely on Guesswork and Assumptions to Understand Contact Center Conversations

Don’t Rely on Guesswork and Assumptions to Understand Contact Center Conversations

4 min read
artificial intelligence

Imagine you walk into a room or join a Zoom call late and two people are already in the middle of a conversation. While you can easily understand the words they are speaking, you don’t have any context to help you figure out the meaning of what they are saying. You don’t know anything about what or how much has already been said. What happens next could lead to a serious misunderstanding if you make wrong assumptions about the topic, the situation, and how the participants feel about what they are discussing.

A machine listening to a human conversation faces a similar challenge: It may be able to accurately recognize the language and the words being used, but it is limited in its ability to correctly understand the context in which those words are spoken, such as the intentions of the speakers or the identity of the things they’re talking about. That’s the situation with many basic artificial intelligence (AI) solutions. They may accurately identify words being said, but can’t derive deeper insight and value because they lack ways to understand and apply context.

Going With the Flow for Contact Center Calls

However, narrow AI (AI applied to specific use cases and/or domains) has an advantage: it can learn to understand conversations by analyzing conversational clues and patterns. In the case of contact centers, conversational AI designed for contact center environments can use something we call conversational flow analysis to increase its understanding.

Using conversational flow analysis, conversational AI can automatically identify in real time key classifying elements from a conversation between humans. For the contact center, these elements are sections based on a common set of topics and include parts of the conversation such as the greeting, introductions and authentication, problem description, resolution, and so on. By training the conversational AI to recognize these phases, it can correctly identify sections of interactions in real time.

Fortunately, most customer service calls follow predictable patterns. For example, the customer states who they are, and then presents a reason for calling. The agent and the customer engage in a dialogue aimed at getting to the root of the customer’s problem. The result of that process is a resolution that addresses the issue that caused the customer to pick up the phone in the first place.

If the conversational AI can correctly identify these predictable parts of the call, it can use this information to significantly enhance its understanding of what is going on from moment to moment during the interaction. For instance, it may correctly identify whether numbers being spoken are the customer’s phone number (entity recognition), or if some statement by the customer is a likely place to look for their problem (intent recognition). It can then supply details about the call to other parts of the conversational AI platform to optimize the conversation, analyze the call for insights, automate agent work, and more.

Workarounds Don’t Work Very Well

Conversational flow analysis helps take the guesswork out of understanding the conversation. That’s why it’s important to ask about this capability before you invest in a new conversational AI solution. Otherwise, anything you deploy might be like walking into a room in the middle of the conversation and guessing at the context. Sometimes it will be right, but just as often, it may be wrong.

Don’t be persuaded that shortcuts and workarounds in other solutions work just as well. Without conversational flow analysis, an AI solution has to rely on identifying certain keywords, timing of dialogue during the call, or other simple tricks to make guesses about what’s going on in the call. These solutions have difficulty recognizing the specific details of the conversation, which are necessary for automating optimizations that make the journey of the customer and agent as smooth and seamless as possible.

Only conversational AI that uses conversational flow analysis (among other essential capabilities) can see beyond a bird’s eye view and hone in on the details of what’s happening during the call. While vendors of solutions without this capability may claim that it’s not essential or important, their solutions can’t come close to understanding as much of human-to-human conversation as conversational AI with conversational flow analysis.

Using Deep Understanding to Drive Business Value

With more accurate and deeper understanding thanks to conversational flow analysis, conversational AI can better predict intent, guide agents with appropriate information and actions, help resolve issues faster, correctly analyze and optimize the conversation, and extract information for automating after-call work and promises management. Knowing this, why would you settle for anything less?

Learn more about conversational flow analysis in this article “Why Conversational AI is Incomplete Without Conversational Flow Analysis.”

Table of Contents