Many analysts predicted 2019 to be the year of conversational analytics; the year when businesses will be on the road to transformation because of it.
That may or may not come to pass, but there's little doubt in anyone's mind that this form of data analytics has started to make its mark in the world of business. Like other forms of analytics, this one, too, rides piggyback on technology. After all, it's a technology that's increased the speed, scope, and types of conversations. What's more, with new developments, every conversation thread can become a thousand lines of dialogue. And, as technology goes deeper into people engagement, so will conversational analytics.
Essentially, conversational analytics helps discussions with buyers become more meaningful.
Conversational analytics helps brands:
- Better analyze chats with consumers
- Enhance personalization
- Respond with the right messages for positive business outcomes
So, why now?
Why is the use of Conversational analytics rising? What are the factors that are driving the onward march?
Some may find it ironic, but today's world is increasingly using technology to talk to each other. That's right. Verbal communication has moved from just two people speaking to each other, face-to-face, from the Neanderthal age to the post-modern world we find ourselves in today. People now use the Internet, software, and gadgets to communicate not only with each other but also with machines.
In all this, someone realized that all of it was producing tons of data, which was going to waste. And that helpful soul thought – hey, how about analyzing it? That, in a simplistic manner, is what defines conversational analytics.
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Here are two leading causes for the growth of conversational analytics:
1. Messaging Apps
WhatsApp, WeChat, Facebook Messenger, LINE, Telegram, etc, are chat tools used in abundance today. The number of users across such platforms is expected to reach about 3 billion this year.
2. Virtual Digital Assistants and voice search
"Hey, Siri, can you tell me how many people use voice assistants?" A recent survey says there will be 8 billion such assistants in use by 2023. Here's another prediction – the voice commerce market will be US$80 billion by that year.
Look around you, everyone's using Google Voice, Amazon Alexa, Apple Siri, for almost everything – from flight booking to getting the news, to weather updates.
Over 45 million Americans currently possess a digital assistant or a smart speaker, according to another survey.
Smart-speaker ownership surged 36% to 53 million US people aged 18 and older, according to a survey from National Public Radio (NPR) and Edison Research. About 21% of US adults said they owned a smart speaker, such as an Amazon Echo, Google Home, or Apple HomePod.
With such staggering figures, can there be any doubt over the rise of conversational analytics at all?
Here's an illustration that will help you understand just how much data is generated with one touchpoint or channel of communication:
Melanie has asked Alexa to order pink shoes. From the moment she utters the words, "Hey, Alexa, can you order size seven pink shoes at the cheapest rates for me?" to the moment the product arrives at her doorstep, a trail of data is created. It includes her shipping address, her payment method, her foot size, her color preference, the number of shoes ordered, a refund (if any), and so on. Not to forget that her preferred way of shopping is using Alexa. And these are just the superficial data that can be easily collected with that one action of Melanie; going deeper yields more.
But perhaps the most significant boost to this form of analytics comes from the advent of real-world artificial intelligence applications.
AI, an all-encompassing term, encompasses machine learning, natural language processing (NLP), and a host of other related technologies.
AI tech is what has made two things possible:
(a) Given the technology to develop machines that interact with humans
(b) Given the wherewithal to make sense of the data that is generated, which would never be possible for even huge teams of humans.
Computers (machines) have started to understand human language. As they continue to grasp the full spectrum of human language and language-like nuances, the use of NLP will eventually lead to deep consumer customization, elevating machine-human conversations to a new level.
Overall, the advent of AI has made it possible to sift through the mounds of structured and unstructured data from such conversations, find hidden patterns, understand people's sentiment, and increase personalization. This is the game-changer in conversational analytics, one that will act as fuel in a rocket.
