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Conversational Analytics and First Party Marketing

As a business, making sure you can access reliable first-party data is very important. While you might already have access to data like websites, history of customer purchase, paid searches, and such digital information, these pieces of information provide little insight to marketers. That’s where conversational analytics comes in.

Meaning of Conversational Analytics

Conversational analytics uses natural language processing (NLP) technology to extract data from human conversations and speeches. NLP enables computers to “comprehend” human conversation and speech. NLP relies on artificial intelligence (AI).

Conversational analytics and first-party marketing go hand-in-hand. Conversational analytics will provide you with insightful information on the interaction between your customers and your brand and what they think about your service or product, and most importantly, what they say about your brand.

What this means is that when people talk about your brand Conversational analytics will extract important data from these conversations and present it to you.

How Speech Analytics Works

Speech recognition or speech analytics find application almost everywhere. You must have interacted with Siri, Alexa, or any other speech-intelligent software. What you might not have known is that it is speech analytics that makes this software work.

Speech recognition focuses on phonemes, the building blocks that make words. For example, the English language has 42 phonemes that speech recognition software use to “understand” what is said. When someone speaks, the software breaks down the speech into phonemes.

Conversational AI and First-Party Data

Conversational analytics is considered the number one source of first-party data. Today, many consumers are not impressed with purely digital purchases that only involve point-and-click. They want a blended experience that also involves some element of conversation.

In fact, research has shown that up to 70% of consumers get angry or frustrated if they can’t contact human sales representatives. Whether it’s a text message, an online chat, or a phone call, consumers want human reps that they can connect with.

This means, as a business owner, you need to devise ways of listening to your customers. You’ve to hear what they say and understand it. In simple terms, you need conversational analytics to help capture what your customers say and convert it into actionable data.

If you frequently have phone conversations with your customers, then you have a goldmine of useful data. But this data can only be useful if it is recorded and kept safely. You might be engaged in thousands of hours of phone calls, but if you don’t have these conversations recorded, this data will go to waste.

But why are these calls important? From the calls, you learn why they called, what calls led to purchases, what made the purchases go through, whether there are more calls for sales or services, and whether they are mad or happy.

The information you can get from their calls is very important for business decisions. But imagine if you’d have to spend hours listening to recorded calls and trying to extract this information, how long would it take you? Days, weeks, months, or years?

This is where you can apply conversational artificial intelligence. Speech recognition software will do the listening and extract the data that you need in a very short time.

Challenges Of Conversational AI

Computers are machines that depend on human-built software to “understand” speech. The challenge here is that human speech has lots of nuances. Unlike computer programs that deal with straightforward mathematical equations to solve problems, speech recognition software relies on logic and follows loose patterns.

Take the English language as an example. People don’t talk the same. People use different accents, phrase patterns, colloquialism (slang), inflections, and varying use of words. One word may mean different things depending on which region you are in.

As you can see, it is difficult to build an algorithm that can help computers “learn” to process human language. However, a lot of progress has been made to incorporate AI in speech recognition. The good news is that research is still ongoing on conversational analytics and first-party marketing.

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