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A guide to machine learning in Google search

ML in Search

Machine learning is a branch of artificial intelligence (AI). Machine learning enables computer systems to learn from data, recognize patterns and thus make predictions – without being explicitly programmed to do so. This works by using machine learning with algorithms. These are trained to arrive at a desired result even with new data.

One of the most prominent areas where machine learning has made unprecedented progress is in search engine optimization. Google, the undisputed leader in the search engine world, makes extensive use of machine learning to provide users with accurate and relevant search results.

The integration of ML into Google search has the potential to revolutionize both the way we search for information and the way businesses optimize their online presence. This guide will walk you through the basics of machine learning in Google search, how it works, its applications, and how you can use it to improve your SEO strategy.

Whether you’re a seasoned SEO professional looking to deepen your understanding of machine learning or a beginner just entering the world of digital marketing, this guide will provide you with the insights you need to understand and overcome the opportunities and challenges that machine learning brings to Google search.

Join us on this exciting journey through the world of algorithms and AI, and discover how machine learning is shaping and revolutionizing Google search. Not only will you understand the technology behind it, but you’ll also learn how to use it to your advantage. We promise that by the end of this guide, you will be better equipped to successfully navigate the digital landscape.

What is machine learning?

To understand what machine learning is, let’s first place it in the world of artificial intelligence. This is also important because these terms are often confused:

Artificial intelligence (AI) is a branch of computer science. With their help, intelligent behavior is to be automatically transferred to computers.

If you go one step deeper into the world of AI, you find yourself in Machine Learning. This involves training algorithms with data to make predictions. Machine Learning is learning to make more and more specific predictions with more and more experience (data).

If we venture one step further, we arrive at a newer subarea: Deep Learning . With the help of artificial neural networks, Deep Learning is able to convert unstructured information, i.e. texts, images, sounds and videos, into numerical values. These are then used for pattern recognition, prediction, or other learning.

Einordnung ML

Now we know where Machine Learning should be classified. But what is the difference between regular software and machine learning? In short, we tell a “normal” piece of software what to do – along with the data it needs to do it. In machine learning, we train the system on how to find a desired solution on its own with appropriate data – in other words, independently. In comparison, it looks like this:

Normale Software vs Machine Learning

Machine learning tasks

The question now arises: What tasks does machine learning perform? And how do you use them for your business? The second question requires a little more background knowledge, which we also provide in this article. But the first we can answer you directly:

  • Prediction of values (e.g. power consumption)
  • Probability calculation (e.g. purchase probability or weather forecast)
  • Clustering (e.g. by recognizing a certain group)
  • Determine correlations
  • Dimensions reduction
  • Optimization

Big Data, Data Mining or Machine Learning?

Terms like Big Data, data mining, and predictive analytics are often associated with machine learning. That’s not entirely wrong either, but you should still know the differences:

Big Data is huge, complex, and fast-moving data sets. It forms the basis for machine learning, because that needs these large amounts of data to work efficiently.

Data mining is performed by humans on data sets to find interesting patterns from them. In doing so, data mining uses techniques developed with the help of machine learning.

Predictive analytics is a process that uses historical data sources to create a mathematical model that predicts events in the future. Predictive analytics also uses machine learning techniques to its advantage. We have explained how you can use predictive analytics for your marketing strategy in an extra post.

Types of Machine Learning Algorithms

So machine learning uses algorithms to turn data into patterns and then into predictions. Three types of machine learning algorithms are used:

Supervised machine learning

In supervised learning, the algorithm is given fully labeled training data. From them, he should recognize patterns and correlations in order to subsequently make correct predictions about unknown data. A clear target variable is always predicted. It differs in classification and regression.

Classification is about determining whether something is part of a group or not. For example, predicting whether a customer will buy a product or not. Buyer group and non-buyer group.

If the target variable of the regression is to be classified, it is about how many customers will buy the product.

Unsupervised machine learning

If an algorithm belongs to unsupervised learning, it is not fed with sample data, but with unsampled data. Without a target variable, it should then independently cluster the data into patterns and relationships.

This type of algorithm is used when there is a lot of data available, but you do not know in advance how to use it. Clustering then shows you how to use them.

Reinforcing machine learning

Reinforced machine learning is a special form of algorithm because it interacts with its environment and does not require sample data. Actions are evaluated through a reward system. Thus, the algorithm independently learns the solution to a problem.

This works by not training the algorithm beforehand which action is the correct one. He receives only positive or negative feedback. This is how an action can be assigned to the correct time

How does Google use machine learning in search?

All major search engines use machine learning in one or more ways. With a market share of 84%, Google is known to be the largest. And uses a variety of machine learning algorithms to improve search results for users. We want to introduce you to some of the most important algorithms Google uses for Machine Learning:

  1. Natural language processing (NLP): Google uses NLP technologies to better understand the meaning of search queries and deliver more relevant results. This includes natural language query processing, misspelling detection, and multi-language query support.
  2. Recommender Systems: Recommender systems analyze user behavior and preference data to identify preferences. Google uses these recommender systems to deliver personalized search results.
  3. Clustering and classification: Google uses clustering and classification of (unsupervised) machine learning to rank how relevant a website is to a search query.
  4. Anomaly detection: Identifies unusual activity from normal within large data sets. For example, Google recognizes spam results and can remove…
  5. Google FLAN accelerates the transfer of learning from one domain to another and makes it less computationally intensive. Important to know: In machine learning, a domain does not refer to a website, but to the task or group of tasks it performs, such as sentiment analysis in natural language processing (NLP) or object recognition in computer vision (CV).
  6. V-MoE’s sole purpose is to enable training of large image processing models with fewer resources.
  7. Sub-pseudo-labels improves the recognition of actions in videos and helps with a variety of video-related insights and tasks.
lauren edvalson WiSizdeZHBI unsplash scaled

Google core algorithms

In addition, Google still uses some core algorithms to optimize the search results of its users:

RankBrain

Introduced in 2015, this algorithm contributed to the fact that Google no longer considers the world as strings of keywords and characters, but as things (entities). Thus, keywords, characters, and words combined into sentences became so-called entities. An entity is a thing or concept that is unique, well-defined, and distinguishable. Previously, Google considered an input like (Frankfurt, Hesse) as two words. They regularly occur together, but just as often they occur separately. With RankBrain, the two keywords Frankfurt and Hesse became one entity. This means that if there is a context, Google recognizes the context and thus the entity (Frankfurt, Hesse) for both keywords.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is somewhat younger. Launched in 2019, it provided Google with a transition from a unidirectional understanding of concepts to a bidirectional one. While previous models could only gain knowledge from words in one direction (unidirectional), it now gains contextual understanding based on words in both directions (bidirectional). The order of the words thus plays an important role in the unidirectional model; in the bidirectional one, information is also sent back.

MUM

MUM stands for Multitask Unified Model and is multimodal. That is, the model extracts information from different modalities such as texts, images, videos, etc. But what is much more interesting is that MUM can gather information in different languages, which it translates into the user’s language for an answer. If you want to prepare sushi, for example, a Japanese sushi master will probably have the best tips for you – but in Japanese. Through MUM, the user receives the Japanese preparation tips in his language. This also improves access to information for those languages that are not often considered on the Internet.

Where else machine learning is used

We have only touched on a few of the key algorithms that can have an impact on organic search. But there is more to what machine learning is being used for.

Questions like:
In Ads, what drives the systems behind automated bidding strategies and ad automation?
How does News know how to group stories?
How are specific objects identified in images?
How are video recommendations made?

These questions can all be answered with machine learning.

What does machine learning look like in practice?

Now we have cleared up a lot of the theory around Machine Learning; clarified the connection to AI and Big Data, explained the tasks and divided the algorithms into different types. But how does machine learning work in practice? A graphic helps us with the explanation:

Machine Learning in der

Definition: The basis of any machine learning process is the definition of a problem or goal. You need to define what machine learning should optimize.

Data: The second step is data acquisition. It is usually very time-consuming, because the better the data quality, the sooner you get your desired output.

Training: During this phase, the algorithm is trained – the actual machine learning begins. Patterns are recognized from which probabilities or values are predicted.

Interpretation: The results of the training are evaluated. On the one hand, this serves to understand the algorithm. Second, to see if it is producing the desired results. If he does not, he will be sent to training again with more data.

Model: A model is developed from the successful training results.

Practice: The model is used for practice and applied to unknown data

Application areas of machine learning

Machine learning can be applied in a wide range of work areas, for example in emergency services such as the police, for logistics systems, in mobility such as autonomous driving, and in IT security. For us, machine learning naturally plays an important role in marketing.

Machine learning can be a great help, especially in personalization. The algorithms are then applied to learn and compare customer behavior. The output can be used for individualized marketing in the form of product or action recommendations.

In Customer Relationship Management (CRM), machine learning is used to effectively optimize processes and make predictions for customer value.

Does machine learning make sense for your business?

As mentioned earlier, some medium and large companies are already using machine learning to individualize their marketing strategies. Techniques such as predictive analytics are applied to personalize the customer experience and attract new appropriate customers.
However, the development of machine learning algorithms is very complex. So at what point does it make sense to apply machine learning to your business?

Many well structured data
Do you have a high-quality, large and well-structured data set? Excellent, then Machine Learning can be a helpful solution for your business. After all, the foundation of any machine learning is data, and you’ve already got that.

Rules as decision-making aids
Do you already solve your problems with heuristics (rules and strategies)? Then you have an important foundation stone to go one step further. Machine learning can help you find more complex rules to solve more complex problems.

Machine learning can be used in pretty much any area. To decide if you need Machine Learning you need to ask yourself two questions:

1. do i need machine learning now?
2. what is my ROI (return on investment)? The question of all questions: Is it even worth it?

The use of machine learning is not even done as well. It takes a lot of time, competent manpower, money and just the data. So think carefully about when is the right time for Machine Learning to add value for you. Ask yourself these two questions regularly. Expert advice can also be helpful.

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