Machine learning in everyday digital marketing

Machine learning in everyday digital marketing

Machine learning in everyday digital marketing

robot lying down studying and learning on a notebook

If you've seen reports about cars that drive themselves (without a driver) or planes that are in the air flying autonomously most of the time, even without knowing it, you're seeing the potential of machine learning.

And if large companies are already using this possibility of machines helping humans in different sectors, we can say, without a shadow of a doubt, that digital marketing industry did not stay out of this innovation. And it could not.

What is machine learning?

Machine learning is a type of artificial intelligence that can be explained when a computer makes decisions based on algorithms, which help it recognize patterns and make predictions based on all this data.

With these results, the system improves itself and presents increasingly accurate predictions. And when we say system, we mean that when one machine learns, all the others learn too. The progression is exponential. This is machine learning.

But this concept is not new. This search for patterns is a way to make many aspects of human life easier. In the past, it was a way to improve harvests and prevent hunger. Today, with the use of machines, it can even result in differentiated initiatives that will direct a political campaign to achieve better results.

But far from grandiose actions like airplanes or self-driving cars, machine learning has also been improving the daily lives of human beings in many aspects that we don't even stop to think about, such as improving spam filters and classifying files into specific folders in your emails.

Other formidable actions of machine learning are the interaction with computers through voice, with the use of personal assistants and chatbots

How does it work?

There are some methods that will help machines improve their predictions. This engineering can happen like this:

    • Supervised learning: here machine learning needs the help of a “teacher” who will insert specific data into the system so that the computer can see patterns;
    • Unsupervised learning: here the data is entered without specific direction and the machine will learn to see the patterns among a set of data;
    • Reinforcement learning: In this case, the machine is taught which action should be prioritized in a given context. The results presented will be related to rewards and punishments that will reinforce the learning.

In digital marketing

Machine learning for digital marketing allows for many uses that are of invaluable importance. We can mention the results of each user's social media news feed, made available based on each person's preference data, or even personalized recommendations for products, movies or music.

To explain this better, we will show how machine learning could be used with Google data. For example, when a mother buys a TV, she includes some data in this purchase that allows information to be cross-referenced with her social media profile, such as her preferences based on what she likes.

With this data, it is possible to gather other information that can create a flow of sending materials that stimulate a new sale.

In other words, this shows that when we use Google, we leave traces on the platform that can be very useful in enhancing actions to help the user move more smoothly through the sales funnel. This is valuable for digital marketing, because it saves time and improves results.

But a Google Cloud survey conducted with users and marketing professionals presented at the 2018 RD Summit reveals that these digital marketing actions, based on the potential of machine learning, could have better results.

  • 63% of consumers would like to have different experiences (which could be worked on based on the user's purchase history);
  • 57% of marketers are unable to deliver information the way they would like;
  • 62% of people expect brands to deliver a consistent experience;
  • Only 42% of users believe this is already done.

This sea of ​​data that is available on Google, for example, allows companies to use this treasure in a better way, building an integrated ecosystem of tools and solutions.

How to make the most of machine learning

The accuracy of the results presented by machine learning is a reliable source for digital marketing to rely on. To achieve this, it is possible to improve results through some actions.

Lead qualification

Data analysis with machine learning can allow a company to increase revenue through marketing actions.

Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQL) are some of the many benefits of machine learning that allow you to qualify customer and prospect lists.

With the relevant data available, it is possible to build an ideal customer profile (ICP) because with each sale made the data is updated and improves the possibilities for a new sale.

This will help sales team professionals save time and prioritize increasingly qualified leads.

More efficient ads

The recommendation system can be greatly improved with the potential of machine learning because it identifies patterns in consumer behavior and also in the context in which they are inserted, offering items specifically aimed at them.

With this data, increasingly personalized, specific and efficient advertisements can also be created for each person.

What we mean is that segmentation can be greatly improved based on each consumption data. Let's use the example of the mother above who bought the TV. There is an endless amount of data that will point out differences from mother to mother, even if they have the same age, number of children and income.

The accuracy of the combinations and predictions interpreted by machine learning will allow ads to be different even if each person “seems” to be in the same market niche.

chatbot

Bots have been winning over humans since they entered a process of learning the naturalness of human speech.

They can help a visitor navigate a website, answer common questions, and improve the quality of responses based on new questions they receive.

Engaging content

Machine learning can analyze data, create new insights, and help build personalized content strategies for the Content Marketing.

This more personalized and targeted material will add value for the user, because it will allow the mechanisms to better find content that will reach the target audience more effectively.

Avoid cancellation fee

The data presented by machine learning can help reduce the Churn Rate (or cancellation rate) of businesses.

Conclusion

Know that this is a reality with no turning back, the use of machine learning can help humans improve their results, save time and money.

It is a fact that machines will replace us in many tasks. And in digital marketing, there are many activities that will be better performed with the speed, analysis capacity and conclusions of machines.

Let's be happy about this and pay attention to what the platforms

 

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