How it Works

How AI Works for Conversational Analytics

Artificial intelligence technology can be used in more situations than ever before, including conversational analytics. When most people discuss conversational AI, they refer to chatbots or something similar instead of the analytics aspect, but both are important advances.

 

In fact, the two applications are highly interconnected. Chatbots can be used within a business to offer easy access to the conversational analytics that AI provides. But how does this artificial intelligence work?

It Does Vary Slightly Based on the System

While the general process of how artificial intelligence works for conversational analytics is the same across software platforms, some of the specifics will be different. After all, companies need to stand out to remain competitive, and slight variations are one way to do so.

 

You should also keep in mind that analytics and AI market leaders, like IBM, Microsoft, and SAP, previously had the lead on AI-driven data analytics and conversation. Today, however, there are many more options available as the technology advances and becomes more accessible.

How Conversational Analytics Works

Before getting into how the AI in conversational analytics functions, consider the components needed for any type of conversational analytics. It begins with a transcription engine that turns all the raw audio into text. From there, indexing layers will sort data, making it possible to search it using a query tool. There are also reporting applications that display graphics or complete basic analytics of the text. This is combined with employees completing additional analytics.

The AI Requires Conversational Interpretation

When working with artificial intelligence for conversational analytics, the AI must be able to perform some element of interpretation. This is particularly true in situations when companies want the data accessible via an AI chatbot. In this situation, the artificial intelligence needs to understand that specific information that the requester wants based on how the company, or even the department, defines the words used. To maximize the effectiveness of conversational analytics, the AI must understand the semantics involved at the level of the request.

Understanding Conversational AI

To better understand how AI works for conversational analytics, take a moment to review how it works with conversational AI, also known as chatbots. In this case, the artificial intelligence can be simple or complex. Simple chatbots will use an if-else network and a flow chart, guiding users through the chart to deliver a response. More complex approaches rely on deep learning and other advanced techniques to actually understand what the person says and create the appropriate response. Most conversational artificial intelligence will combine these two approaches, so the complex tech does not need to cover as vast of an area.

 

Both approaches require natural language processing, which we will discuss in a moment. They also need something like a neural and generative conversation model that can ask variations of questions if the chatbot does not understand part of the query. Some may also use a small talk module or something similar to take care of random open-ended questions. In this case, the AI would figure out the context of the conversation and create an appropriate response.

Using Natural Language Processing

Unsurprisingly, both conversational AI and conversational analytics with AI rely heavily on natural language processing. In both situations, the artificial intelligence must be able to interpret what is being said. This is true whether the AI is a chatbot reporting data to an employee or reviewing conversations to perform analytics.

 

While no language processing or semantic understanding is needed to find the most commonly used words in conversations, it is necessary for anything more advanced. To be able to rate a conversation or agent, the AI must understand what occurred during the conversation and whether the issue was resolved. This is next to impossible without natural language processing.

Conversational Analytics Work Regardless of Bot Type

Those who work with conversational analytics have experience working with a range of bots, from the decision trees to the more advanced ones described above. Whether a bot relies on artificial intelligence, analytics can help enhance the creation of a bot that improves engagement, user acquisition, and monetization.

The AI Also Uses Transcriptions

The best artificial intelligence for conversational analytics will look at transcribed data as well as the raw audio data. This can be incredibly useful by providing an easier-to-evaluate form of data. It also helps developers and those in customer-facing positions evaluate the intent behind phrases. With the transcriptions readily available, developers behind the artificial intelligence can see if any adjustments need to be made to the AI. At the same time, those using the analysis can easily view examples of the results, helping to further ease the interpretation of data.

 

The way that artificial intelligence works is constantly evolving as developers come up with more effective solutions or those that produce more detailed results.

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