what is machine learning

And how machine learning impacts your business processes

In the information age, and new technologies, having the right data is essential to guide the main decisions to be made in a company. It is necessary to have security so that the paths to be followed are of prosperous results and growth for the whole organization. However, with the immense amount of data that is collected daily, the analysis can be compromised.

Machine Learning - or machine learning - is able to generate and analyze vast amounts and complexities of data in an agile and automatic time. In this way, it is possible to reach more accurate results to support the entire process of digital transformation of companies.

machine Learning And how machine learning impacts your business processes
machine Learning And how machine learning impacts your business processes

The advantages of using this tool in their strategies are diverse, impacting the internal structure of the company - especially its processes - and generating value for end consumers. Machine Learning has also become a key resource for the success of the institution’s marketing plans.

If you want to know how machine learning is revolutionizing businesses, read on. In this article, we will talk about what it is, what its benefits are and the trends and possibilities.

What is machine Learning IA?

Machine Learning is a tool that is part of the set of actions of artificial intelligence. As the name suggests, it is the process of machine learning, which adapts considering the pattern of behavior and interactions that are made. This learning process happens without the explicit need for programming.

Machine Learning software is able to learn, grow, and develop new functions by coming into contact with new data. Its categorization capacity is extremely fast and reliable, bringing positive results for all business management and being fundamental to decision making.

With data analysis, the computer can detect trends and offer solutions to problems or bottlenecks that have not yet been identified by the organization. This is because the capacity of the machines can be superior to human work.

To achieve high efficiency, machine Learning uses algorithms and big data, identifying throughout the data structure stored patterns in order to create connections and learn from them. This learning process is carried out by different models, in some cases human interaction is necessary.

What is machine Learning IA?
What is machine Learning IA?

Supervised Learning

This type of methodology is used when the history of actions is fundamental to be able to predict probable future events. In this sense, the algorithms are directed to inputs, in which the desired output is already predicted by the organization.

This is the case, for example, of the validation of a transaction performed by credit card on a website. The algorithms can predict if the action is suspected of fraud, avoiding losses and future claims by the customer.

When there is this suspicion, the registration data is validated and the user will need to confirm the legitimacy of the information. So that the computer can validate if the person who is trying to do the business is, in fact, the owner of the card.

Unsupervised Learning

In this case, the system does not rely on data history to try to define what is the "right answer" for a given action. The algorithm, therefore, must figure out what is being required by exploiting existing data in order to find some structure.

This method can be positive for those who seek to identify customer segments that work with similar products or sectors. By cross-referencing this information, you can collect the data to create targeted and efficient marketing campaigns.

When considering patterns, algorithms can also create structures that allow suggested reading topics, recommend a product or purchase item, etc. Because different patterns of user behavior are crossed, seeking connections and similarities.

Reinforcement Learning

This learning method happens when the algorithm makes a discovery through trial and error. He analyzes what were the reactions to a certain attitude, considering whether it is consistent with the stipulated goal.

It takes into account three components:

  • agent: who will take the decision;

  • environment means the scenario with which the agent interacts;

  • action: the attitude taken by the agent.

The purpose of this method is to learn the best strategy to be used considering all the components mentioned. That is, the algorithm begins to detect what is the action taken by the agents in a given scenario, over a period of time.

How can machine Learning be applied?

The machine learning process is able to generate inputs and data relevant to the growth of a company. In fact, knowing what drives people to get in touch with your organization, requiring a certain type of product is critical to being able to qualify leads and optimize marketing campaigns.

With user behavior information on websites and social networks, you can gain insight into content personalization while helping customers advance their sales journey.

With machine Learning, therefore, it will be possible to develop a more personalized marketing campaign, capable of dialoguing directly with potential customers. In this way, investment in the sector will become more effective and campaigns tend to have a higher conversion rate for the sales sector.

For example, when purchasing media, you can enter data about a customer’s purchase history. With this, the machine Learning algorithms will look for patterns in the behavior of this consumer to point out the next products to be consumed. It is possible, therefore, to elaborate exclusive discounts and benefits.

Data collection and analysis also allows the machine Learning to establish a navigation pattern for a given user. With this, you can customize the communication by offering content that is within the needs and desires presented by the person’s profile. That is, the brand begins to dialogue exactly with its target audience.

How can machine Learning be applied?
How can machine Learning be applied?

Unlimited input of data

The data processing capacity of the machine Learning is practically unlimited, and it is possible to put different sources to collect the information.

With this feature it is possible to create alternatives to automate the internal processes of a company, allowing a global vision of the organization. This understanding is essential to make management more efficient, working on improvement points and making decisions more efficiently.

Agility in data processing and analysis

Data collection and mining is critical to being able to understand customer specificities and the capabilities to improve internal processes. But just having the information is not enough, you need to follow them and do an analysis in real time.

Machine Learning acts directly on this need, promoting data processing and analysis in an agile way. Thus it is possible, for example, to change in minutes the offer that is presented to the customer in an online ad.

Potential in conversion

By providing a more targeted and immediate offer, considering the pattern of user behavior on networks. Machine Learning provides the opportunity to increase retention and conversion rates in marketing strategies.

In this sense, all the knowledge obtained by the machine will be fundamental to optimize the marketing of the company and increase the number of sales.

Segments customers

To achieve as many opportunities as possible, you need to direct your communication. Machine Learning allows the company to identify various segments of its market, creating small segments of its audience, considering the patterns of behavior.

In this way, communication becomes more personalized and close, increasing the possibilities of interaction with the brand and, consequently, enhancing each stage of the sales funnel in the purchase journey.

Increase the customer experience Increase customer experience

Providing personalized communication is critical to increasing customer lifetime value with the brand. This calculation takes into account the purchase history, the interaction made with marketing campaigns and the actions it takes on the customer platform.

Machine learning allows you to gather all the information needed to perform a more effective calculation, allowing the brand to optimize its future interactions to increase this period of customer interaction and, consequently, the positive experience with the brand.

machine Learning: What are the trends and possibilities?
machine Learning: What are the trends and possibilities?

What are the trends and possibilities?

The expectations for the growth of the strategy are enormous. Machine learning can be involved in different sectors, providing a new way of working the organization’s internal and external business and strategies. The transformation of your company will go through solutions that require data and analysis.

Through it, it is possible to provide a new look at processes, optimizing the points that work and working on the points of improvement. All this with realistic information and raised in real time.

With machine Learning it will be possible:

  • innovate: creating new thinking and business disruptions based on data science;

  • explore: analyse unfamiliar patterns through information collected;

  • risk: challenge the market by proposing new solutions for structural bottlenecks in a given sector.

Machine Learning makes complex data analysis automated and systemic. In this way, it is possible to build models and increase the possibilities for profitable solutions in any market. With this information, the risks in risking the launch of new products are reduced, allowing companies to experiment and risk more in the market.

tripulação ET
tripulação ET

Team of commanders, experts prepared to take insights from the market and transform into relevant content

Combining Big Data with FinanceCombining Big Data with Finance
Which mindset should a leader have? Find out here.Which mindset should a leader have? Find out here.
Pillars of digital transformationPillars of digital transformation