For many years now, email has played a major role in marketing. Every company strives to make its email campaigns more effective and engaging in order to attract more customers and increase sales. However, given the very fierce competition that dominates many areas of business, this is becoming harder and harder to do.

To boost the effectiveness of email campaigns, many marketing teams are turning to neural networks. One way to use them is to predict subscriber behavior. And in the near future, this approach will very likely become the standard one. AI can analyze large volumes of data in different languages and identify patterns that help predict which customers are likely to open emails, which topics and interests, which links they're more likely to click, and which factors boost conversion into purchases.

AI is also useful for analyzing and selecting the optimal send time. A model can show which days of the week and what time work best for sending emails in order to get the maximum response from the audience.

In this article, we'll look in general terms at how neural networks, in their current form, can significantly improve the effectiveness and quality of email marketing campaigns. We'll also see how they work, and answer the question of how they can practically help a business attract more customers.

How neural networks work

A neural network is a special type of machine-learning algorithm that mimics the way the human brain works to solve a wide range of tasks. Let's cover the key aspects of how it functions and is applied.

A neural network usually consists of three types of layers: input, hidden, and output. The input layer receives the initial data set, which passes through one or more hidden layers, where each neuron processes the incoming information and passes it on. The process finishes at the output layer, where the system produces the final result or prediction.

Each neuron in a trained system is connected to others and is characterized by its own weight and threshold value. It receives input data, on which it performs certain transformations. If the output signal of a given neuron exceeds a set threshold, it activates and then passes the information further along.

  • Input layer. The first layer in a typical neural network is called the input layer. Once its neurons start receiving information, they process it using a mathematical function (an activation function) and pass the information to the next layer based on a comparison with a preset threshold. There's usually only one input layer, which pre-processes text, photos, graphics, audio, video, and other data types to obtain their numerical representation. It then passes this numerical representation as information to each neuron. In the next step, the output signal is scaled according to preset "weights".
  • Hidden layer. Now, at the second stage, the data is processed by the hidden layer. The neurons in it receive data either from the input layer or from neurons in the previous hidden layer. Each neuron then passes the data sequentially to another nonlinear activation function and sends the information further along.
  • Output layer. A neural network has only one output layer, which marks the logical completion of the process. Its neurons receive information from the previous layers. They then process it through new activation functions and produce the desired result. Depending on the type of technology, this result can simply be used as final, or it can be fed back as new data into the same neural network (feedback) or into another one (cascade) to uncover additional relationships, metrics, and information.

The training process

Training is one of the main stages of working with AI. This is where all parts of the system are tuned to learn how to solve a specific task. During training, based on the selected data during a test run, the network adjusts the weights of its neurons in order to minimize errors in the results.

Thanks to their ability to learn by example and adapt to new tasks and goals, the use of neural networks is today one of the leading trends across many areas of life, suitable for businesses of very different kinds. Through effective training and application, the system can significantly improve the accuracy and efficiency of forecasting in just a few minutes, making it an indispensable tool in today's world of large and complex data.

Applying neural networks in marketing

Neural networks make it possible to solve a wide range of tasks and problems in marketing.

  • Advanced customer-base segmentation. AI helps users find and uncover hidden patterns and create far more precise segments and samples from the entire audience. This helps companies that need to improve the targeting and personalization of their marketing campaigns.
  • Automation. AI is used to automate routine marketing tasks, such as sending personalized messages, managing ad campaigns, and processing and analyzing the effectiveness of marketing decisions. This is especially useful and makes sense when employees want to spend more time on creative tasks rather than routine work.
  • Content creation. Neural networks can be used to create content such as text, images, or video, which is a huge help in generating and publishing large volumes of textual and visual material for an audience. This is especially useful when you need to quickly write a lot — both complex material for professionals and content that's "explained simply".
  • Sales forecasting. AI can be used to estimate and forecast sales volumes, identify market trends, and optimize sales strategies, helping companies make well-considered strategic decisions for business growth and thereby increase revenue.
  • Predicting audience actions. Artificial intelligence is used to analyze information and predict consumer behavior, helping companies better understand their audience and make targeted marketing decisions. AI can also be used to analyze the behavior of email newsletter recipients. Neural networks can track email opens, link clicks, time spent on a site after clicking through from an email, and other campaign performance metrics. Based on this information, they can draw attention to the aspects that need work and can be improved to optimize content and increase its effectiveness.

In this way, the use of neural networks in email marketing not only improves content personalization and campaign optimization, but also helps companies better understand their audience, predict its behavior, and automate processes to achieve better results.

Predicting events with AI

An important aspect of using neural networks in email marketing is the ability to predict audience behavior. Machine-learning algorithms can analyze big data and run checks to predict which customers are likely to churn and stop reading newsletters, which products will interest them, or which actions on the site will lead to a purchase. This lets companies see the full picture and take targeted action to retain customers and increase sales.

Predicting subscriber behavior

To predict subscriber behavior using neural networks, you can use a method that trains the network on time series. This requires using information about subscriber activity over a given period of time, such as email opens, link clicks, average time spent on the site, and other metrics.

One way to solve this problem is to use recurrent neural networks, which are specifically designed to analyze sequential data with high efficiency. The neural network needs to learn from time-series data on subscriber behavior and can then be used to predict their future actions.

This requires building a neural network model in which the input layer represents subscriber behavior data, while the subsequent layers perform the calculations needed for forecasting. Using training data, such a neural network will immediately tune itself based on prior results stored in memory in order to predict future behavior.

Conclusion

Companies looking to improve their marketing for the long term should consider trying out neural networks. Over the past few years, this technology has made significant progress, and over the next two we can expect even more changes, as the pace of the technology's evolution keeps accelerating.

Artificial intelligence will help predict subscriber behavior, allowing businesses to effectively analyze information, predict future audience actions, and make decisions to increase the effectiveness of marketing campaigns and newsletters.