Geo 101: What Are Neural Networks?
Artificial neural networks are already giving marketers more advanced tools for predicting consumer behavior and understanding sophisticated audience segments.
With the goal of breaking down some of the most important concepts to provide a better understanding of the basics — and a jumping off point for exploring how far technology may take us — we introduce the next installment of our GeoMarketing 101 series: understanding artificial neural networks.
What Are Neural Networks?
An artificial neural network refers to a computer system modeled on the human brain and nervous system. How? A collection of units or “nodes” to form a system that “learns” over time by considering a large number of samples. For example, in image recognition, a neural network might learn to identify images that contain a person by analyzing example images that are labeled “person” or “no person” — and then using these results to identify people in other images. This is about repetition and comparison rather than knowing what a human being looks like, but the system does get “smarter” at recognition over time.
This makes neural networks similar to machine learning in that the goal is to use technology to parse large amounts of data/specific kinds of data more easily and more intelligently.
But these two means do differ. As Zendesk’s Brett Grossfeld explains in a blog post, “machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned.” On the other hand, “deep learning structures algorithms in layers to create an artificial neural network that can learn and make intelligent decisions on its own.”
Why Do Neural Networks Matter To Marketers?
Essentially, they matter because they’re backing advancements in artificial intelligence, and AI is already reshaping the marketing industry: Marketers are always seeking better insights, and the way that artificial neural networks can learn — improving data analysis, image recognition capabilities, and more — is something that brands should be paying attention to today. Artificial neural networks are already giving marketers more advanced tools for predicting consumer behavior and understanding more sophisticated audience segments.
Secondly, while neural networks are already seen as imperative for facial recognition, self-driving cars, and more, they’ve now been applied to speech recognition. (For a highly technical at how neural networks are used in speech recognition, check out this blog post from software engineer and machine learning specialist Andrew Gibiansky.)
As we’ve written previously, whether they’ve been thinking about it or not, neural networks, AI, and natural language processing affect marketers’ business: first, because of the ability of voice-based communication to engender relationships, and second, because of the growing import of understanding the intent behind users’ queries when it comes to search marketing.
And while understanding queries has always mattered to search marketers, the new world of search — in which customers look for structured answers to their queries in featured snippets or from a voice assistant, rather than clicking on a series of blue links — prizes a greater understanding of intent. This means that marketers need to get smarter about intent, and advancements in the sophistication of neural networks could become a compelling way to do this.