The definition of machine learning is “automatic learning”. I’m talking about a system of algorithms based on a machine’s ability to:
- Imitate human behavior;
- Learn progressively;
- Act autonomously, i.e. without having been expressly programmed to perform a specific action.
All of this already applies to digital marketing and is a great asset for companies and SMEs in developing their online business. What if I told you that by now you too are using the results of learning models, perhaps without realizing it?
The machine learning algorithms that I introduce here are only a very small part of the great world of digital marketing. If you are interested in learning more about all aspects of this activity, discover the Course in Digital Marketing.
In this article, I deepen the theme of automated knowledge and I will answer, through examples and applications that are part of our daily life, the following questions:
- What is machine learning and how does it work?
- Why is it important to use machine learning models and how to benefit from them in your business or career?
- What is artificial intelligence and what is the difference between deep learning and machine learning?
What is Machine Learning
Machine learning is a system that makes a computer capable of learning, predicting, and making changes independently, without the need for human programming intervention.
Machine learning is a branch of Artificial Intelligence aimed at creating and optimizing systems that emulate human intelligence, and are therefore capable of making improvement choices based on data analysis, formulating hypotheses, and making changes to evolve.
These data are learned thanks to the use of algorithms which, through statistical criteria, improve over time their ability to analyze data and information found, such as the recognition of patterns or models. Thanks to this learning system, computers constantly learn and improve their performance over time.
To better understand what machine learning is, you need to focus on the two main phases of the knowledge process:
- Data collection phase: every interaction or activity performed by the learning machine is transformed into something that it can use as an experience. The device observes what people do and, through this collection of information, predicts their future behavior;
- Predictive analysis phase: the machine learning algorithm animates this device and decides how to behave in future situations thanks to the data it has stored and classified during the first step.
Let’s see an example right away: have you ever noticed that Amazon constantly personalizes your homepage by offering you those products that are most in line with your interests? It can do this thanks to software that has learned to know your preferences and which, through this study, will be able to recommend products that are ever closer to what you are looking for. On the marketing side, it is an advantageous mechanism, as it allows you to save on advertising budgets and allows you to offer personalized customer journeys.
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How machine learning works
At the end of the 20th century, various scientific communities developed various technologies that are used in the fields of mathematics, and information technology, but also in the 4.0 industry of the Internet of Things.
What are the different machine learning techniques?
- Artificial neural networks (neural networks): they emulate the biological neural networks of the human brain and are mathematical or IT models capable of modifying their structure concerning the external stimuli they receive (input). We also often talk about Neuromarketing;
- Pattern identification: find a constant to classify data;
- Computational statistics: it allows to represent of reality quantitatively;
- Data mining: with which significant information can be extracted from a large amount of aggregated data. Ex: you want to send a newsletter about your product but your database has many contacts. You can filter them to send the DEM only to potentially interested users.
But what are the three types of machine learning? The three types of machine learning are supervised, unsupervised, and reinforcement learning.
Let’s see in detail the machine learning models:
During this type of automated knowledge, the algorithm is provided with examples, problems, and solutions, i.e. input-output data pairs, from which it learns to find solutions. The programmer supervises and checks, through tests, the correctness of the predicted results.
A very simple example is the spam filter present in e-mail. Initially, the software is taught the characteristics of emails considered unsafe. Then, it manages to assign tags to the data it stores, “spam” and “no-spam”, and will therefore be able to classify the mail you receive, even that sent by recipients with whom it has not yet interacted.
Based on the statistical data collected in a previous phase, the machine learning system can find results in situations that have never arisen yet (predictive model). A classic example is everything that is “recommended” to you by the platforms, such as the music you might like on Spotify or YouTube, or the similar products that e-commerce or Amazon offers you.
As in pedagogy, this knowledge process is based on positive reinforcements, i.e. rewards, and serves to increase the efficiency of a machine learning tool, software, or program. The algorithm that constitutes machine learning improves its performance “motivated” by the fact that every time it returns a correct output, it is sent a rewarding input as an evaluation of its work.
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Machine learning: 6 uses in the company
Where is machine learning used and why is it becoming so important?
The applications of AI learning span a multitude of fields and sectors. Datasets based on machine learning algorithms are useful for:
- All types of businesses, for large companies, SMEs, and also for
This model allows the achievement of sales and marketing objectives with a small budget.
Here are some examples of machine learning systems and how AI can be introduced into internal business processes and the various digital marketing channels, such as:
- Production sector;
- Customer management;
- Neural networks;
- Email marketing;
- Social advertising;
- Marketing automation.
1. Machine Learning in the manufacturing sector
Machine learning is used to:
- Optimize and reorganize production by companies in the manufacturing sector;
- Improve yield;
- Analyze the root cause;
- Manage the supply chain and inventory;
Developers use their knowledge of statistics, probability, and computation to build machine learning models, which can be set to learn automatically without further intervention. Collaboration between developers and data scientists can make machine learning projects more valuable and useful.
Machine learning provides predictions, such as through predictive maintenance, that enable companies to prevent equipment malfunctions, increase productivity, improve asset performance, and reduce capital and operating costs.
2. Customer management and retention thanks to machine learning
Machine learning helps companies evaluate customers by indicating their actual and potential value based on certain data. The definition of a customer value assessment system throughout its life cycle represents a crucial factor for e-commerce companies, but not only, since it can be applied in many other fields.
These systems, through the use of artificial intelligence algorithms, allow organizations to identify, understand and retain the most important customers. In essence, these systems analyze huge amounts of customer data to identify those who make the most purchases or who are particularly loyal, or both. In particular, customer lifecycle dataset models are proving to be particularly effective in predicting the revenue a company will earn from a single customer over a given period.
This information is essential for marketing strategies, as it allows companies to focus their activities on the most valuable customers and to incentivize their interaction with the brand. Additionally, these models allow organizations to target new customer acquisition spending to attract individuals with similar profiles to their existing top buyers.
3. Definition of customer churn rate
Machine learning for companies helps to build customer loyalty by defining strategies and variants of the processes used. Keeping existing customers happy and loyal is much more cost-effective and less expensive than acquiring new customers. To achieve this goal, companies are investing in building churn rate calculation models, which help identify potential buyers who are most likely to discontinue their interaction with the company and the reasons for doing so.
An effective model uses calculation systems based on machine learning to provide a complete view of the problem: from the risk of abandonment of individual customers to the main factors of abandonment, ranked in order of importance. These findings are essential for developing an algorithm-based retention strategy.
Knowing the customer churn rate helps companies optimize:
- Their offers;
- Email campaigns;
- Other targeted marketing initiatives.
To keep the most important customers coming back to buy. With the growing competition in the market, dynamic pricing has become crucial. This strategy, also known as “demand-based pricing,” allows you to stay ahead of ever-changing market dynamics.
With dynamic pricing, organizations can flexibly price items based on factors such as level of customer interest, demand at the time of purchase, and customer involvement in marketing campaigns. To achieve this, enterprises need to use robust machine learning strategies and analyze large amounts of data to understand how customers’ willingness to pay for goods or services changes in different situations.
Despite the complexity of dynamic pricing models, many companies such as airlines and shared transit services have already successfully implemented dynamic pricing optimization strategies with artificial intelligence to maximize revenue.
4. Clustering and classification to divide customers
Thoughtful customer targeting is a common goal for marketing firms, but the lack of reliable data and precise analytical tools has made this goal difficult to achieve.
Today, thanks to machine learning, enterprises can use advanced segmentation algorithms to create clusters of multivariate analysis with customer data based on multiple dimensions including browsing behavior, affinity, and geographic data. These segmentation algorithms can also be used to create purchasing behavioral models that help predict which customers are most likely to buy a particular product at a particular time.
This information allows companies to adopt a personalized marketing strategy, providing customers with offers and promotions that reflect their interests and purchasing behavior. Personalization of campaigns can significantly increase customer loyalty and overall company revenue.
Using data and advanced algorithms also allows companies to identify new market opportunities and improve their marketing efficiency. Studying customer data can reveal emerging trends and behaviors while automating marketing processes can reduce the time and resources required to implement effective campaigns.
In summary, advanced data analytics and machine learning are revolutionizing the way businesses approach marketing. To emerge in such a competitive market, it becomes increasingly important to be able to use Predictive Analytics and Clustering systems to:
- Personalize marketing campaigns;
- Identify market opportunities;
- Improve the efficiency of marketing activities.
5. Neural networks and image classification
Machine learning has proven to be useful in a wide range of applications for different industries:
- Financial services;
- Sanitary ware;
Its huge potential also extends to the science, healthcare, construction, and energy sectors. For example, using machine learning-based computational systems for image organization allows you to assign a label to a predefined set of categories or any input image. This technology can be used to create 3D building plan models from 2D blueprints, make it easy to tag photos on social media, communicate medical diagnoses, and more.
Deep learning methods, such as neural networks, are often employed for cataloging images because they allow for more effective identification of the most relevant characteristics, even in the presence of complications such as variations in view, lighting, scale, or color. the volume of superfluous image information. Thanks to this technology, it is possible to obtain insights of higher quality and relevance.
6. Digital marketing management with machine learning
Machine learning is increasingly important for digital marketing management. Thanks to it, companies can analyze large amounts of data and identify hidden patterns and trends, improving their online presence and increasing conversions in all modern practices whether SEO, SEM, or email marketing.
Google is committed to providing surfers with the best possible experience. This is why it suggests to users useful and relevant answers about the needs expressed at that moment.
We know that the ranking factors with which the search engine classifies the results that respond to a specific user query are over 200. But what hierarchy exists between the evaluation elements it applies? Google uses artificial intelligence and neural networks to put each factor in order.
Thanks to machine learning, Google can offer users:
- queries the moment users type into the search bar. Think, for example, of words written incorrectly, or when you are prompted to compile the sentence you are writing;
- related searches that help people even if their query doesn’t exactly match what they’re looking for online.
Finally, the vocal assistant, through speech recognition, can interpret voice search queries and respond with an appropriate SERP (Search Engine Result Page).
Email Marketing and Automation
Previously, I mentioned the use of machine learning models useful for segmenting a very large database, to establish personalized communication with each lead. With automation, you can program automatic mailings and you can also create a communication flow with users upstream.
Using electronic mail marketing software that includes learning models, you can also meticulously classify all contacts in your CRM, assigning them labels such as preferences. The system will progressively learn from the behavior of your users, it will be able to understand each of them which day and the best time of day for sending the email based on the opening rate.
This system of algorithms will be able to classify and order all the data you have collected on a person to allow you to suggest to your potential customer’s products in which they are statistically more interested. This strategy will result in better performance on open rates and conversions and budget and resource savings.
SEM, Social Advertising
Spotify, Google, Amazon, Facebook, Instagram, YouTube, and many platforms that we use every day use artificial intelligence systems.
These digital behemoths make money when companies spend on advertising, right? For this reason, they aim to continuously improve the performance of their tools to satisfy companies and entice them to invest in Ads. They exploit the potential of automated knowledge to learn what our tastes and habits are and predict what we will want, to offer it to us.
Machine learning systems allow these big players to understand and memorize our searches and our desires, to propose them to us, and induce us to buy. In this way, companies can hit only the statistically most interested target, thus optimizing returns.
For example, each of us on Facebook or LinkedIn has a certain number of friends or connections. Despite this, we don’t see each of their posts in the feed, have you ever wondered why? The reason is simple, the social channel algorithm only shows us the posts it deems most interesting for us.
This also happens in social advertising where machine learning algorithms tend to show us only the advertisements on which they know we interact, ensuring that those who invest in the campaigns will be able to maximize results and optimize the available budget.
In the initial part of the article, I mentioned the customization of the Amazon homepage or E-commerce through the login made by the user. The software that operates within these digital spaces can acquire a lot of information about your behavior and your shopping habits.
If you buy a good that is subject to consumption, such as a two-month supply of biscuits, the system will be able to re-propose it assuming you are about to run out. Or, if you buy many products related to running, the platform will offer you running shoes instead of walking sneakers.
Your home page will always be different from that of another person, it will follow your tastes and behavior by adapting to the needs you express, to the point of anticipating them.
All this might be scary in a way, but as we said, it is an advantage for you, because you will no longer waste time on useless searches. It is also an excellent aid for brands to increase sales, with consequent savings in time and economic resources.
Artificial Intelligence vs. Machine Learning
What are the differences between artificial intelligence and machine learning? The first is a macro area of the other.
- Artificial intelligence is the discipline that allows you to emulate human behavior, making sure that robots, computers, and software can perform actions that are normally performed by humans.
- Machine learning takes artificial intelligence (AI) one step further by making these machines also capable of:
- Learn how man;
- Evolve independently;
- Improve your performance progressively over time.
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Machine learning and deep learning
One of the approaches of machine learning is Deep learning, often translated as “deep learning” or ” deep neural network”, which has its roots in the use of artificial neural networks and the imitation of the interconnections between neurons in the human brain.
Another feature that emulates man is the ability of neural networks to modify their connection patterns concerning a changing environment, or rather to the variation of the input stimuli they receive.
The uses that are already in use today concern:
- Image identification;
- Speech recognition.
Conclusions and free advice on how to use machine learning in the company
At the beginning of the article, you were wondering what machine learning was, after a few paragraphs you realized that this machine learning system is already part of your daily life.
From social network algorithms aimed at showing us posts of interest, to voice assistance models such as Alexa or Siri that use artificial intelligence to simplify human tasks, up to marketing automation platforms, there are numerous advantages that the algorithms can bring to your business and everyday life. Discover all the secrets of machine learning, and request a personalized and free coaching session.
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