The development of mankind has led us to the next level of self-awareness – the creation of artificial intelligence. Intelligent machines are already changing the approach to many problems and tasks, they look with people for answers to important questions. How does this technology work? Based on the knowledge that is in the algorithm, full-stack in marketing AI solves the set tasks: from mathematical calculations to the development of new medicines. AI can solve any problem if you train it with the necessary algorithm. If you set marketing information in the system, the machine, for instance, will take on the laborious process of analyzing customers and their behavior or will find a way to attract online customers. How relevant is the use of AI in the field of marketing, we decided to find out in this article.
Neural Network Development
Almost all existing systems today operate within the framework of predefined algorithms. AI fulfills its tasks and acquires new skills through deep machine learning. In the usual systems, the information should be downloaded in advance, while machine learning is characterized by the fact that there are specific algorithms that make the system develop independently based on previously downloaded information. You can hire full stack developer to help you with the development of customized AI programs for your project or company.
The history of the development of neural networks began in 1950, but at that time there were no technological possibilities for implementing this concept. The computing power of the present allows you to process a large amount of data, which gave a new, already practical impetus to the development of neural networks.
Computer neural networks are communications of “electronic neurons” that are capable of processing and classifying information. But in order to train machines and create the necessary algorithms, specialists are needed, the lack of which is currently very noticeable. However, McKinsey predicts job growth of 250,000 more than candidates like full stack developer and machine learning engineer shortly. The situation requires to be changed!
If we talk about the type of developers for this activity, we note that full stack developer skills include Python knowledge as the most of the work on Machine Learning is done in Python, but knowledge of any language will speed up learning.
Python can be used to implement AI because of the simple and seamless structure. The Python syntax makes it easy to implement various AI algorithms, which also reduces development time when compared with other available programming languages. Using Python allows users to create neural networks with a set of useful libraries that can be used to develop AI. Other features include the ability to test algorithms without having to implement them.
Java full stack developer can also be considered a good choice for AI programming, as Java provides search algorithms and neural networks. Pretty useful language for AI as it offers scalability, debugging and graphical representation.
Neural Network in Marketing
A business strategy in marketing will always work for a human, but AI can solve routine algorithmic actions.
Marketing was originally built around the product. Customer-orientation still involves a product itself. The question that AI helps solve is how to make the product better for customers.
Most of the data that reveals important customer information exists in an unstructured form: images, data in the local language and video, so it is impossible to process it mechanically, and therefore it remains untouched in most companies. Using AI algorithms, you can analyze all types of data, integrate analysis tools into daily marketing processes to make communications more targeted, relevant and effective.
Thus, for example, The Grid service helps to develop sites in a short time without developers, based only on the downloaded data: text, images. Someone said, “What about content”? Well, the Wordsmith service creates news, product descriptions and other plain text using neural networks. Mechanical tasks can be safely transferred AI, and leave yourself a creative component.
How NNs Can Learn to Predict Customers’ Behavior
Do all these facts and cases suggest the idea: “How limitless are the possibilities of AI in marketing”? Here is an example: to create a program for analyzing behavior, the machine learning algorithm works with a list of customers, their social networks, purchase histories, and even the time of purchase. All this will help the system to create a clear portrait of the buyer and thereby predict his behavior when shopping.
The machine learning model examines examples and develops a statistical relationship between lifestyle and customer habits. After that, when you provide the algorithm with the data of a new client, it classifies it based on the templates that he outlined from examples in advance.
As a rule, the more data you provide, the more accurate the machine learning algorithm becomes when performing its tasks. For a person to come to a practical conclusion, the system needs to analyze a whole bunch of parameters and if you enter them manually, it will take more than one year. And a system based on deep machine learning will be able to recognize something, even if there were no exactly similar examples before.
In marketing analysis, AI should consider three main elements:
- Common identifier. Modern consumers use a huge number of devices, so digital identity today is extremely fragmented. This often leads to misunderstanding. To avoid this, it is better to set a common identifier at different data sets. These steps will help create a unified vision for the client.
- Data collection. AI can give meaning to all your data and extract insights from them, but only if you can collect them before activation. Choose a data management platform that will collect data from different sources and allow you to organize, segment and use it in real-time marketing.
- Data endpoints. Best customer experience is common to all points of contact. Based on common identifiers and collected data, the algorithm can create a single experience on several platforms.
Work on neural networks is far from being finished. Various efforts are made to improve deep learning algorithms, and accordingly, there will be even more opportunities for the implementation of AI in marketing. Algorithms are already able to predict customer behavior and based on this to create a product, compose mailings and take into account new and old problems. The development of neural networks creates a large number of new jobs, developers only have to keep up with the times and innovations.
Anastasia Stefanuk is a passionate writer and Information Technology enthusiast. She works as a Content Manager at Mobilunity, a provider of dedicated development teams around the globe. Anastasia keeps abreast of the latest news in all areas of technology, Agile project management, and software product growth hacking, at the same time sharing her experience online to help tech startups and companies to be up-to-date.
