Understand AI Neural Networks for Marketing Mastery

AI Neural Networks for Marketing Mastery

When you understand neural networks, you give yourself the option to remain competitive with other businesses that are using them to market their products and services. In this blog, we have put together some important and interesting information on how neural networks are implemented in marketing, why people are using them for this purpose, and what that will look like for the future of ads and content marketing. 

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Neural Networks vs. Traditional ML Models

Most AI ran on traditional machine learning (ML) models, which are pretty good at helping marketers with certain types of data and, therefore, to find leads or customers. It could also create new content using linear predictive models. The downside to this, though, is that MLs are not very creative. The upside? They are the less expensive option. So, if as a marketer you’re looking for information–like how to detect spam or what price something will be in the future–this isn’t a bad model to use. 

However, neural networks are the harder-hitting model. Neural networks are like the human brain, which is likely how it got its namesake. It uses a neuron-type system with lots of layers to process the information it is given from lots of different angles. It will take all this data and keep learning, too. By continuously learning, it can pick out subtler patterns and some hidden gems in the data without being specifically programmed to do so. The great thing about neural networks is their ability to handle data that isn’t as structured and is a little more messy, like video, for instance. Take a look at the differences between neural networks and MLs below. 

Main Differences:

  • Structure: A traditional model has pre-made rules inputted, and it follows them step-by-step. A neural network learns as it goes. 
  • Data: MLs are great with structured data, and sometimes that is all you need. A neural network can handle that and more complex data. 
  • Complexity: MLs do well when given a specific task, and neural networks are great at complicated problem-solving and finding unique patterns.
  • Interpretability: It is easy to understand why MLs come to the conclusions they do when one looks at the data they are trying to analyze, but neural networks are much like a black box; understanding their decision-making process is much harder.
  • Prediction: Neural networks can see all elements of an ad including color and images, so it can make better predictions than ML alone. 

Practical Ways Neural Networks Can Be Implemented

One way that marketers understand the customers coming to their websites and buying their products or services is by using segmentation. They take certain groups of people who all have certain things in common in order to target them with ads that might appeal to them as a group. Neural networks can help with this process by making those groups even more niche; therefore, you’re not losing some of the customers on the edges who aren’t quite interested in what you have to offer. The AI does this by watching behavior on the website. 

You can connect with people so much better when you talk to them as an individual, so as you can imagine static ads and mass messaging will become an outdated tactic. There is more than just this though, neural networks can also:

  • Predict future behavior: You can use past data to guess what trends you might see next in your industry and make better sales. Or predict what people might be looking for in your services over the next few months. 
  • Optimize campaigns: When your content is live, it can be changed on the go, depending on how people are reacting to the ad or visuals. 

Neural Networks are Branching Out

Creating content is a big task for businesses online; it’s one of those time-consuming things that everyone has to do to stay relevant or gain leads. Neural networks can actually help with this in a couple of different ways, like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Below, we explain what these two are and what that means. 

Variational Autoencoders vs. Generative Adversarial Networks

Variational autoencoders will condense data, so when you feed it the data you have on your customers, it can clean it up and put it into something called latent space. This space will take only the important details and get rid of the bits it doesn’t need. Once it’s managed that, it will explore that latent space to take some samples of the data, which allows it to create new data points. These new data points will feel familiar because they share the same features as the original points, but it will also be completely new ideas. That’s how you can get ad copy or product descriptions that feel like copy you’ve seen before but are new. 

GANs, on the other hand, are like two talking heads. One side of the AI will create an image, video, or whatever you need it to. The other side probes and tries to see if it can tell if the content is AI. They go back and forth like this and then spit out something that should feel incredibly human. Of course, AI isn’t human, so it isn’t perfect, but it’s a start.  

Real-Life Case Uses for Neural Networks

Netflix Personalization Engine

We discussed personalization with neural networks a bit above, and Netflix definitely takes advantage of it. You can tell by the spookily accurate predictions about which shows you will actually like. They have it down to such a science that they’ll even put the likelihood as a percentage. In the past, if you were a 20-year-old female, they might just recommend rom-coms because it would be more likely you would want to watch them than a 40-year-old male. But we all know that’s not really how life works. Everyone is different. With neural networks, they can grab the attention of a viewer with actual movies and shows they want to watch. This obviously maximizes their ROI. 

Spotify’s Discover Weekly Playlist

There is this cool feature on Spotify that’s called the Discover Weekly Playlist. This playlist will feed you songs once a week that it thinks you will really enjoy. It does this by looking at the past data of what you have listened to by analyzing things like the tempo of the music or the genre. This keeps you using the Spotify app as you find new music much easier and, therefore, want to listen for longer. 

AI Neural Networks Are the Future of Marketing

Marketers will start to use Neural Networks more and more in the AI they use for marketing. It has so many benefits that if you don’t jump on board, you might be left behind. However, you need an expert on your team because the ethical concerns of AI, as well as other nuances, can land you in hot water if you don’t know what you’re doing. 

If you need assistance with custom content that ranks and gets clicks, improves your brand’s exposure online, increases quality traffic to your site, and converts visitors into customers, contact ClickGiant today. We are a leading digital marketing agency serving clients nationwide. 

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