Artificial Neural Networks are Improving Marketing Strategies

Hello Geeks 👨‍💻, This Blog help you to get information about Neural Network.

What Are Neural Networks?

Artificial neural networks are an important subset of machine learning. They are what computer scientists use to work on complex tasks, such as making predictions, strategizing and recognizing trends. Unlike other machine learning algorithms, which may organize data or crunch numbers, neural networks learn from experience. Like humans.

Neural networks, as the name suggests, are modeled after the neural networks of the human brain, which are responsible for human decision making. The brain takes in information and then attempts to connect the dots to come up with a conclusion. We don’t always get it right at first, nor do the machine learning algorithms. But through trial and error, we, and likewise the artificial neural networks (ANN), start coming up with better outputs.

How Do Artificial Neural Networks Operate?

Currently, most ANNs are relatively simple when compared to the complex neural interactions that take place when a human mind makes decisions. There is an input layer, an output layer, and a hidden layer sandwiched in between — where there are hundreds of virtual nodes the algorithm connects and reconnects to when trying to reach an outcome.

To ‘learn’ with each input experience, the algorithm will alter the internal connections until it figures out how to achieve a desired output within a specified level of accuracy. Once the algorithm has learned, more inputs can be entered and the ANN provides a workable prediction.

What About Deep Learning?

Deep Learning or DL, refers to a more intensive version of machine learning. Remember the single hidden layer in the artificial neural network? With DL, there are multiple layers.

Not only are deep learning neural networks more complex, but it is here that there exists the hope (and the fear) that the algorithms will take off and begin learning on their own. Where the technology is right now, whether it’s basic machine learning, NN or DL, the algorithms are still dependent on the provision of inputs from external sources, i.e. humans.

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.

Examples of Neural Networks in Action

Microsoft’s BrainMaker-

Microsoft took a set of variables, such as the date of last purchase, the number of products bought and registered, and the number of days between a product release and purchase, and plugged it into BrainMaker to learn which customers were most likely to open their direct mail. They also bought data relevant to their customers, including the number of pieces of employer and income data. By using BrainMaker’s neural network software, Microsoft increased their direct mail response rate from 4.9 percent to 8.2 percent, which translates to, according to company spokesman Jim Minervino, “the same amount of revenue for 35 percent less cost.”

LinkedIn and Bright

LinkedIn purchased Bright in 2014 to integrate its algorithms to create better matches between employers and job candidates. This NN takes variables like past hiring patterns, account location and job descriptions to give each potential match a ‘Bright Score,’ which correlates with how relevant the match may be. This then dictates the potential employee or employer matches users find when using LinkedIn.

Conclusion -

Artificial intelligence applications in addition to hardware applications,the more common use are the software applications. In recent years,there will be more and more relevant applications in the financial, medical fields.Financial and medical fields both have some characteristics: they are equally has uncertain environment, complex factors and a large amount of data. For the analysis of these data also need concept of Fuzzy to present the result. In my opinion, the future of artificial intelligence that the system should be applied on the things in life, such as aviation, transportation, education, etc.

Thank You for Reading Geeks👨‍💻 ….🤓🤓