Voice recognition tech understands you. Now isn’t it time you understand it?
Understanding Voice Recognition and Natural Language Processing
Voice recognition uses an artificial intelligence technology called natural language processing (NLP). NLP refers to a machine interpreting what human language means with an acceptable degree of accuracy. We might call this the machine “understanding” what is said.
For instance, Google Translate “understands” the text you type, then generates its translation in whatever language you select. NLP might also be used to detect—again, with varying degrees of accuracy—the emotional tone or sentiment of writing or produce a sensible summary of a longer piece of content.
Apple’s Siri and Amazon’s Alexa are two popular consumer tools that use NLP. In both cases, what marketers know as “voice recognition” is actually NLP.
To keep this definition accessible, we’ll refer to the AI technology behind Siri and Alexa by the broad term NLP. In reality, NLP consists of different types of technologies and algorithms like machine translation and speech recognition. For our purposes, it’s enough to know that AI powers voice recognition and NLP is a type of AI.
In the case of Siri and Alexa, NLP will process your spoken words and determine what they “mean” to the system. Then, Siri or Alexa will come back with (hopefully) the accurate response to your command or question.
The result is a system that “understands” what you say and responds right away with an answer.
For more AI definitions, read this post.
Voice Recognition and AI in Marketing
Voice recognition changes how consumers interact with brands. Siri or Alexa return the best result for your voice command or query. How that result is determined is often outside the control of marketers.
Consider this small, but illustrative, example. Last November, Amazon ran a promotion to give users more practice placing an order with Alexa. I received a discount by ordering my George Foreman grill directly through Alexa, instead of on Amazon’s website. Amazon profits from the sale either way, but a host of marketers missed out on the ability to influence my purchase decision through promoted products or reviews.
It’s unlikely that consumers will stop performing extensive research online before they purchase products. Marketers will still have the opportunity to develop relationships with consumers and target them with relevant messages.
But marketers may need to build brand awareness much earlier in the purchase process as the use of voice recognition increases, so that when consumers say “Hey Alexa,” your product comes to mind first.
The way consumers search may change, too, according to Campaign:
We unconsciously change our behavior when using voice search. When you are searching for a restaurant on your desktop or phone, you might type in "Best Brunch in Los Angeles." But when you use voice search you change your behavior and ask a question, like "What restaurant has the best brunch in Los Angeles?" or "What restaurants are open for Brunch Now?"
As a result, voice search queries are longer than their text counterparts – they tend to be three-to-five keywords in length, and they tend to explicitly ask a question, characterized by words like who, how, what, where, why and when, with the expectation that the search engines will provide an answer back.
As Chief Content Officer, Mike Kaput uses content marketing, marketing strategy, and marketing technology to grow and scale traffic, leads, and revenue for Marketing AI Institute. An avid writer, Mike has published hundreds of articles on how to use AI in marketing to increase revenue and reduce costs. Mike is the co-author of Marketing Artificial Intelligence: AI, Marketing and the Future of Business (Matt Holt Books, 2022). He is also the author of Bitcoin in Plain English, a beginner’s guide to the world’s most popular cryptocurrency.