what is it and what is its influence on digital marketing? – WAU

Imagine being able to generate models that analyze large and complex data quickly and automatically to deliver accurate results on a large scale? This is what machine learning does. This powerful technique is becoming increasingly popular with companies’ digital transformation. With accurate data models, […]

Imagine being able to generate models that analyze large and complex data quickly and automatically to deliver accurate results on a large scale?

This is what machine learning does. This powerful technique is becoming increasingly popular with companies’ digital transformation.

With accurate data models, companies are able to identify profitable opportunities and avoid dangerous mistakes.

But not only that. The advantages of using this strategy are many and can help you even with the process of prospecting customers and selling your service or product. Therefore, its use influences both the success of digital marketing.

In this article you will discover:

What is machine learning

The translation of the term “machine learning” itself already hints at its meaning. This technique encompasses the idea of ​​machines with the ability to learn on their own from large volumes of data.

But how do they do it?

Through algorithms and big data, identifying data patterns and creating connections between them to learn how to perform a task without human help and intelligently.

These algorithms use statistical analysis to predict responses more precisely and deliver the best predictive result with the least chance of error.

This technology can be separated into two main categories: supervised or unsupervised.

The supervised algorithms they are those in which the human being needs to interact controlling the output and data input and interferes with the training of the machine by making comments on the accuracy of the predictions. Finally, the machine applies what has been learned in its algorithm for the next analysis.

Already in the category unsupervised, the algorithms use deep learning to process complex tasks without human training.

We will talk a little more about these categories in the topic “popular methods”.

Advantages of machine learning

While some machine learning tools can be expensive, the only real factor that marketers may find difficult to adapt are the ever-changing algorithms launched by Google, among other search engines.

On the other hand, there is no doubt that using machine learning technology provides numerous advantages for companies. Meet some of them.

1. Make unlimited data entry

Machine learning has the ability to process virtually unlimited amounts of data from a variety of sources.

In this way, it is possible to constantly review them and adjust the message based on customer behaviors.

Once a model is trained from a complete set of data sources, it can identify the most relevant variables and convey the right information, in addition to being able to automate the company’s internal processes.

2. Process, analyze and predict quickly

The speed with which this technology can consume data and identify relevant information is in real time.

For example, machine learning can constantly optimize the next best offer for the customer. Therefore, what the customer can see at noon is different from what he will see at one o’clock in the afternoon.

3. Help with conversion

These systems act on the results of machine learning and make the marketing message much more dynamic.

In addition, it helps with the retention and conversion of a specific customer by processing information quickly informing the right time to contact.

4. Learn from past behaviors

A major advantage of machine learning is that models can learn from past results to continually improve their predictions based on new data.

5. Customer Segmentation

Customer segmentation is extremely important, but it takes a lot of work.

Machine learning can be used to identify various segments of your target market, as well as create micro-segments based on behavioral patterns that you cannot detect.

This data can help you create a predictive approach to segmenting your customers, allowing you to guide each one individually through their buying journey.

6. Lifetime customer value

The customer’s lifetime value is calculated based on their demographic history, purchases, their interactions with their marketing campaigns and the actions they take on their platform.

Machine learning calculates the customer’s lifetime value more accurately, thereby enabling them to optimize their future interactions with them.

Differences between artificial intelligence, machine learning and deep learning

Many people confuse the meaning of artificial intelligence, machine learning and deep learning. Although they are related, they do not mean the same thing. Know what each of these technologies is.

Artificial intelligence

Artificial intelligence or AI is the ability of the machine to imitate some human characteristics, such as visual perception, speech recognition, decision making and language translation.

There are many ways to simulate human intelligence, and some methods are more intelligent than others, be it a simple statement of logic or a complex statistical model.

Machine learning

Machine learning is a subset of AI. That is, all machine learning relies on AI, but not all AI has automatic learning.

This technology is the ability of computers to learn without being explicitly programmed, adjusting to respond according to the data available for analysis.

One aspect that separates machine learning from other intelligent systems is its ability to change itself when exposed to more data, that is, automatic machine learning is dynamic and does not require human intervention to make certain changes. This makes you less fragile and less dependent on human experts.

Deep learning

Deep learning is a subset of machine learning. Generally, when people use this term, they are referring to artificial and very complex neural networks.

Deep artificial neural networks are a set of algorithms that have established new records in precision for many important problems, such as image recognition, sound recognition, recommendation systems, among others.

Evolution of machine learning

Because of new computing technologies, today’s machine learning is not like automatic learning from the past.

This technology was born out of pattern recognition and the theory that computers could learn without being programmed to perform specific tasks.

So, researchers interested in artificial intelligence decided to see what computers could learn from the data.

The iterative aspect of machine learning is important, because as models are exposed to new data, they are able to adapt independently.

Thus, they learn from previous calculations to produce reliable and repeatable decisions and results.

Although many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data faster and faster is a recent development.

Popular methods

As discussed in the topic “What is machine learning”, the most adopted methods are supervised learning and unsupervised learning, but there are also other methods of machine learning. Meet the most popular.

Supervised learning

In this method, the trained algorithms use data inputs, in which the desired output is already predicted.

Supervised learning is commonly used in applications where historical data can predict likely future events.

For example, it can anticipate when credit card transactions are suspected of fraud or that a customer is likely to file a complaint.

Unsupervised learning

This method is used for data that has no history. The system does not count the “correct answer”. The algorithm must find out what is being requested. The goal is to explore the data and find some structure inside.

Unsupervised learning works well on transactional data. For example, it can identify customer segments with similar attributes that can be treated similarly in marketing campaigns, or it can find the main attributes that separate customer segments from each other.

These algorithms are also used to segment text topics, recommend items, and identify open data values.

Semi-supervised learning

This method is used for the same applications as supervised learning. However, there is a difference: it uses labeled and unmarked data for training.

This type of learning can be used with methods such as classification, regression and forecasting.

In addition, semi-supervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process. An example of this is being able to identify a person’s face on a webcam.

Reinforcement learning

This method is often used for robotics, games and navigation.

With reinforcement learning, the algorithm finds out through trial and error which actions produce the greatest rewards.

This type of learning has three main components: the agent (the apprentice or the decision maker), the environment (with which the agent interacts) and actions (what the agent can do).

The goal is for the agent to choose actions that maximize the expected reward over a given amount of time, and so he will reach the goal much faster following a good strategy. Therefore, the purpose of reinforcement learning is to learn the best strategy to be used.

Machine learning in digital marketing

Digital marketing is a strategy that is always changing with new technologies.

Fortunately, machine learning came to automate several tasks that used to take a long time to do.

With this technique being used to solve a huge set of diverse problems with the help of data, channels, content and context, it becomes easier to focus on the strategy as a whole.

Know the main changes that will occur with machine learning applied to digital marketing.

Search Engine Optimization

From an SEO perspective, keywords can become less important. Search engines earn more revenue when they provide users with higher quality content.

As a result, the algorithm they use needs to be more focused on providing each user with relevant content, rather than being stuffed with keywords.

You therefore need to start thinking about the quality of your content as a ranking factor in the search engines.

Pay-per-click (PPC) campaigns

As Google launched new “smart” features, such as Google Smart Bidding, Smart Display Campaigns and Audience in the market to help companies maximize conversions, the future of PPC ended up involving machine learning.

To become more strategic and take your PPC campaigns to the next level, you need to:

  • know the most valuable metrics for your company;
  • understand the obstacles that could hinder the achievement of your goals;
  • monitor the performance of your campaigns to make more strategic decisions;
  • learn how Google’s new smart features work.

Content Marketing

Currently the internet has become flooded with a lot of content, whether good or bad.

However, as mentioned above, to be successful, you need to create content that is valuable to your readers. To do this, you need to understand consumer trends.

Machine learning tools allow you to reduce the amount of time you spend tracking your data, as well as allowing you to densify your data better so you can create actionable tasks that will lead you to success.

That way, you will be able to better understand your customers’ profile and which path they take to reach you. This helps you to track the content most relevant to him and what he likes to consume.

It is important to remember that this is also applicable to your email marketing campaigns.

Link Building

After Google launched the Penguin algorithm, many thought that link building was dead, as it penalized any business that bought links.

But instead of dying, link building has evolved. Now, as a marketer, you need to look for brand awareness content, articles that mention your brand, and search for guest post content instead of simply buying your links.

Future of this strategy

Experts believe that machine learning will continue to grow across the mobile space, gaining an even greater presence within applications, digital assistants and AI as a whole. You can even enter the territory of drones and self-driving cars.

However, as the demand for more data and more algorithms is increasing, you can expect more machine learning tools to become available.

While this can be great news for some, it is important that adjustments to marketing processes are made to avoid mistakes along the way.

Tips for adapting to machine learning

As machine learning is on the rise and new tools and algorithms are being launched every year, it is essential that you, as a company, adapt your processes. For this:

Make your site responsive

Having a responsive website that has fast load times, supports multiple media and is mobile friendly is crucial to your Google ranking.

Research shows that improving the design of your website can significantly increase your traffic. Make sure to run multiple tests on your website and keep the user experience in mind at all times.

Optimize for local search

To rank highly on Google you need to think about smart search and optimize for local search.

This means that you should focus on listing your company with its name, address, website URL and other company details on a variety of platforms so that it is easily found.

Be ready for voice searches

The combination of machine learning and the proliferation of voice search has made conversational research a new channel for companies of all types who want to rank highly on Google.

In addition to ranking for long-term keywords, consider creating content that meets your customers’ specific voice queries.


In general, it is important to understand the need to create a connection with the user and automate tasks using technology.

With new smart devices and user interaction with them, your brand will need to adapt to meet an increasingly digital demand.

Thus, machine learning will enter your strategy with the aim of delivering a faster and more personalized experience to the customer, which goes beyond their expectations.

This new digital age is the era of assistance and the ability to surprise consumers by meeting their needs.

Therefore, brands must have an extremely deep knowledge about the interaction of each consumer in particular, creating a detailed view of data that helps to understand personal journeys separately.

In this sense, machine learning proves to be a good option to immerse yourself in this data and help you through this process of digital transformation of your business.

However, this technique is just one that will transform our daily lives. If you want to know everything about what awaits us and how to adapt your company to this new reality, check out the Digital Transformation and Marketing ebook!

Digital transformation and marketing