Machine learning: learn more about this trend!

Machine learning, an essential branch of artificial intelligence, is increasingly present in our daily lives, shaping everything from the recommendations you see online to complex business decisions that drive innovation.

This technology allows systems to learn and improve their performance from data, without the need to be explicitly programmed for each new situation.

Here at Branddi, we deeply understand the power and nuances of this tool, as it is a fundamental pillar in our intelligent brand protection solutions, allowing us to identify patterns and anomalies on a large scale with precision. But what exactly defines this area and how does it actually work in practice?

We invite you to continue reading to unravel the concepts behind this transformative technology. Shall we go?

What is machine learning?

Essentially, Machine Learning is a field of artificial intelligence focused on developing systems that can learn and improve from data, without being explicitly programmed for each task.

To do this, they analyze information, recognize patterns, and use this learning to make predictions or make decisions.

This capability has driven its adoption on a large scale. So much so that recent global research, such as a McKinsey study released by CNN Brazil, indicate that more than 70% of companies already use AI based on machine learning to optimize results.

At Branddi, for example, we apply the principles of Machine Learning to train our systems to recognize with agility and precision everything from practices of unfair competition to Fraud attempts and online piracy.

It's this capacity for continuous learning that allows us to provide dynamic and effective brand protection in an ever-changing digital environment.

Machine learning and deep learning: what are the differences?

Within the vast field of Machine Learning, there is an even more specialized and powerful sub-area: Deep Learning.

Although both seek to make machines learn with data, the main difference lies in the way they learn and the complexity of the problems they can solve, especially with unstructured data such as images, audio, and text.

Traditional Machine Learning generally depends on a previous stage where specialists define and manually extract the most relevant characteristics (features) from the data so that the algorithm can learn.

Deep Learning, on the other hand, uses structures called artificial neural networks with multiple layers (hence the “deep”), which are able to learn these characteristics directly from raw data, in a hierarchical and much more autonomous way.

The rapid growth of investment in Generative AI, which frequently uses Deep Learning and generated US$ 33.9 billion globally in 2024, according to Stanford AI Index Report, highlights its importance.

How does machine learning work?

The operation of Machine Learning is based on algorithms that analyze vast sets of data to identify patterns and learn from them, without the need for explicit programming for each specific task.

Based on this analysis, the system builds a model capable of making predictions or making decisions about new data. Therefore, this model is continuously being improved as it processes more information.

A notable example of its impact is on e-commerce: it is estimated that recommendation systems that use machine learning, such as those from Amazon, are responsible for generating about 35% of the platform's total sales, demonstrating how learning from data translates directly into concrete results.

Types of machine learning

Machine Learning doesn't operate in a single way: it encompasses different learning approaches, each appropriate to specific types of problems and data.

The choice of the correct method depends fundamentally on the objective sought to achieve and the nature of the information available. In fact, this versatility is one of the reasons why technology has spread so quickly.

Um recent report from IDC (International Data Corporation) projects that worldwide spending on AI systems, where Machine Learning is central, will exceed US$ 600 billion in the coming years, reflecting their growing impact on several industries.

Understanding the main categories of machine learning is critical to harnessing its potential, whether to create personalized recommendations or to identify complex patterns in online activities, a capacity that we continuously refine to protect brands in the digital environment.

Let's explore the most common approaches below.

Supervised Learning

In this approach, the machine learning algorithm is trained with a set of previously labeled data, where each input example has a known correct “answer”.

The objective is to learn how to map inputs to outputs, allowing the model to make accurate predictions about new unseen data. It's like learning with a teacher.

Common examples include classifying emails as spam or non-spam, image recognition (identifying a cat in a photo), and forecasting property prices based on their characteristics.

Unsupervised Learning

Unlike the supervised one, here the algorithm works with data that does not have predefined labels. The goal is to discover structures, patterns, or relationships hidden in the data itself, without a “right answer” as a guide. Think of it as finding insights on your own.

Typical applications involve segmenting customers into groups with similar behaviors (clustering), anomaly detection (identifying false transactions), and reducing dimensionality to simplify complex data, revealing the most important characteristics autonomously.

Reinforcement Learning

This type of machine learning is similar to human trial and error learning. In it, an agent (the algorithm) learns to make decisions by interacting with an environment.

To do this, he receives rewards for desirable actions and penalties for unwanted actions, seeking to maximize the total reward over time.

It is widely used in games (teaching an AI to play chess or Go), robotics (training robots to perform tasks), dynamic recommendation systems, and in the development of strategies for autonomous vehicles.

Benefits of machine learning for companies

The implementation of Machine Learning in companies transcends mere technological adoption. This is because it is a strategic investment capable of generating real value and lasting competitive advantages.

And the impact is measurable: an Accenture study, widely publicized in industry analyses such as those of Intuition, revealed that 42% of companies stated that the profitability of their ML and AI initiatives exceeded expectations.

This demonstrates the power of machine learning to transform data into actionable insights and meaningful optimizations.

Whether improving operational efficiency or ensuring the security of digital assets against complex threats, Machine Learning offers a range of opportunities.

We will detail below the main gains it can bring to your business.

Increased operational efficiency

The application of Machine Learning goes far beyond simple automation; it reengineers processes to achieve higher levels of efficiency.

Algorithms analyze workflows (process mining), identify invisible bottlenecks, and automate repetitive, rule-based tasks with precision and speed that exceed human capabilities, often through AI-powered RPA (Robotic Process Automation).

Think about optimizing logistics routes in real time, chatbots that instantly resolve customer questions, or systems that predict machinery failures before they occur, proactively scheduling maintenance.

This not only streamlines operations, but it also frees human teams from tedious tasks, allowing them to focus on activities with greater added value, such as strategy and innovation.

The tangible result is a significant average reduction in operating costs, often around 30% in a few years, as indicated by studies referenced by consultancies such as Deloitte, transforming efficiency into a pillar of profitability.

Improved decision-making

In a world flooded with data, the human capacity to process and extract value from all that information is limited.

Machine Learning overcomes this barrier, analyzing massive volumes of structured and unstructured data at speeds impossible for human analysts, discovering subtle patterns, complex correlations, and emerging trends.

This equips managers with much deeper and more accurate insights, underpinning strategic decisions ranging from dynamic pricing and inventory management to credit risk assessment and investment selection.

ML models can also run complex simulations of future scenarios (“what-if analysis”), allowing us to assess the potential impact of different choices before implementing them.

This data-driven approach dramatically reduces reliance on intuition and “guesswork”, minimizes risks, and substantially increases the likelihood of successful corporate decisions in increasingly volatile business environments.

Predictive analytics

The true strength of Machine Learning often lies in its ability to look to the future, not with a crystal ball, but through the rigorous analysis of historical data to identify indicators that predate future events.

This is because predictive models can predict with remarkable accuracy everything from the probability of a customer leaving the company (churn) to the future demand for a specific product, including the prediction of failures in critical equipment or fluctuations in financial markets.

This ability to anticipate allows companies to move from a reactive to a proactive stance, taking preventive actions, optimizing resource allocation, and preparing for challenges and opportunities before they materialize.

In the retail sector, for example, the application of AI and ML to refine demand forecasts has demonstrated the ability to increase accuracy up to 20%, directly impacting inventory management, reducing costs with excesses or ruptures and improving customer satisfaction.

Improving the quality of products and services

Truly understanding customer experience and product performance in the real world is fundamental to continuous improvement.

And Machine Learning enables this understanding at an unprecedented scale and depth, analyzing diverse sources of feedback such as online reviews, mentions on social networks, support tickets, and usage data.

Algorithms can quickly identify friction points, desired functionality, and bugs, directing development and support efforts. That is, for companies that face the challenge of scams and counterfeits that harm the consumer experience, ML is vital.

At Branddi, for example, we intensively use Machine Learning to detect and remove fake sites, advertisements, and profiles that attempt to deceive customers.

By eliminating these sources of negative experiences, we help our clients to protect the integrity of their brand and to ensure that consumers have contact with the genuine experience, which, as observed in our results in the area of combating Online Scams, can lead to a reduction of up to 80% in complaints and legal risks associated with these fraudulent practices.

Marketing optimization

The era of mass marketing is giving way to hyperpersonalization, and Machine Learning is the engine of that transformation.

Using techniques such as clustering to segment audiences based on real behaviors and preferences, recommendation systems (such as collaborative filtering) to suggest individually relevant products, and predictive models to estimate customer lifetime value (LTV), companies can create much more targeted and effective campaigns.

In addition, algorithms optimize bids in digital advertising auctions (programmatic advertising) in real time and dynamically adjust prices based on demand and consumer profile.

This data-driven approach ensures that marketing investment is allocated more intelligently, maximizing the reach of the right people with the right message at the right time, which translates into higher conversion rates, greater engagement, and a Return on Investment (ROI) significantly higher.

Competitive advantage

In today's business landscape, the ability to adapt and innovate quickly is more than a differential—it's essential for survival.

Machine Learning acts as a fundamental catalyst for this agility, providing a decisive competitive advantage.

The analysis of Gartner Hype Cycle™ 2023 for Artificial Intelligence reinforces this view, highlighting that innovations in AI (including Generative AI that dominates current discussions) offer 'significant and even transformative benefits'.

Gartner points out that 'The early adoption of these innovations will lead to a significant competitive advantage'.

In other words, companies that integrate ML effectively not only optimize operations and make smarter decisions based on data; they are 'rethinking their business processes' and the value delivered to customers, as mentioned in the analysis.

Investing strategically and in a planned manner in AI and ML capabilities, considering the most promising innovations identified by Gartner, is no longer optional, but an imperative to remain relevant and ahead of the competition in a market undergoing constant and accelerated technological evolution.

Fraud and threat detection

The sophistication and volume of modern digital scams and attacks require equally advanced defenses. Traditional systems based on fixed rules are easily circumvented by fraudsters who adapt their tactics constantly.

Machine Learning, on the other hand, continuously learns with new data, identifying anomalous patterns and suspicious behaviors that are out of the ordinary, even if they have never been seen before.

This is successfully applied to the detection of fraud, analysis of insurance claims, identification of money laundering transactions, detection of network intrusions (cybersecurity), and identification of fake accounts or bots on online platforms.

The speed and scale at which ML operates are essential to intervene in real time and prevent financial loss and reputational damage.

Branddi: high technology and human expertise combined

Understanding and applying Machine Learning isn't just theory for Branddi: it's the heart of our intelligent brand protection solution.

To do this, we use the power of AI to relentlessly monitor the global digital environment, identifying threats such as unfair competition, fraud, and piracy at an unparalleled scale and speed.

However, technology alone is not enough. The precision of our AI is enhanced by the expertise of dedicated specialists, who analyze, validate, and direct actions, ensuring strategic and effective action from monitoring to takedown.

It's this combination that allows us to shield your brand with real results.

Want to see how this approach protects your business? Visit the Branddi website and learn more about our Shielding marketing!