Decades ago, when someone heard about artificial intelligence, they automatically thought of robots invading the world and subjecting people to their yoke. However, today, we all have internalized how positive it is in everyday life. Thanks to it, we can communicate with a website through a chatbot, or receive promotions that are tailored to our hobbies and interests, among many other things. However, for those who have represented an important advance, it has been for those responsible for marketing, who through machine learning in digital marketing, have the opportunity to make crucial decisions, quickly based on big data. This is what I am going to talk about in this post. Let’s see it.
Automatic learning or machine learning (ML) is a class of artificial intelligence methods that are characterized by not providing direct solutions to problems, but training systems to apply solutions.
We can come across a large number of machine learning methods, but they are usually divided into two main groups: those that learn with a “teacher” and those that do not .
In the case of the former, it is a person who provides the machine with initial data in the form of situation-solution pairs. The machine learning system then analyzes these pairs and learns to classify situations based on known solutions . A very simple case that is useful to all of us is, for example, when the system learns when to mark certain messages that arrive in our mailbox as spam .
In the other case, that is, when it learns without a teacher, the machine receives all the information in a disordered way from situations without solutions, and it learns to classify those situations based on similar or different signs, without human guidance .
In the field that interests us, that of digital marketing, machine learning is used above all to find patterns in the activities of users on a website. This helps to predict the future behavior of these users and quickly optimize advertising campaigns.
Why is Machine Learning effective in Digital Marketing?
The objective of machine learning in marketing is none other than to help make quick decisions, based on large amounts of data (Big Data).
The work process in this regard is as follows: ML specialists create a hypothesis, test it, evaluate it and analyze it. Although it seems simple, this work is long and complicated, and sometimes, the results are wrong, because the information is changing every second.
For example, to evaluate 20 advertising campaigns considering 10 behavioral parameters for five different segments, a specialist will need approximately four hours. If such an analysis is carried out every day, the specialist will spend precisely half of his time evaluating the quality of the campaigns. When using machine learning, the evaluation takes minutes and the number of segments and behavior parameters is unlimited.
Thanks to Machine Learning, we can respond faster to changes in the quality of the traffic generated by campaigns. The result is that marketers can spend more time creating hypotheses , rather than taking routine actions.
Another important factor to take into account is the speed , but due to the fact that the data expires, and as it becomes obsolete, the value of the results we have obtained decreases.
One person cannot process the volumes of information that analytics systems collect in a matter of minutes. Through those ML systems, hundreds of requests can be processed , organized, and provided with results in the form of an immediate response to a question.
What benefits does Machine Learning have for daily work in Digital Marketing ? Very easy:
- Improves the quality of data analysis.
- It allows you to analyze more data in less time.
- The system adapts to changes and new data.
- It allows you to automate marketing processes and avoid routine work.
- It does all of the above quickly.
Examples of the use of Machine Learning in Digital Marketing.
There are a wide variety of uses for machine learning in the field of digital marketing, however, I think the most interesting or noteworthy are these that I present below:
- Recommender systems.
They are the ones already known to all, in which customers are offered the products that interest them at that moment.
A recommendation system predicts which products a customer is most likely to buy. With this information, it generates push and email notifications, as well as “recommended products” and “similar products” blocks on the web.
The result of this is that users see personalized offers, which increases the probability that they will make the purchase.
To achieve this, K-means clustering algorithms are often used.
- Segmentation by forecast
The objective of the segmentations is none other than to be able to use the advertising budget only on those target users who are worthwhile or who are more likely to buy our product or service.
The most used segmentations are:
- Creation of segments on which to target advertising, in such a way that advertising is shown to those groups with the same set of attributes.
- Targeting that is triggered by displaying ads to usersafter they perform a certain action, such as viewing a product or adding an item to the shopping cart.
- Predictive targeting, in which ads are shown to users based on how likely they are to make a purchase.
The main difference between these types of guidance is that predictive guidance uses all possible combinations of tens or hundreds of user parameters with all possible values. All other orientation types are based on a limited number of parameters with certain ranges of values.
What forecast segmentation predicts is the probability that a user will make a purchase in “n” days.
The result of using this type of segmentation is that you can show advertising to a more specific audience, which increases the effectiveness of the campaigns.
The most common algorithms to achieve this are: XGBoost , CATBoost, Decision Tree (if little data is available or few patterns are evident).
- LTV Forecast
The best-known methods for calculating customer lifetime value , or LTV, are based on knowledge of a customer’s total profit and the length of time the customer has been interacting with the business. However, it is often interesting to know the LTV before it leaves, in order to create business strategies based on the result for each client. In this case, the only solution is to predict the LTV based on the available data and group by segments .
Once you have the expected LTV per customer, and you have created the different segments based on this, the segments are loaded in the system that is used, and the communication shipments are automated based on the abandonment rate of each one, with the aim of avoiding these leaks and maximizing the value of each client.
Once you have launched the campaigns, the segments should be loaded into Google Analytics using them to analyze the effectiveness of the advertising campaigns based on the expected LTV.
The result of applying this type of technique is that you can determine the advertising budget per user based on the LTV, thus improving the effectiveness of the campaigns.
Common algorithms for this purpose are usually: XGBoost , SVM , Random Forest or Logistic Regression.
- Forecast dropout rate.
The concept of abandonment or exit refers to customers who have left the company and, therefore, the associated loss of income. It is usually expressed in percentage or monetary terms.
Abandonment rate forecasting allows you to respond to a customer’s intention to abandon your product or service before they actually do. Which is tremendously useful for any company, since you can define actions to prevent that from happening.
The way to work it would be to create different segments based on the probability of abandonment, and plan and automate a series of actions for each of those segments.
Thanks to this, what you get is to improve customer retention and therefore, the benefits of your company.
The most common algorithms for this purpose are: SVM, Logistic Regression and other classification algorithms.
As you can see, machine learning is not something as complex as it may seem when reading its name, being quite well down to the reality of any company. This is aimed at obtaining quantifiable improvements and benefits in terms of budget efficiency, marketing actions and results.
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