Artificial Neural Networks in Marketing

As much as we like to think that the evolution of artificial intelligence into sentient, self-thinking entities will forever be confined to the pages of an Isaac Asimov novel, that reality is far closer than we think.

Artificial Neural Networks (ANN), which are in essence, computing systems modeled on our very own biological neurological systems, have made the concept of a self-thinking AI entity a reality, or a close approximation of it, rather.

Now, by saying that neural networks emulate our own neurological process, we don’t mean to evoke images of walking, talking humanoid robots that could be planning to take over our planet. That is, thankfully, still contained within the wide, sometimes terrifying, world of science fiction. For now.

What we mean is that artificial neural networks are progressively learning systems that continuously improve their function over time. The network is made of hundreds, thousands or even millions of neurons called units arranged in three interconnected layers:

• Input units, which receive information and data from an external source that the network needs to process or learn about.
• Output units, which produce a response to the information processed or learned by the network.
• Hidden units, which sit between the input and output units and form the bulk of the network that processes or learns the tasks it’s supposed to perform.

How Neural Networks Are Used in Marketing

ANNs are used across industries — in medicine, engineering, finance, and others. They are also transforming the available set of marketing technology resources, giving marketers new, more efficient and more dynamic tools for:

• Predicting consumer behavior
• Creating and understanding more sophisticated buyer segments
• Marketing automation
• Content creation
• Sales forecasting

The most widely used application of artificial neural networks is in the field of predictive analytics. In this case, the neural networks can help marketers make predictions about the outcome of a campaign by recognizing the trends from previous marketing campaigns. While neural networks have been around for decades, it is the more recent emergence of Big Data that has made this technology incredibly useful for marketing.

With a virtual sea of data to input into a neural network, it’s now possible to achieve sophisticated, accurate predictions that can help CMOs make smarter decisions about what actions to take and what channels to allocate more resources to.

Likewise, with market segmentation, sales forecasting, and content creation and distribution, the neural networks, fed with enough data, are able to provide more precise insights and predictions, helping marketing decision-makers better gauge expectations. This technology is also allowing for a more dynamic level of automation, which isn’t only evolving the marketing workflow but is creating an even more seamless experience for the consumer.

What is Predictive behaviour Modeling?

Simply speaking predictive behavior modeling is an area of predictive analytics that tries to predict or model the future behavior of people — in particular customers. The prediction of future behavior can also be formulated as a classification problem.

For example, if there are four possible behaviors or actions a customer can perform, the predictive behavior model, which is a deep neural network, assigns each of these actions a probability score.

Predictive behavior modeling is the science of building algorithmic models and training them based on historical customer data to predict the future behavior of these customers. In other words, to predict the likelihood a customer will take in a particular action.

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In the field of predictive analysis, predictive behavior modeling goes beyond passive customer analysis.

Rather than trying to make well-founded assumptions based on the analysis of historical data that is normally done by humans, predictive behavior modeling allows companies to make decisions based on future predictions made by algorithmic models. And as you might already suspect, these algorithmic models are deep neural network models.

Before we delve deeper into the practical implementation of predictive behavior modeling, we should first take a look at some concrete use cases.

Predicting Customer Churn

One important application of predictive behavior modeling is in the area of customer churn. Customer churn occurs when customers or subscribers stop doing business with a company or service and leave.

Given a large amount of customer data like demographic data, customer purchase history, service usage, billing data, etc. a neural network that is trained on this data can perform a classification of customers into various categories of risk in terms of future churn.

More, precisely we would train the model on customer data from the past. The data would contain information on customers who stopped doing business with the company, as well as information on customers who are still doing business.

The network trained on this data would be able to classify a brand-new customer into one of the categories of risk for future churn. Also known as customer attrition, customer churn is a critical metric because it is much less expensive to retain existing customers than it is to acquire new customers.

As you can imagine, it is critical for a business to predict a potential churn. After segmenting and identifying the customers most likely to leave, the company can take the necessary steps (such as marketing or incentives) to convince them to stay customers. In addition, customers will feel more relevant because the company is communicating with them, resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals.

Use Case 2: Predicting the Outcome of a Marketing Campaign

Another use case of customer behavior modeling is in the field of marketing. Algorithmic models can be trained on the results of previous marketing campaigns or strategies that targeted a particular group of people.

Some marketing campaigns are more appealing to one group of people, and other campaigns are more appealing to another group. Some people with certain characteristics who are exposed to a particular marketing campaign under certain conditions are more willing to buy or upgrade to a new product or service than others. The trained algorithmic models can predict exactly that.

We would implement a neural network model that can predict which kind of marketing campaigns or actions are more likely to be successful for a particular group of people.

This would be also a classification task. Given the data of a particular customer, we would implement a neural network that performs a classification of this customer into various groups. Each group is associated with the likelihood that the customer in this group will buy a product or service advertised in the marketing campaign.

With this knowledge, less time and money would be spent on people who have no interest in the product or service. Instead, the extra money and time could be spent to attract customers who, according to the neural network’s predictions, are more interested in the product or service advertised in the campaign. And this would result in a better return on investment.

Conclusion

A marketing neural network uses machine learning to make smarter predictions. A well-trained model can provide more precise insights and predictions, helping marketers better gauge consumer expectations. In addition, it allows for a more dynamic level of automation. The network will continually improve its skills, making the entire process more efficient and seamless for both the marketer and the consumer. It will help companies make more informed decisions about their products and services. Creating a neural networks-based predictive model is an important step toward transforming marketing into a machine-learning endeavor.

Neural networks are powerful tools for marketers because they can analyze infinite amounts of data. The model can identify discrete patterns, and it can differentiate audience clusters. It can also accurately predict the probability of conversion for a wide variety of audiences. It is a powerful tool that allows advertisers to measure incrementality, ensuring that money isn’t wasted on the least likely customers or existing customers. While these tools are often difficult to use for marketing purposes, they do have several advantages.

One of its greatest strengths is its ability to analyze unlimited amounts of data. Its inherent classification and pattern recognition capabilities make it a powerful tool for targeting a wide variety of audiences. Using this technology, marketers can accurately predict whether a particular advertisement will result in a specific outcome for a given consumer. And the system can be adjusted to account for changes in the consumer’s behavior over time. By using a marketing neural network, you can be sure your money is going to be spent on the most likely consumers, rather than on the least.

Another major benefit of neural networks is that they are capable of analyzing an unlimited amount of behavioral data. Because of their inherent learning capabilities, neural networks can differentiate audience clusters and identify discrete patterns in data. This allows advertisers to target a broader spectrum of people than ever before. This means that they can accurately calculate the incrementality of their marketing dollars and ensure that they’re not spending money on existing customers or those who are least likely to convert.

A neural network can also segment audiences by their conversion and attention probability. A good marketing neural network can make decisions and adjust media buying strategies in real time. It can even show promotional offers to people who have low conversion potential, while focusing more on high-value potential customers. It can optimize advertisements in real-time, increasing their relevance and chances of success. It is a powerful tool for marketers. If you can use a good marketing neural network, you can easily improve the results of your online advertising.

The benefits of a marketing neural network are many. Unlike traditional models, it can be used to analyze data and make predictions. This technology is a breakthrough in marketing. With it, marketers can target audiences and understand trends and measure performance in real time. This is a great way to make more informed decisions and increase profits. However, it’s not the only benefit of a marketing neural network. It can be applied in the field of business and finance, and is very useful for evaluating trends and forecasts.

NN Group number 12

Dr. S.T. Patil

Mrunal Barve

Rohan Aparajit