Machine Learning: What is it and what is its influence on digital marketing?

Machine Learning
Machine Learning

Machine learning may seem futuristic, but its applications are endless today, including in digital marketing. Understand everything about the subject and how it impacts the future and current scenario.

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 the digital transformation of businesses.

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

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

What is machine learning

The very translation of the term “machine learning” already indicates its meaning. This technique covers 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 accurately 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 are those that humans need to interact controlling the output and input and interfere with machine training commenting on the accuracy of forecasts. Finally, the machine applies what has been learned in its algorithm for the next analysis.

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

Let’s talk a little more about these categories in the topic “popular methods”.

Advantages of Machine Learning

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

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

1. Do unlimited data entry

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

This way you can 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 and can automate internal business 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 they will see at one o’clock in the afternoon.

3. Help with conversion

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

In addition, it helps with retaining and converting a specific customer by processing information quickly informing the right time to contact you.

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

The customer segmentation is extremely important, but 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 behavioural patterns that you cannot detect.

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

6. Customer Lifetime Value

The value of the customer life is calculated based on their demographic history, shopping, their interactions with their marketing campaigns and actions they take on your platform.

Machine learning calculates the lifetime value of the customer more accurately, thus optimizing 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

The artificial intelligence or AI is the ability of the machine to mimic 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 smarter 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 when exposed to more data, ie machine learning is dynamic and requires no human intervention to make certain changes. This makes it 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 precision records for many important problems, such as image recognition, sound recognition, recommendation systems, among others.

Machine learning evolution

Because of new computing technologies, machine learning today is not like machine learning in the past.

This technology was born 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 data.

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

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 large data faster and faster is a recent development.

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

Supervised learning

In this method, trained algorithms use data inputs, where 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 will likely 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 within.

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 key attributes that separate customer segments from each other.

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

The semi-supervised learning

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

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

In addition, semi-supervised learning is useful when the cost associated with labelling is too high to allow for a fully labelled 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 discovers through trial and error which actions yield the greatest rewards.

This type of learning has three main components: the agent (the learner 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 certain amount of time so that he will achieve the goal much faster by following a good strategy. Therefore, the goal of reinforcement learning is to learn the best strategy to use.

Machine learning in digital marketing

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

Fortunately, machine learning came to automate many tasks that used to take a long time to complete.

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

Learn about the key changes that will occur with machine learning applied to digital marketing.

Search Engine Optimization

From an SEO standpoint, keywords may become less important. Search engines receive more revenue when they provide users with superior content.

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

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

Pay per click (PPC) campaigns

Because Google has introduced new “smart” features such as Google Smart Bidding, Smart Display Campaigns, and Market Audience to help companies maximize conversions, PPC’s future has come to involve 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 business;
  • understand the obstacles that could make it difficult to achieve your goals;
  • track the performance of your campaigns to make more strategic decisions;
  • learn how Google’s new smart features work.

Content Marketing

Nowadays the internet has become flooded with a lot of content, either good or bad quality.

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 and allow you to better densify your data so you can create actionable tasks that will lead you to success.

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

It’s important to remember that this also applies to your email marketing campaigns.

After Google launched the Penguin algorithm, many thought 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 throughout the mobile market, gaining an even greater presence within applications, digital assistants and AI as a whole. You can even enter drone territory and auto-drive 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 may be great news for some, it is important that adjustments to marketing processes be 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 business, adapt your processes. For this:

Make your site responsive

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

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

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

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

Be ready for voice lookups

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

In addition to qualifying 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 user connection and automate tasks through technology.

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

Thus, machine learning will come into your strategy with the goal of delivering a faster and more personalized customer experience that goes beyond your expectations.

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

Therefore, brands should be extremely knowledgeable about the interaction of each particular consumer, creating a detailed view of data that helps them understand personal journeys separately.

In this sense, machine learning is a good option to dive into this data and help you through this whole process of the digital transformation of your business.

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