Introduction
Indeed, Machine Learning is a prime example of this as it offers new performance opportunities to companies and allows them to better understand their customers. Thus, enabling companies to offer their clients the best possible experience.
Thanks to Machine Learning’s powerful analytical abilities, this digital tool allows companies to benefit from a wealth of data collected.
What is Machine Learning?
Machine Learning is a method of data analysis that examines the capabilities of a machine and how it works. It is a branch of artificial intelligence that automates analytical model building.
Machine Learning is the study of computer algorithms. It aims to improve automatically the algorithms by analysing and processing the various available data in a relevant and efficient way. Machine Learning is divided into two main phases: learning and prediction.
Phase 1: The Learning Phase
This consists in letting the machine build its reasoning system without having to impose a program beforehand. For this learning phase, the machine relies on several examples in order to understand the logic of the model it has to integrate.
To train itself, the Machine Learning will focus on the Big Data to be analyzed in order to determine its transformation algorithm. Based on the examples studied, the Machine Learning starts its learning phase in complete autonomy.
Phase 2: The Prediction Phase
After integrating the reasoning and the problem’s algorithm itself, the Machine Learning must be able to determine the purpose of a given situation. The more complete the Machine Learning is, the more accurate the predictions obtained by this tool will be.
Supervised and Unsupervised Machine Learning
Machine Learning is divided into two categories:
Supervised Machine Learning systems:
They require the supervision of a specialist or of a data analyst who is asked to provide the variables that the model should use and analyze to develop forecasts. Once created, an algorithm applies what it has learned to new data.
Unsupervised algorithms:
Based on Deep Learning, they analyze data and deduce results using neural networks that identify correlations between multiple variables among large amounts of data. They use what they learn to process incoming data. Supervised systems are better suited to more complex processing tasks such as image recognition and speech synthesis.
Predictive analytics
Predictive analysis consists of exploiting and processing the data from Big Data using statistical algorithms or Machine Learning techniques, in order to establish probabilities based on the past.
Predictive analytics bring together multiple data sets to help companies decide in which direction to go next and how they can deliver a better customer experience. With the increase in data, computing power, the development of Artificial Intelligence (AI) software and simpler-to-use analytical tools, many companies can now use this type of analysis and integrate it into their strategies.
The Advantages of Machine Learning
Machine Learning can be applied to web marketing and it can be used to:
Collect and analyze data
Data is the basis of all marketing actions. Indeed, it is crucial to correctly know how to collect and use it.
In this field, AI makes it possible to carry out tasks that would be impossible to be processed by a human being, because it would take too much time or because it is imperceptible to the human eye.
AI enables large volumes of data to be taken into account in real time and processed according to predefined rules.
AI is able to identify information such as conversion probability and interest for one product or message over another.
Segment and target your audience
Artificial Intelligence enables you to segment your audiences in real time based on data that has been collected and segments that have been defined. You can therefore target different types of audiences and reach customers and prospects with the right level of personalization.
Personalize your site
Consumers are looking for experiences adapted to their own needs when browsing the Internet. Customization allows you to adapt each element of your site in real time according to the identified type of visitor.
With Artificial Intelligence, you can trigger actions in real time according to the segmentation carried out by the algorithms. This allows you to prioritize your actions and to optimize your marketing budget, since the targeting is much more concise. It is then possible to personalize many elements in real time according to the purchase probability and the visitor’s sensitivity to messages, banners, pop-ups, voucher proposals, etc.
Predict consumer behavior
Machine Learning helps you identify your target consumers, understand their past and present interactions, anticipate their needs and their next Internet searches. This allows you to adapt your offers to address their expectations.
Foster brand commitment
The more data that Machine Learning has to work with, the more effective it will be in reaching potential customers with the right message at the right time and on the right channel.
Optimize sales
In order to define the best price, Machine Learning's algorithms factor in seasonality as well as supply and demand. This helps businesses set dynamic pricing, predict future sales trends, and manage inventory more efficiently, ultimately maximizing revenue and customer satisfaction.
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