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Image showing several different sized balls, some separated, some connected. Neural networks ultimate guide.

Neural Networks: Ultimate Guide

TJ Kiely

Sep 26, 2023

As machine learning advances, so do its many capabilities. One area of interest for businesses is the use case for neural networks. This subset of machine learning takes on a brain-like form to connect dots between data and learn more than what’s on the surface.

Neural networks support a slice of the artificial intelligence pie called deep learning. It’s what powers some of the technologies we use every day, such as voice assistants on smartphones and Google’s automatic translator. The idea of neural networks in computer science dates back more than 70 years. But the use of neural nets has really taken off in the last 10 years or so as machine learning is applied to various areas of our everyday lives.

Here's what you should know about neural networks and how you can apply them to critical business use cases.

Table of Contents

Brain graphic with networks.

What Are Neural Networks?

Neural networks are a type of artificial intelligence (AI) that is modeled on the human brain. These brain-like networks are composed of multi-layered interconnected neurons, or nodes. The nodes are connected by synapses, which transmit information from one neuron to another. Neural networks can learn to recognize patterns of input data and can make predictions based on these patterns.

Companies use neural networks for a variety of tasks, including image recognition, identification of spoken words, and prediction of financial markets. Neural networks have been shown to be particularly effective at these tasks because they can learn to recognize patterns that are too complex for humans to discern.

How Are Neural Networks Useful?

Neural networks have been found to be particularly useful for tasks such as image recognition and classification, data mining, and pattern recognition. In recent years, neural networks have also been used for more complex tasks such as natural language processing and machine translation.

For marketing purposes, neural networks can add context and clarity to the customer journey. Because neural networks look for hidden connections and patterns that would otherwise be harder to detect, marketers can get richer insights into their customers’ experiences.

For example, neural nets may be able to pinpoint niche behaviors about a particular area of the customer journey, such as how users engage with content or make a purchase.

Knowing how customers move through their digital journeys can help brands cut the clutter and create a more direct route to success. You can eliminate stumbling blocks that might prevent customers from engaging or purchasing, such as slow page-load times or complex navigation.

Another opportunity for using neural networks is to predict customer churn. Churn is a costly problem that makes brands rely more heavily on new business. However, if you can predict churn, you may be able to take the right steps to keep those customers longer.

Using neural networks, you can find answers to questions like:

  • Why do customers churn?
  • What steps do customers take before they churn?
  • When does the churn process really start?
  • How long does the churn process last?

Neural networks look for patterns between clusters of data to draw richer insights. By looking at lots of customer journeys from a bird’s eye view, you may be able to identify at-risk customers and see what behaviors trigger certain outcomes, like churn.

Using neural networks in marketing is not a substitution for your conventional ways of mapping the customer journey. Rather, machine learning complements it by making your current models clearer and more accurate.

Computers can more quickly and accurately look at empirical customer data to find common denominators between actions and behaviors. They can be sorted into segments or groups based on these findings.

You’ll have information about your customers that you didn’t have before and had no real way of knowing. Use that intel to strengthen your positioning, provide better experiences, and shorten the path to conversions.

Five pathways.

What Are Types of Neural Networks?

Deep neural networks support a wide range of use cases; therefore, they can take many forms. Here are a few examples of neural networks and how they work:

1. Artificial Neural Networks

An artificial neural network (ANN) is a network of simple elements called neurons. These neurons receive input, change their internal state (activation) according to that input layer, and produce output depending on the input and activation. Neural networks are similar to the human brain in that they are comprised of a large number of interconnected processing nodes.

Artificial neural networks are used to model complex patterns in data. They are commonly used in fields such as computer vision and speech recognition. Neural networks are also well suited to modeling non-linear relationships.

There are many different types of ANNs, which we outline here.

2. Convolutional Neural Networks (CNN)

Convolutional neural networks (CNNs) are a type of neural network that is specifically designed to work with data that has a grid-like layout, such as images. CNNs rely on an activation function that defines the output based on the set of input data.

CNNs are well suited to image recognition tasks because they can learn to detect patterns of pixels in an image that indicate the presence of an object. For example, a CNN might learn to detect the pattern of pixels that form a human face.

CNNs are typically composed of three types of layers:

  1. Convolutional layers
  2. Pooling layers (or hidden layers)
  3. Fully connected layers

Convolutional layers are responsible for learning the patterns of pixels in an image that indicate the presence of an object. Pooling layers are used to reduce the size of an input image so that it can be processed more efficiently by the CNN.

3. Recurrent Neural Networks (RNN)

Recurrent neural networks are a type of artificial neural network that is used to model time-varying data. Recurrent neural networks are similar to traditional artificial neural networks, but they have an additional layer that allows them to retain information about previous inputs.

This type of deep learning makes recurrent neural networks well suited to tasks such as speech recognition and natural language processing.

4. Feedforward Neural Networks

Feed-forward neural networks (also called multi-layer perceptrons, or MLPs) are a type of artificial neural network in which information travels in only one direction, from the input layer to the output layer. There are no feedback loops, meaning that information does not travel backward through the network. This makes feedforward neural networks much simpler to design and train than other types of neural networks.

Companies often apply feedforward neural networks to tasks like classification and prediction. They can be trained using a variety of different algorithms, including backpropagation.

5. Long Short-Term Memory Networks (LSTM)

Long short-term memory neural networks are made up of a number of connected neurons. They process information by passing it through the layers of neurons. The first layer encodes the input, while the second layer stores it in memory. The third layer retrieves the information from memory and processes it.

These deep neural networks are much more effective than traditional artificial intelligence systems. They can handle complex tasks such as machine translation and natural language processing. Additionally, they can learn from experience and improve over time.

Long short-term memory neural networks could be the key to unlocking better artificial intelligence. These networks mimic the way the human brain processes and stores information. By understanding how they work, researchers can create more efficient and powerful machines.

Typing on a laptop.

Neural Network Use Cases for Business

The opportunities to apply neural networks in business are virtually endless. We’ve seen them at work in the medical and pharmaceutical industries to pinpoint diseases and treatment options. We’ve also seen them used in the retail industry to forecast product demands and ensure adequate inventory.

From a marketing standpoint, neural networks hold a lot of value. Here are some real-world examples of how companies can use them to optimize their marketing:

Neural Networks for Better Search Engine Optimization

Several years ago, Google announced it aimed to improve its search engine functionality. The path forward was a neural network that went 30 layers deep.

Google’s goal has always been to return the most relevant search results based on user queries. It optimized its neural network to handle more complex searches, such as processing colors and shapes in images.

Companies can take a page from Google’s playbook by using neural networks to improve their own SEO. By learning what customers are searching for online and seeing what your competitors are doing, you can better optimize your own content to meet their needs.

Neural Networks for Higher Conversion Rates

Many consumers feel ad fatigue. We see so many ads (as many as 4,000-10,000 per day!) that our brain chooses to ignore most of them. That’s a huge waste of money for companies pouring their marketing budgets into ads that will never be seen or heard.

Neural nets may help to combat ad fatigue and wasteful spending. Neural intelligence can help you identify only the customers who might be interested in what you offer. For example, it can improve your marketing timing so you can connect with customers who are actively searching for what you offer.

You can also tailor your content to speak to specific stages of the customer journey and build stronger connections with customers based on where they are in the decision-making process.

By limiting your marketing to only those who are most likely to buy, you can avoid frivolous ad spend and contributing to ad fatigue. Your brand image might also get a subtle boost because you’re not subjecting uninterested consumers to a deluge of unwanted emails or ads.

Neural Networks for Improved Personalized Recommendations

Healthcare companies use neural networks to create personalized treatment plans for their patients. They predict how patients may respond to treatments based on their genetics. The same idea can be applied to business marketing strategies.

By connecting more dots between your customers, you can get a better idea of what else your customers might be interested in based on their purchases. You can present targeted offers and product recommendations to them that they’re most likely to respond to.

How Meltwater Helps You Leverage Neural Networks and Deep Learning

Neural network AI technology is all around us, and you can take advantage of ready-made platforms like Meltwater to put this power into your hands. Meltwater is a data-driven platform that helps you make better use of your data. We collect data from a range of public online and offline sources, including social media, blogs, news outlets, podcasts, and more, then transform information into insights and actions to improve your business.

Get a demo of the Meltwater platform and take the guesswork out of connecting your data for better insights! Level up your data analytics capabilities today: