Search Engine Ranking Models - Ultimate Guide - SEO Agency Serpact™ (2023)

In recent years, with the rapid growth of the World Wide Web and the difficulty in finding the information you want, efficient and effective information retrieval systems have become more important than ever, and the search engine has become a major tool for many people. The ranking system, a central component in every search engine, is responsible for matching the processed queries and indexed documents.

Due to its central role, much attention has been paid and continues to be given to the research and development of ranking technologies. In addition, ranking is also essential for many other information retrieval applications, such as filtering, answering questions, multimedia retrieval, text aggregation, and online advertising. The use of machine learning technologies in the ranking process has led to innovative and other effective ranking models, and has led to the emergence of a new research area of ​​the name – ranking training or Learn-to-Rank.

Before proceeding with the examination of the models, it is important to emphasize that there are no uniform ranking models and that each model or group of models is used according to the relevant problem that needs to be solved. There are many different scenarios and ranking models that are of interest for document retrieval. For example, sometimes we need to rank documents purely according to their relevance to the request. In other cases, we need to look at the links of similarity, the structure of websites and the variety of documents in the ranking process. This is also called relational ranking.

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Conventional ranking models

Many ranking models have been proposed in the information retrieval literature. They can be roughly categorized as relevance and relevance ranking models.

Models for relevancy ranking

The purpose of the relevancy ranking model is to draw up a list of classified documents according to the relevance between those documents and the search query. Although not necessary, for ease of implementation, the relevance ranking model typically takes each individual document as input and calculates a result that measures the correspondence between the document and the request. Then all the documents are sorted in descending order according to their results.

Early relevance ranking models retrieved documents based on the appearance of concepts / words from a search query. Examples include the Boolean model. In principle, these models can predict whether a document is relevant to the request or not, but they cannot predict the degree of relevance.

The Vector Space (VSM) model is proposed to further model the degree of relevance. Both documents and inquiries are presented as vectors in Euclid space in which the internal calculation of two vectors can be used to measure their similarities. To obtain effective vector representation of the application and documents, the TF-IDF calculation is widely used.

Popular models:

  • BM25
  • VSM
  • LSI- Latent Semantic Indexing
  • Language Model For Information Retrieval – LMIR

Importance ranking models

Search Engine Ranking Models - Ultimate Guide - SEO Agency Serpact™ (2)One of the most popular models here is the so-called PageRank model. PageRank uses as a basic criterion the likelihood of a user arbitrarily clicking links to be taken to a particular web page to account for the likelihood of a link being weighted. Many algorithms have been developed to further improve PageRank’s accuracy and efficiency. Some focus on speeding up calculations, while others focus on refining and enriching the model. Examples are topical-sensitive PageRank and Query-dependent PageRank. It is assumed that links from pages with the same theme will weigh more than links with other topics.

Algorithms are also offered that can generate a stable ranking of importance over spamming links. For example, TrustRank is an importance ranking algorithm that considers the reliability of web pages when calculating the importance of pages. In TrustRank, a set of trusted pages are first identified as home pages. Then the trust on the homepage spreads to other pages in the web link graph. Because TrustRank distribution starts from trusted pages, TrustRank can be more spam-resistant than PageRank.

Estimates based on request-level positions

Given the large number of ranking models, a standard rating mechanism is needed to select the most effective model. Indeed, evaluation has played a very important role in the history of information retrieval. Information retrieval is an empirical science and it is a leader in computer science for understanding the importance of relevance and comparative analysis. Information retrieval is well served by the Cranfield Experimentation methodology, which is based on joint collections of documents, information needs (inquiries) and relevance assessments.

By applying the Cranfield paradigm to document retrieval, the relevant evaluation process can be described as follows:

  • Collect a large number of (randomly extracted) queries to form a test set.
  • For each request q- Collect the documents {dj} m j = 1 associated with the request.
  • Take the judgment on the appropriateness of each document through a human evaluation.
  • Use a ranking model to rank the documents.
  • Measure the difference between the results of the ranking and the assessment of appropriateness using an evaluation measure.
  • Use the average measure for all queries in the test set to evaluate the performance of the ranking model.

A number of strategies can be used for the collection of request documents. For example, a person can simply collect all the documents containing the requested word. One may also choose to use some predefined ranking systems to obtain documents that are more likely to be relevant.

A popular strategy is the merger method used in TREC. This method creates a pool of potentially relevant documents by sampling documents selected from the various participating systems. In particular, the top 100 documents received in each submission cycle for an application are selected and consolidated into the human evaluation space.

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Learn-to-Rank & Machine Learning

The previous section introduced many ranking models, most of which contain parameters. For example, in BM25 there are parameters k1 and b, parameter λ in LMIR and parameter α in PageRank. In order to achieve a relatively good ranking (in terms of evaluation measures), these parameters must be set using a validation set. Nevertheless, parameter tuning is far from trivial, especially given that the assessment measures are discontinuous and indifferent in terms of parameters.

In addition, a model perfectly tuned to the training set sometimes performs poorly on unseen test requests. This is usually called over-fitting. Another question is about the combination of ranking models. Given that there are many models in the literature, it is natural to explore how to combine these models and create an even more effective new model. However, this combination and its effectiveness are still in question.

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While information retrieval researchers have described and sought to address these issues, machine learning has proven to be effective in automatically adjusting parameters, combining multiple features, and avoiding over-adaptation. Therefore, it seems quite promising to adopt machine learning technologies to solve the aforementioned ranking problems.

However, most of the most up-to-date ranking training algorithms are learning the optimal way to combine the required query and document pairing features with discriminatory training. Ranking methods have the following two properties, which can be divided into 2 types:

Featured based

A feature-based feature means that all documents under study are represented by feature vectors that reflect the compliance of the documents with the search query. That is, for an application q, the related document d can be represented by a vector x = Φ (d, q), where Φ is a function extractor. Typical features used in ranking training include the frequency of terms requested in the document, the output of the BM25 model and the PageRank model, and even the relationship between a document and other documents. These features can be extracted from the search engine index.

Even if the function is the output of an existing extraction model, in the context of ranking training it is assumed that the parameter in the model is fixed and the training is carried out in the optimal way to combine these characteristics. In this sense, automatic parameter tuning of existing models before has not been categorized as “training for ranking” methods.

The ability to combine many features is an advantage of ranking training methods. It is easy to incorporate any new advances in model extraction by incorporating model output as one dimension of characteristics. This is true for popular search engines as it is almost impossible to use just a few factors to meet the complex information needs of web users.

Discriminative Training

Discriminatory training “Discriminatory training” means that the learning process can be well described by the four components of discriminatory training – input, output, hypothesis, training set with loss function. That is, the classification learning method has its own input space, output space, hypothesis space, and loss function.

In machine learning literature, discriminatory methods are widely used to combine different types of characteristics without the need to define a probability framework to represent object generation and predictive accuracy.

Discriminatory learning is an automatic learning process based on learning data. This is also one of the requirements for popular search engines for implementation, because every day this search engines will receive a lot of user feedback and data.

Learn-to-Rank Framework

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From the figure we can see that when training for classification, as a type of controlled training, a set of trainings is required. A typical training set consists of n training queries qi (i = 1,…, n), their related documents represented by function vectors x (i) = {x (i) j} m (i) j = 1 ( where m (i) is the number of documents related to application qi) and the relevant judgments of relevance. Then, a specific learning algorithm is used to learn the ranking model (i.e., how the characteristics are combined) so that the output of the ranking model can predict the etiquette of the underlying truth in the training set as accurately as possible with respect to loss function. In the test phase, when a new request appears, the model learned in the training phase is applied to sort the documents and return the relevant ranked list to the user in response to his / her request.

Basic approaches to ranking training

Pointwise Ranking

Search Engine Ranking Models - Ultimate Guide - SEO Agency Serpact™ (6)When we use machine learning technologies to solve the ranking problem, probably the simplest way is to check that existing training methods can be applied directly. In doing so, one assumes that the exact degree of relevancy of each document is what the models will predict, although this may not be necessary since the goal is to achieve a ranked list of documents. According to the different machine learning technologies used, the pointwise approach can be further subdivided into three subcategories: regression-based algorithms, classification-based algorithms, and ordinal regression-based algorithms.

For regression-based algorithms, the output space contains real-valued results; for classification algorithms, the output space contains unordered categories; and for algorithms based on ordinal regression, the output space contains ordered categories.

Point approaches consider one document at a time in the loss function. They essentially take one document and train a classifier / regressor on it to predict how appropriate it is for the current application. Final ranking is achieved by simply sorting the list of results on these documents. For point approaches, the score for each document is independent of the other documents that are on the results list for the request. All standard regression and classification algorithms can be directly used for pointwise ranking training.

The entry space of the point approach contains a vector of each document element. The output space contains the degree of compliance – relevance of each document. The different types of judgment can be made into the main labels of truth in terms of relevance as a degree:

  • If the decision is given directly as a degree of relevance lj, the basic truth label for the document xj is defined as yj = lj.
  • If judgment is given as a double preference – pairwise lu, v, one can obtain ethics as a basic truth by counting the frequency of a document over other documents.
  • If the estimate is given as the general order πl, the basic truth label can be obtained by using a mapping function. For example, the position of a document in πl can be used as a basic truth.

Kinds:

  • Regression-based models
  • Models based on classification
  • Models based on ordinal regression

Pairwise ranking

Search Engine Ranking Models - Ultimate Guide - SEO Agency Serpact™ (7)The doubles approach does not focus on accurately predicting the relevance of each document, but instead takes care of the relative order between the two documents. In this sense, it is closer to the concept of ‘ranking’ than the pointwise approach. In the dual approach, ranking usually comes down to the classification of pairs of documents, ie. to determine which document in a pair is preferred. That is, the purpose of the training is to minimize the number of missing classified pairs of documents. As a last resort, if all pairs of documents are correctly classified, all documents will be classified correctly. Note that this classification differs from the pointwise approach classification in that it operates on every two documents under study.

It is a natural concern that pairs of documents are not independent, which violates the basic assumption of classification. The fact is that, although in some cases this assumption is not really true, classification technology can still be used to teach a ranking model. However, another theoretical framework is needed to analyze the aggregate data of the model’s learning process.

Models:

  • SortNet
  • RankNet
  • FRank
  • Rank Boost
  • Models based on preference
  • Ranking SVM
  • GBRank
  • Multiple Hyperplane Ranker
  • Magnitude-Preserving Ranking
  • IR-SVM
  • Robust Pairwise Ranking with Sigmoid Functions
  • P-norm Push
  • Ordered Weighted Average for Ranking
  • LambdaRank
  • Robust Sparse Ranker
  • LambdaMart

Listwise Ranking

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Listwise’s approaches directly look at the entire list of documents and try to build their optimal ordering. There are 2 basic sub-techniques for listing:

Ranking teaching: Direct optimization of IR metrics such as NDCG. For example SoftRank, AdaRank.
Minimize the loss function that is determined by understanding the unique properties of the type of ranking you are trying to achieve. For example, ListNet, ListMLE. Listed approaches can be quite complex compared to pairwise or pointwise approaches.
The entry space of the list approach contains a set of documents related to query q, for example, x = {xj} m j = 1. The exit space of the list approach contains the list with ranking (or permutation) of documents. Different types of judgments can be made into basic truth labels for a ranked list:

If judgment is given as a degree of relevance lj, then all permutations that are consistent with judgment are major true permutations. Here we define permutation πy as corresponding to the degree of relevance lj, if ∀u, v satisfying lu> lv, we always have πy (u) <πy (v). In this case, there may be many basic truths. We use Ωy to represent the set of all such permutations.

If judgment is given as a double preference, then again all permutations that are consistent with the dual preferences are permutations of the basic truth. Here, we define permutation πy as consistent with the preferences lu, v, if ,u, v satisfying lu, v = +1, we always have πy (u) <πy (v).
Again, there can be many basic true permutations in this case, and we use Ωy to represent the set of all such permutations.

Such treatment can be found in the definition of rank correlation:

If the estimate is given as the general order πl, it can be directly determined πy = πl. Note that in the list approach, the output space that facilitates the learning process is exactly the same as the task output space. In this regard, the theoretical analysis of the list approach may have more direct value in understanding the real ranking problem than the other approaches when there are discrepancies between the facilitating learning space and the real task output space. The hypothesis space contains multivariable functions h that work on a set of
documents and predict their permutation.

For practical reasons, the hypothesis h is usually realized with an evaluation function f, for example h (x) = sorting ◦ f (x). That is, the evaluation function f is first used to evaluate each document, and then these documents are sorted in descending order of results to obtain the desired permutation.

There are two types of loss functions widely used in the listwise approach. For the first type, the loss function is explicitly related to the valuation measures (which we call the loss-specific loss function), while for the second type the loss function is not related. Note that it is sometimes not very easy to determine whether the loss function is sequential, as some list loss line items can also be considered pointwise or pairwise.

In this article, we mainly distinguish loss by point or double loss according to the following criteria:

  • The list loss function is defined with respect to all documents related to a request
  • The list-losing function cannot be completely decomposed to a simple summation on single documents or pairs of documents
  • The List Loss function emphasizes the concept of a ranked list and document positions in the end result are visible.

Types and models:

Minimization of Measure-Specific Loss

  • Measure Approximation
  • Bound Optimization
  • Non-smooth Optimization

Non-measure-Specific Loss Minimization

  • ListNet
  • ListMLE
  • Ranking Using Cumulative Distribution Networks
  • BoltzRank
Search Engine Ranking Models - Ultimate Guide - SEO Agency Serpact™ (9)

FAQs

How can I do SEO by myself? ›

For now, here are the six steps you should follow to get results with DIY SEO:
  1. Set Your Organic Search Baseline.
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  3. Publish Optimized Content.
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20 Nov 2020

How to do SEO on Google? ›

  1. Make your site interesting and useful.
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Follow these suggestions to improve your search engine optimization (SEO) and watch your website rise the ranks to the top of search-engine results.
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What are the 5 SEO elements? ›

Five Essential Parts of SEO
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24 Dec 2014

Can I learn SEO in a week? ›

☝️ To learn the basics of SEO, you need to spend 4-6 weeks studying SEO every day for at least a couple of hours. Once you master the basics, you can decide whether this is the career path you want to follow.

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The simple answer is that SEO can be a great way to make a decent living, and provided you have the skills to scale an SEO practice, the sky's the limit. Nowadays, there are a lot of ways to make money online: from building an ecommerce website to creating a blog to promote products and services.

Can you learn SEO in 3 months? ›

Experts say it usually takes one to three months to learn the foundations of SEO and a year or more to master the practice fully. The length of time it takes to learn the basics of SEO depends upon several factors.

Is SEO paid or free? ›

SEO is for organic traffic – so that's unpaid or free listings, and SEM is for targeted ads that you pay for. They can be complementary but only if the website itself is SEO-friendly first, then SEM has a greater chance of being successful.

Is Google SEO a skill? ›

If you're interested in the many career and side hustle job opportunities tech has to offer, search engine optimization (or “SEO” as you're more likely to see, hear, or read it) is a skill you should get familiar with ASAP.

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SEO is not that hard to learn. All you have to do is be willing to allocate the necessary time and effort to learn the various SEO concepts. If you are just getting started with SEO and wondering what it takes to go from novice to expert, then this post is for you.

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Getting a Google SEO certification can launch your career forward; but it can also be a waste of time and money. If you're just getting started in SEO, a certificate can help you understand the basics or gain more technical knowledge. Just remember, there is no replacement for hands-on SEO experience.

Can I learn SEO in 2 months? ›

It takes 1-3 months to learn SEO at a basic level and as long as 6-18 months to learn SEO at an advanced level. How much time it takes to learn SEO depends on the number of hours each day you can study search engine optimization.

Is Google SEO difficult to learn? ›

SEO is not necessarily hard to learn, but it does take time, effort, and persistence. If you are just starting and know nothing about search engines and how they work, you can expect to feel a bit overwhelmed initially, especially if you are trying to learn SEO on your own. However, it is important to keep trying.

What are the 3 types of SEO? ›

The three kinds of SEO are:
  • On-page SEO – Anything on your web pages – Blogs, product copy, web copy.
  • Off-page SEO – Anything which happens away from your website that helps with your SEO Strategy- Backlinks.
  • Technical SEO – Anything technical undertaken to improve Search Rankings – site indexing to help bot crawling.
25 May 2021

What are the 3 pillars of SEO? ›

The Three Pillars Of SEO: Authority, Relevance, And Experience.

How do you write SEO in 4 easy steps? ›

Writing SEO Articles with Semrush: The 4-Step Guide
  1. Gather research in Topic Research and the Keyword Magic Tool. ...
  2. Analyze keywords by Keyword Difficulty. ...
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  4. Grade yourself as you write with the SEO Writing Assistant.

What are the main types SEO? ›

12 Types of SEO
  • White-Hat SEO. When you hear someone say white-hat SEO, that means the SEO practices that are in-line with the terms and conditions of the major search engines, including Google. ...
  • Black-Hat SEO. ...
  • Gray-Hat SEO. ...
  • On-Page SEO. ...
  • Off-Page SEO. ...
  • Technical SEO. ...
  • International SEO. ...
  • Local SEO.
18 Oct 2022

What are basic SEO tools? ›

What follows is a list of essential technical SEO tools that every SEO professional should become familiar with.
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26 Aug 2018

Is SEO a hard job? ›

SEO is not that hard to learn, but it can be confusing and overwhelming to get started. Learning SEO means learning about a long list of individual digital marketing strategies, which can feel a bit like adding new weapons to your arsenal as you learn how to wield them.

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Average Rs 35,548 per month.

Is SEO a hard skill? ›

At the top of the list of important hard skills is search engine optimization. Data from the study is based on a LinkedIn poll conducted by Microsoft with 600 global senior marketer participants.

How much does a SEO beginner make? ›

According to Edoxi's salary trend report, which forecasts job salaries in digital marketing over the next year, U.S.-based, entry level SEO specialists can expect a starting SEO salary of $35K, mid-career SEO specialists are likely to earn closer to $47K, and experienced SEO specialists will earn on average $65K in ...

How much is a SEO paid? ›

Average SEO costs are $100-$250 an hour for US SEO agencies. SEO costs often range from $2,500 – $10,000 per month for US agencies. The average SEO plan costs $2819 per month (per Ahrefs) Overseas SEO companies may charge $10-$50 an hour.

Can I learn SEO without coding? ›

The short answer is: no, SEO typically doesn't require much (or any) hands-on coding. You can absolutely do a fine job of SEO without touching code.

How many days will it take to learn SEO? ›

It takes 1-3 months to learn the basics of SEO. The basics of search engine optimization can be understood and learnt within 3 months, however, the more advanced concepts can take anywhere from 6-18 months. This is provided you are consuming knowledge daily and learning from experts.

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Hiring an experienced SEO freelancer or agency by the hour typically costs anywhere between $50-$150 per hour. Of course, you can find people that charge significantly less or more than this hourly rate. For example, this SEO hourly rate breakdown found that 6% of SEO providers charge over $200/hour.

How much does SEO charge per hour? ›

The average hourly fee for an SEO specialist is between Rs 500 to Rs 1500, but here's the thing: SEO takes time to show results.

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SEO is not hard to learn if you start getting your basics right. You just need to be willing enough to allocate enough time and effort in the right directions when learning the SEO concepts. The main problem however is that people don't realize that SEO is a time taking process. It is not a one-day or a two-day affair.

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Steps to Become an SEO Expert
  1. Learn the basics of how search engines work.
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24 Aug 2022

Is SEO an in demand skill? ›

The answer to this question is a resounding “yes”. SEO or search engine optimization is one of the most in-demand skills in the marketing world today. Every business wants to be at the top of the search results pages when people are looking for their products or services online. That's where SEO specialists come in.

Can I learn SEO in one month? ›

While it's a career-long journey, you can indeed learn SEO in about a month — enough to make a huge impact on your website and thus your business as a whole.

Can I learn SEO in one day? ›

Yes. You can surely learn much of SEO in one day, but not everything. When you consider a day as 24 hours of reading and understanding then sure you can learn the basics well, learn about the techniques and what we do in it.

Can you do SEO for free? ›

There are tons of free SEO tools including Answer the Public, Ubersuggest and Google Analtyics. What's the best free SEO tool? Google's suite of tools are powerful and free. Google Analytics and Google Search console are must-haves.

Can you fail Google certification? ›

You may lose all Google certifications, You may be barred from taking or retaking any exam, and. Google, in its sole discretion, may choose to terminate any applicable business relationship with you, if any.

Is Google certificate enough to get a job? ›

Still, Google offers no guarantee of employment. And completing a course does not make you an expert, Pollak cautions. “You can't just go from having no skills to taking a six-month course in data analytics and then present yourself on your resume as a data analyst.”

Can I get a job with SEO certification? ›

Yes, you can. You don't necessarily need a certificate or a college degree for a job in SEO. While some potential hiring managers prefer to hire college graduates, you'll definitely find many who place more emphasis on your hands-on experience and technical skills.

Will SEO exist in 10 years? ›

One thing is sure, however: SEO will be around for a long time. As long as search engines exist and internet users continue to use keywords and phrases to find what they're looking for, the search engine business will continue.

Will SEO exist in 5 years? ›

1. Will SEO exist in 5 years? SEO is not going to be eliminated over the next five years because social media and search engines are more than likely going to merge. Facebook has already begun doing so, doing an average of more than 1.5 billion searches every day. Twitter also partnered with Google.

How do you become #1 in SEO? ›

Typically you get to number 1 by having a good online reputation. Well-known brands have good reputations. These brands rank at the top of Google, too. Reputation is increased by the number of quality web pages that link to any page on your site.

Should I learn Python for SEO? ›

Python is an amazing programming language that will help you become better SEOs. You can use python for SEO by dropping your reliance on Excel and stop using spreadsheets, by leveraging APIs, automating the boring tasks and by implementing machine learning algorithms.

What are main types of SEO? ›

12 Types of SEO
  • White-Hat SEO. When you hear someone say white-hat SEO, that means the SEO practices that are in-line with the terms and conditions of the major search engines, including Google. ...
  • Black-Hat SEO. ...
  • Gray-Hat SEO. ...
  • On-Page SEO. ...
  • Off-Page SEO. ...
  • Technical SEO. ...
  • International SEO. ...
  • Local SEO.
18 Oct 2022

What is SEO and its types? ›

The three kinds of SEO are: On-page SEO – Anything on your web pages – Blogs, product copy, web copy. Off-page SEO – Anything which happens away from your website that helps with your SEO Strategy- Backlinks. Technical SEO – Anything technical undertaken to improve Search Rankings – site indexing to help bot crawling.

What are SEO tools? ›

What Are SEO Tools? SEO tools provide data and alerts about the overall health and success of your website. They help uncover areas of opportunity and identify weaknesses or issues that may prevent you from ranking and earning visibility in the SERPs.

What is SEO example? ›

Search engine optimization (SEO) is the practice of getting targeted traffic to a website from a search engine's organic rankings. Common tasks associated with SEO include creating high-quality content, optimizing content around specific keywords, and building backlinks.

What is the golden rule of SEO? ›

The 'Golden Rule” of SEO is: Understand your customers' needs and create an SEO experience that satisfies those needs - from the search result listing, to the first impression the visitor gets when landing on the page, to the ability for the visitor to quickly and easily find the content she is looking for.

How does SEO make money? ›

Creating a popular blog via organic traffic and monetizing via ads and/or affiliate sales. Creating a popular website ranking in the search engines and selling related physical or digital eCommerce products. Creating a popular SEO blog and teaching what you've learned (and what works) with an SEO course.

What are SEO skills? ›

The job of a Search Engine Optimization specialist with SEO skills is to analyze and review websites and optimize in a way that they will be picked up by search engines. The SEO specialist aims to ensure increased traffic to a website by developing content with appropriate keywords and phrases.

Why is SEO important? ›

In short, SEO is crucial because it makes your website more visible, and that means more traffic and more opportunities to convert prospects into customers. Check out the SEO tools you can use for optimal ranking.

What is SEO in job? ›

A Search Engine Optimization or SEO Specialist tests, analyzes, and changes a website so it is optimized for search engines, and the website subsequently ranks higher in the search results on major search engines such as Google and Bing.

What is the SEO formula? ›

Companies can calculate SEO's return on investment by looking at search engine rankings, organic website traffic, and goal completions, and then using the ROI SEO formula: (Gain from Investment – Cost of Investment) / Cost of Investment.

What are the 12 things to know about SEO? ›

SEO Tips
  • Make the website about one thing. ...
  • Mention keywords where they matter most. ...
  • Link to internal pages on your site. ...
  • Use a permalink structure that includes keywords. ...
  • Remove anything that slows down your website. ...
  • Use keywords in your images. ...
  • Link to other websites with relevant content.

What are the two main points of SEO? ›

SEO is divided in two parts: On-page SEO and off-page SEO. On-page SEO refers to all techniques that can be implemented on your website to improve your ranking in SERP (search engine results pages), whereas off-page SEO refers to everything that can be done outside of your website to improve its visibility on the web.

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