A Data-driven Research: How Businesses Can Grow Using Neural Network Advantages

However, the question is whether other industries, except for IT, can benefit from neural networks. In this article, we are going to tell you about the application of artificial neural network technology in different spheres of activity.

So, what is a neural network? Another name for this technology is artificial neural network (ANN). It was called so because the principles powering this technology were based on the work of neurons in human brain. Our neurons create transient states serving as a basis for making unique decisions that are a part of what we call creativity.

You see, machines cannot think the way humans do. It's okay for them to create an accurate weather forecast or solve a differential equation but they will not tell you the difference between a movie and theater.

For that reason, artificial neural networks were created as the potential solution to this problem. Being based on the physical work of neurons in brain, they have a certain structure allowing them to learn by means of input information analysis and, respectively, give an output result.

Analyzing a big scope of information by artificial neural network is quite a complicated process. That's because there are typically over thousands of neurons in a network that, in turn, have different layers and many other technical and structural peculiarities.

In any case, the neural network architecture allows machines to gain understanding of what they are faced with. So, the longer they learn the better the final result is.

Now, you have an image of how neural networks work. So, let's finally consider the artificial neural network application in different spheres of activity.

Let's see what capabilities of this technology made it popular in such spheres as marketing and finance. 

Talking about neural networks in marketing, it is often used in order to build an efficient target marketing. There can be referred market segmentation which is the division of customers according to their consumer behavior etc.

In other words, target marketing implies the focus on those customers whose needs and desires are most likely to correspond with what a product or service actually offers. So, that group of customers are potential buyers and, roughly saying, the more efficient a target marketing is, the more profit a company is able to generate.

Different types of artificial neural network fit pretty well for framing your company's marketing. Since they are able to conduct a segmentation by multiple characteristics including purchasing patterns, economic status, and much more.

Unsupervised networks take similarity of customers and also their characteristics as a basis for being able to segment them, while supervised ones can be taught to see the difference between segments of customers. That can be achieved through training, so set clear goals before explaining your idea to the team of developers.

Generally, neural network marketing can save your company's time and money since they will help you to make sure that you contact the right customers. Studies show that the rates of response are increasing up to 95% after selecting customers for marketing advertisements.

The ability of neural networks to consider several variables at the same time makes them the perfect choice for the sphere of retail and sales. This way, the forecasts that are made by means of artificial neural networks surpass ones made by experts or using traditional techniques of statistics.

Another way to benefit from neural networks in this sphere is to apply them for the analysis of a shopping cart. In such a way, you will be able to get the information concerning which kinds of product are repeatedly bought together, as well as see the delay between purchasing several products.

This information will come in handy since the knowledge of your customers' purchasing behavior allows you to better optimize processes in your business. Let's say if you have an e-commerce website, then based on this information you could implement feature showing the customers what other people bought together with these type of goods. Without any doubts, neural networks in business will help you to generate more sales.

Also, using that information, retailers can decide on the layout of a store. It means that if the analysis has shown that your customers associate one product with another, you may use that information to your favor and place them near each other.

With neural networks, you are also able to analyze time dependencies between purchases. Let's say the customer bought a water purifier and 4 months later came back to purchase a new filter for it. You can trace that and call your customer back every 4 months offering to purchase a new filter. This way, you decrease the probability that your client will purchase this supply from competitors. So, the advantages of artificial neural network in this sphere are obvious.

Let's move to other spheres where neural networks are popular - finance and insurance.

The main purpose for using artificial neural networks in the sphere of finance and banking is their capability of forecasting. Analyzing the big scope of appropriate information, neural networks are able to make predictions. Moreover, the more information that was analyzed, the more accurate the prediction will be. Forecasts are extremely important for this sphere of activity.

Neural networks in business forecasting serve as a powerful tool for predicting exchange and stock rates and many others. However, that's not the only area where artificial neural networks have found an application within this sphere.

Banks are used to give loans in accordance with their statistical data about a person. Generally, the software is driven by statistical techniques but things have changed. Nowadays all statistical data is calculated by artificial neural networks and based on the result the final decision is made.

The neural networks in finance and investing are quite widespread. A company not only has fewer expenses but gets a system that is able to learn and improve it's identification of credit risks.

The insurance industry has got absolutely new 'superpowers' for tracing insured and non-insured events. For example, some insurance companies already use GPS tracking in order to find the client's car in case of a carjacking. Also, they keep an eye on speeding and that, in turn, may affect the insurance rate.

Artificial neural networks have also found their place in this industry. They are used to segment policyholders into groups that help companies to find out an appropriate pricing.

Another application of artificial neural networks is related to fraud detection since they are able to be taught to detect fraudulent claims, as well as strange circumstances.

Also, neural networks can be useful when it comes to the retention of customers. Since the competition in this industry is tough, every customer is important to a company. With the help of neural networks, insurance companies are able to detect the reason why the customer left by means of analyzing his or her history. Also, they can be applied for managing special offerings for certain groups of customers to motivate them to stay.

It seems like the time for our last couples of spheres. They are telecommunications and operations management.

This technology in the sphere of telecommunications is able to cover quite a big range of areas. Their forecasting capabilities are used to conduct analysis of customers, as well as call data. That's needed to predict the reasons and moment when customers move to another company.

Also, the forecasting capabilities of artificial neural networks are used for defining the efficiency of upcoming promotional campaigns and searching for the clients who are likely to bring the most profit.

Except for these cases, they are also used for real-time analysis of network traffic in order to optimize routing and increase the service quality and much more.

The sphere of manufacturing has been greatly automatized for the last couples of decades. With the development of the IT sphere, employees working in factories have got mobile application development service intended to increase their efficiency, while the machinery got an artificial intelligence.

Artificial neural networks are successfully used for different types of scheduling. Manufacturers have started using neural networks for scheduling their machinery, lines of assembly and so on. The technology is also used to solve other problems connected with scheduling. For instance, to establish a timetable, schedule of a project or the schedule of multiprocessors.

Another artificial network example is their usage in manufacturing processes which sometimes covers a wide range of activities, including the control and scheduling of the whole floor's work or even improving the system of manufacturing itself.

Nowadays, mainly all manufacturers use robotic tools to control the quality of their products. For example, some of them use cameras to identify defective products. The artificial neural network behind this camera is able to learn over time, so the number of unnoticed defective products will be reduced in the future.

So, artificial neural networks are capable of being used not only in the IT industry but in other fields of activity as well.

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