Why Machine Learning is a huge Value for Marketers

Machine learning

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Can a machine think and improve itself?
Can a computer acquire knowledge and know-how?

How is it useful for modern marketing?

We know that data and Artificial Intelligence have become vast players of marketing during the last few years.

A lot of big companies and advertising agencies are using AI to improve products and better understand customers behaviours. Machine learning has a critical role in the marketing game. Data are a gold mine for marketers. If an algorithm can use them to make a prediction and giving suggestion, this becomes a crucial digital marketing lever.

This is even more important in our data-based era because marketing is focusing more and moreover a technical approach based on data analysis. Machine Learning is the technology that, as a marketer, you want to leverage for your marketing strategy.

After reading this post, you will know how machine learning adds enormous value to your marketing strategy, what are supervised, unsupervised learning, and semi-supervised learning.

What Is Machine Learning with Examples

Is it possible for a machine to think and make a decision as a human?
Machine learning is the capacity of a computer to learn and improve its bits of knowledge and know-how by itself based on experience. It derives from a group of algorithms and theories about the self-learning of intelligent software.
Machine learning techniques allow to progressively sharpen and improve an algorithm’s reliability in recognizing data patterns. This means that computers can learn from the data they analyze by identifying patterns.

The system is capable of making hypothesis and tests, which makes it extremely valuable for digital marketing applications.

Machine Learning algorithms classification goes with how they learn from data.
We have:

Supervised learning, based over structured and labelled data. The algorithm works on data which has input and output. In this case, the algorithm will use the pieces of information it already has to make predictions and analyze new coming data. An example of this kind of use is the anti-spam filter on email providers, like Gmail.

Unsupervised Learning. All data is unlabeled. The system has no information, nor the previous path about the output, algorithms learn from the input data, identify patterns and classify the content — for example, Amazon’s recommendation system.

Semi-supervised Learning. Some data is labelled, but most of it is unlabeled. The training comes mixing supervised and unsupervised techniques. Usually, there is a smaller amount of labeled data in comparison to unlabeled ones. Typically, some data are clustered together by using an unsupervised learning algorithm. After that, the existing labeled data work as a base to cluster the rest of unlabeled ones. An example of this kind of algorithm is speech analysis.

Reinforcement Learning: the point of reinforcement learning is making decisions sequentially, and decisions are dependent. The software takes an action to reach maximum reward in a particular situation. The input is active training part of the system. Output comes out from the software decisions while is accomplishing the given task.

Scientists are studying without a stop to find always new, improved solutions.
What is relevant for us is that Machine Learning abilities to find a pattern and predict output is a powerful way of improvement for our marketing strategy.

Machine Learning And Marketing: A Few Examples

As we said, the more data is numerous and accurate, the more the software becomes “smart”. For our marketing intent, this means that the more intelligent it is, the better it can understand, or even predict, customers’ desires.

robot and human

Machine Learning unlocks powerful marketing insights. Machine Learning outlines the differences for:

Building a more efficient customer journey.

Thanks to predictive algorithms, it’s possible to analyze customers’ behaviour. Machines Learning algorithms help marketers in studying different metrics, such as clicks, conversions, time spent on the website and even more. They can improve themselves and learn how to react in the most efficient way for every customer’s step, for example, with more personalization.

Machine Learning can help to optimize the marketing mix by choosing which offers to show, supported by contextual content and previously collected information. Algorithm tries to predict a combination that could lead to a new sale, cross-sell or recommendations.

You can have an example of looking at Netflix. The platform uses the watching history of the spectator and confronts it with similar ones. In this way, it can suggest contents that the user is more likely to want to see. The same technology is used for personalization of thumbnails. Netflix uses video frames for existing movies and shows and ranks them, to identify which ones have led to more clicks.

Understanding customers’ sentiment

Analyzing recursive behaviors, and coupled with other algorithms more specifically focused on language, Machine Learning can help reading customers’ emotions. It can analyze text to find situations of dissatisfaction, and this allows brands to do something about it.

Increasing content optimization and engagement

Do you know which kind of content the customer’s looking for? Well, maybe you don’t, but Machine Learning does. You can use it to catch people’s attention. If people of two different clusters are navigating the same website, they may watch two completely different versions of the same web pages.

This happens because Machine Learning recommends contents or views according to historical data and real-time people behaviours. The result? A boost to your KPIs conversion.

Powering Testing

A/B test has always been crucial in marketing strategies. However, it’s time-consuming, and you focus on one variable. Artificial Intelligence and Machine Learning can make testing faster and more accurate. With this technology, it’s possible to experiment on a virtually infinite number of variables, which means running many tests in parallel with different layout options and real-time optimisations.

In this way, the best variations can be proposed to each different user, thinking about his preferences and habits, and the entire process is way less time-consuming.

The more data the system analyse, the more accurate it becomes, maximizing ROI from customer journey thanks to AI.


  • There are four types of Machine Learning: Supervised, Unsupervised, Semi-Supervised and Reinforcement
  • Machine Learning systems are capable of making hypothesis and tests. This makes them it very important for digital marketing.
  • The more data the system analyzes, the more accurate it becomes.


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