Machine learning is artificial intelligence that gives computers the ability to learn data. Algorithms can be influenced based on input data. This system works to train algorithms so that the final results are accurate.
When you hear the term machine learning, it must be familiar if it is closely related to artificial intelligence. Basically, machine learning is a development of artificial intelligence. Along with the development of technology, the modification of devices in such a way by applying technological concepts to facilitate work is increasing. Machine learning is one of them.
So, what is this artificial intelligence actually for? What are the benefits of applying it to certain activities in your work? Let's listen to a further explanation of machine learning!
What is Machine Learning?
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| What is Machine Learning? |
Machine learning refers to an artificial intelligence that can give computers the ability to learn data, identify patterns, and make decisions without the need for programming first. That way, machine learning requires relatively minimal human intervention or involvement. This machine also focuses on developing computer programs that can access, use, and learn that data.
System learning is done by observing or based on data. The observation process is quite diverse, such as direct experience, giving instructions, looking for data patterns and making decisions. Some of these things make it possible for the system to automatically learn and adjust an action.
Computers form complex algorithms that make the application of data for the learning process. The programming forms a model that can build a prediction around the identified data.
An algorithm uses a parameter to build a pattern in the decision-making process. The algorithm will automatically adjust the parameters if there is additional new data to check for changes in patterns if there is a change.
Another thing you need to know when getting to know this artificial intelligence is the division of its learning system. There are two main learning systems, namely directed or undirected.
At a glance, you may have been able to grasp the fundamental differences between the two. The directed learning system refers to the process of completing data and solutions that are indeed provided for machines in learning and identifying certain patterns. In contrast, the undirected learning system refers to providing access to collected but unsorted data so that the machine can independently decipher the patterns in it.
Machine learning is a component that plays an important role in the development of the field of data science. This is none other than because the algorithm works in such a way as to make predictions and classify the data mining process.
How machine learning works step by step?
So, how does machine learning work? This technology will work when you enter a data set into the selected algorithm. The data set will become data that functions in developing the selected algorithm. In inputting data, the method used can affect the algorithm. In addition, newly entered data also functions to test the algorithm, to ensure whether the algorithm works correctly or not.
The system is then trained to make predictions and results that will be examined further later. If the prediction results are less accurate, the algorithm must be trained in depth until it can produce accurate predictions. This also aims to train the algorithm so that the final results will reach the optimum level and the right accuracy.
What is the relationship between AI and marketing?
Can we use this technology to support other processes such as product or service marketing processes? The answer is yes! Both machine learning and marketing processes are closely related to data collection or management. If you have problems analyzing data, this artificial intelligence technology can help you quickly and effectively.
So, how does machine learning help support your marketing process or efforts? Here's the explanation!
- Analyze the required data. Find patterns in audience activity on your website. By using this technology, you can even predict their future activity patterns to optimize your website.
- Optimize content. It is no longer a strange thing or step if optimizing the content you create is an essential point in marketing activities. In that case, machine learning technology can help you rank higher on the SERP page.
- Increase personalization. By using this algorithm system, you can share personalized content with your audience or customers. You can even use it to track user behavior in detail when visiting your website, understand what they like, and display a personalized homepage with recommendations for them.
- Improve marketing automation. By improving the quality of your marketing automation, the level of engagement of your audience or customers will automatically increase significantly.
- Utilize chatbots. By using chatbots based on machine learning technology, queries or questions asked by your customers will be automatically answered. The answers given can also have a high level of accuracy. This is because the algorithm must study the existing information in order to answer these questions correctly.
Machine Learning for SEO
In addition to being used to support marketing needs, machine learning is also utilized in the Search Engine Optimization (SEO) process. How is machine learning used to help the SEO process? Check out the following explanation!
Search engines like Google also use machine learning where their algorithms will prioritize things related to optimization such as ad optimization, determining website ranking factors, and much more. For that, it is not foreign if machine learning is related to SEO efforts.
It is important for you to prioritize the user experience when visiting your site when talking about machine learning. The better the user experience, the greater the likelihood of user engagement on your website.
High-quality information and proper web optimization are essential. However, user experience is no less important than both of them. There are several important points that you should pay attention to, such as:
- Try to adjust the website so that users do not work too hard to access the information in it.
- Pay attention to the level of effectiveness of each part of the website and as a whole.
- Provide visual media that is easy and comfortable for users to access.
- Stay consistent when designing a website design that is friendly for the user experience.
- Try to choose a design that is simple but still functions as it is.
- Provide a specific page or section to interact with your audience.
Machine Learning Utilization Strategy for SEO
Machine Learning is the right tool to help the search engine optimization process. Here are strategies that can be applied in utilizing machine learning:
1. Providing Smart Content
Machine learning or AI can easily process content needs according to your client's requests. AI can help in developing and updating content continuously.
2. Local Search Engine Optimization
AI SEO is the right solution for local search engine needs. Local search engines play an important role in achieving rankings on the first page of search engines.
3. Developing Quality Content
By using machine learning or AI SEO, it is likely that website traffic will increase so that your site can appear on the first page of search engines like Google.
4. Creating a Strong Analysis
Machine learning also plays a role in gradually checking the progress of your business gradually. AI is able to provide detailed and complete reporting analysis that is also easy to access.
What are examples of machine learning in SEO techniques?
Machine learning is not enough to be implemented only in the aspects of big data and artificial intelligence. In its development, machine learning is also applied to the realm of search engines and digital marketing. The existence of machine learning algorithms is very helpful in conducting searches related to what users actually want to eliminate curiosity.
The emergence of the term Googling has now become a new verb that is starting to be known by many people to carry out search activities about certain topics (someone or something) on the internet using the Google search engine. Google handles 40 thousand queries every second, more than 3.5 billion searches per day, 1.2 trillion searches every year. According to StatCounter Global Stats, Google has a market share of 92 percent of the entire search engine market in the world.
Lately, SEO techniques have been quite popular among business activists and digital websites. The use of SEO techniques is considered to work because our website has the opportunity to appear on the first page of the Google search engine. With this step, our business brand will be easily recognized by the wider community. By optimizing SEO, the website has the opportunity to get more traffic. Here are some examples of machine learning applications in SEO techniques:
1. K-Nearest Neighbors (KNN)
K-nearest neighbors (KNN) is a type of supervised learning algorithm used for regression and classification. KNN tries to predict the right class for test data by calculating the distance between the test data and all training points. Then select K the number of points closest to the test data.
The K-Nearest Neighbors or KNN algorithm is one of the algorithms widely used in the world of machine learning for classification cases. This algorithm works by taking a number of K closest data (neighbors) as a reference to determine the class of new data. This algorithm classifies data based on similarity or proximity to other data. K-Nearest Neighbor, data points that are close together are called neighbors.
In this algorithm, it is known as the method of calculating distance with Euclidean Distance. To calculate the distance between two points in the KNN algorithm, the Euclidean Distance method is used which can be used in 1-dimensional space, 2-dimensional space, or multi-dimensional space.
1-dimensional space means that the distance calculation only uses one independent variable, 2-dimensional space means there are two independent variables, and multi-dimensional space means there are more than two variables. The use of KNN is implemented in search recommendations made by users who are used as people"s also ask.
2. Support Vector Machine
Support Vector Machine or SVM is a supervised machine learning model that uses a classification algorithm to solve two-group classification problems. After providing the SVM model with a labeled training dataset for each category, it can categorize new text.
Simply, Support Vector Machine is one of the machine learning algorithms with a supervised learning-based approach that can be used for classification and regression problems. SVM works to find the best hyperplane or decision boundary function to separate two or more classes in the input space.
Hyperplane can be a line in two dimensions and can be a flat plane in multiple planes. The use of Support Vector Machine is applied to the classification of leads based on demographic characteristics.
3. Information Retrieval (IR)
Information Retrieval (IR) is the process of obtaining information system resources that are relevant to information needs from a collection of those resources. Searches can be based on full text or other content-based indexing.
Information retrieval or information retrieval is a field related to the structure, analysis, organization, storage, search and retrieval of information. The purpose of information retrieval is to provide the best information according to the current needs of users. Simply, there are four processes in information retrieval or information retrieval.
Starting from the existence of information needs from users. Users analyze information needs by compiling queries or keywords or frequently asked questions. Selection of information retrieval systems, for example when users use Google. Then users will immediately search on Google with queries and query strategies that have been prepared
4. Decision Tree
Decision Tree is a non-parametric supervised learning algorithm used for classification and regression purposes. This algorithm has a hierarchical tree structure. One of the most difficult decisions and things is when a digital marketer makes budgeting and selection in choosing the right keywords. Moreover, each keyword has a different search intent and average search volume.
For example, there is a keyword X with 1350 search volume. This means that this keyword has 1350 searches within a period of one month. The use of the decision tree algorithm is also carried out to classify based on keyword segmentation. This will make it easier for digital marketers to estimate PPC based on the grouping of keywords that have been selected according to search intent and what keywords the business wants to target.
5. Learning to Rank
The Learning to Rank algorithm is used to solve problems related to keyword relevance. It is undeniable that having keywords will help us find what we want. At least user expectations are met first to conduct a search according to the problems they are facing.
For example, when you want to search for the keyword "recommendations for men's shoes for traveling", the user's expectation is to find the right review or at least a list of shoe brands that are suitable for traveling. However, these keywords are often irrelevant in the sense that the explanation is not sharp enough.
So sometimes users rush back because the article does not display complete information. Therefore, in the Google algorithm, especially on the first page (top three), Google usually recommends complete and structured reviews.
Decision trees are also used in several things related to digital marketing:
- User behavior whether to click or open an article link
- User segmentation based on demographic characteristics. Starting from location or place of origin, age, gender, where to get information about the website or article
- Budgeting or budget in conducting an advertising campaign
6. Convolutional Neural Networks
As we know that neural networks strengthen search engine algorithms in SEO techniques. Moreover, Google's own algorithm also knows how many books you have searched for, how many keywords you want to search for every day, how many keyword recommendations come out even with similar keywords.
Later, Google will recommend authors with the same type of book, articles that match the search results. So SEO techniques are no longer just for doing keyword searches. But how the keywords match the relevance of the search results that they really need.
One of the algorithms that supports is Convolutional Neural Networks which is also useful in optimizing images in SEO. Images are a very important visual component in measuring audits and website testing.
An example is when you have a website and are building a landing page such as adjusting fonts, blog content, alt text on images. Even based on research from Kyleads, 40% of marketers quote a conversion rate of less than 0.5%. Thus, SEO fits into a business's long-term strategy.
Companies like Google are using CNNs for facial recognition, where faces can be matched to names by looking at the unique features of each face in an image. Similarly, CNNs are being tested for use in document and handwriting analysis, as they can quickly scan and compare a person’s handwriting to results from big data.
Conclusion
Artificial intelligence technology such as machine learning can easily support your marketing activities or efforts, especially in digital marketing. To maximize results, of course, you need a deep understanding of what to do and what to avoid. Like humans, systems also need practice in order to show good results or significant progress.
Also make sure you are ready with solutions to challenges that may arise later. To improve your digital marketing, use SEO services by Sitespirit as a solution. With the right strategy, get more potential customers.
