SEO
Machine Learning Examples For The Real World
As an SEO professional, you’ve heard about ChatGPT and BARD – or even used them yourself.
You might also be familiar with a handful of machine learning models from Google, such as BERT and RankBrain.
These are all great applications of machine learning.
But it isn’t always immediately apparent how machine learning can be useful for your BAU (business as usual) tasks or daily SEO to-dos.
There’s also the question of ethics, as in data privacy and fairness, when it comes to using machine learning in your work.
However, understanding how machine learning works in search (and in real life) can only work to your advantage as an SEO pro – whether you’re technical or not.
In this comprehensive article, learn the types of machine learning and ethics associated with the technology, then take inspiration from seven machine learning examples that have had a great impact on our world.
As an added bonus, at the end of this article, explore eight machine learning examples directly applied to SEO.
What Is Machine Learning?
Machine learning is a subset of artificial intelligence (AI) which enables systems to learn and improve without the need to be explicitly programmed. This is achieved by using large data sets that train the parameters in the model.
Perhaps the most well-known examples of machine learning currently are ChatGPT and BARD – and while this post won’t be focusing on them, I decided to ask them both to explain to me what machine learning is. Here are their responses:
ChatGPT
“Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without explicit programming. In other words, it is a method of teaching computers to learn from the data they are given, and then to make decisions or predictions based on that learning.
The goal of machine learning is to develop algorithms that can learn patterns in data, and then use those patterns to make decisions or predictions about new data. This is done by training the machine learning algorithm on a dataset of known inputs and outputs, and then using that knowledge to make predictions on new, unseen data.
Some common applications of machine learning include image recognition, natural language processing, fraud detection, and recommendation systems.”
Screenshot from ChatGPT, April 2023
BARD
“Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
Machine learning can be used for a variety of tasks, including:
- Classification: Sorting data into categories. For example, a spam filter might classify emails as spam or not spam.
- Regression: Predicting a continuous value. For example, a weather forecast might predict the temperature tomorrow.
- Clustering: Grouping data points together based on their similarity. For example, a recommender system might cluster users together based on their past purchases.
- Anomaly detection: Identifying unusual data points. For example, a fraud detection system might identify transactions that are likely to be fraudulent.
Machine learning is a powerful tool that can be used to solve a variety of problems. However, it is important to note that machine learning algorithms are only as good as the data they are trained on. If the data is biased, the algorithm will be biased as well. Additionally, machine learning algorithms can be susceptible to overfitting, which is when the algorithm learns the training data too well and does not generalize well to new data.
Despite these challenges, machine learning is a rapidly growing field with a wide range of applications. As the amount of data available to us continues to grow, machine learning is likely to play an even greater role in our lives in the future.”
Screenshot from BARD, April 2023Supervised Learning
As the name suggests, in supervised learning, the model learns under supervision. It is trained with labeled data, and the output is manually reviewed.
The machine learns from the labeled data, which is then used to make future predictions.
Once the output is received, the model remembers it and will use it for the next operation.
Image from author, April 2023
Image from author, April 2023There are two main types of supervised learning: classification and regression.
Classification
Classification is when the output variable is categorical, with two or more classes that the model can identify; for example, true or false and dog or cat.
Examples of this include predicting whether emails are likely to be spam or whether an image is of a dog or cat.
In both of these examples, the model will be trained on data that is either classified as spam or not spam, and whether an image contains a dog or cat.
Regression
This is when the output variable is a real or continuous value, and there is a relationship between the variables. Essentially, a change in one variable is associated with a change that occurs in the other variable.
The model then learns the relationship between them and predicts what the outcome will be depending on the data it is given.
For example, predicting humidity based on a given temperature value or what the stock price is likely to be at a given time.
Unsupervised Learning
Unsupervised learning is when the model uses unlabeled data and learns by itself, without any supervision. Essentially, unlike supervised learning, the model will act on the input data without any guidance.
It does not require any labeled data, as its job is to look for hidden patterns or structures in the input data and then organize it according to any similarities and differences.
For example, if a model is given pictures of both dogs and cats, it isn’t already trained to know the features that differentiate both. Still, it can categorize them based on patterns of similarities and differences.
Image from author, April 2023There are also two main types of unsupervised learning: clustering and association.
Clustering
Clustering is the method of sorting objects into clusters that are similar to each other and belong to one cluster, versus objects that are dissimilar to a particular cluster and therefore belong in another.
Examples of this include recommendation systems and image classifying.
Association
Association is rule-based and is used to discover the probability of the co-occurrence of items within a collection of values.
Examples include fraud detection, customer segmentation, and discovering purchasing habits.
Semi-supervised Learning
Semi-supervised learning bridges both supervised and unsupervised learning by using a small section of labeled data, together with unlabeled data, to train the model. It, therefore, works for various problems, from classification and regression to clustering and association.
Semi-supervised learning can be used if there is a large amount of unlabeled data, as it only requires a small portion of the data to be labeled to train the model, which can then be applied to the remaining unlabeled data.
Google has used semi-supervised learning to better understand language used within a search to ensure it serves the most relevant content for a particular query.
Reinforcement Learning
Reinforcement learning is when a model is trained to return the optimum solution to a problem by taking a sequential approach to decision-making.
It uses trial and error from its own experiences to define the output, with rewards for positive behavior and negative reinforcement if it is not working towards the goal.
The model interacts with the environment that has been set up and comes up with solutions without human interference.
Human interference will then be introduced to provide either positive or negative reinforcement depending on how close to the goal the output is.
Examples include robotics – think robots working in a factory assembly line – and gaming, with AlphaGo as the most famous example. This is where the model was trained to beat the AlphaGo champion by using reinforcement learning to define the best approach to win the game.
Machine Learning Ethics
There is no doubt that machine learning has many benefits, and the use of machine learning models is ever-growing.
However, it’s important to consider the ethical concerns that come with using technology of this kind. These concerns include:
- The accuracy of a machine learning model and whether it will generate the correct output.
- Bias in the data that is used to train models, which causes a bias in the model itself, and, therefore, a bias in the outcome. If there is historical bias in data, that bias will often be replicated throughout.
- The fairness in outcomes and the overall process.
- Privacy – particularly with data that is used to train machine learning models – as well as the accuracy of the outcomes and predictions.
7 Machine Learning Examples In The Real World
1. Netflix
Netflix uses machine learning in a number of ways to provide the best experience for its users.
The company is also continually collecting large amounts of data, including ratings, the location of users, the length of time for which something is watched, if content is added to a list, and even whether something has been binge-watched.
This data is then used to further improve its machine learning models.
Content Recommendations
TV and movie recommendations on Netflix are personalized to each individual user’s preferences. To do this, Netflix deployed a recommendation system that considers previous content consumed, users’ most viewed genres, and content watched by users with similar preferences.
Auto-generated Thumbnails
Netflix discovered that the images used on the browse screen make a big difference in whether users watch something or not.
It, therefore, uses machine learning to create and display different images according to a user’s individual preferences. It does this by analyzing a user’s previous content choices and learning the kind of image that is more likely to encourage them to click.
These are just two examples of how Netflix uses machine learning on its platform. If you want to learn more about how it is used, you can check out the company’s research areas blog.
2. Airbnb
With millions of listings in locations across the globe at different price points, Airbnb uses machine learning to ensure users can find what they are looking for quickly and to improve conversions.
There are a number of ways the company deploys machine learning, and it shares a lot of details on its engineering blog.
Image Classification
As hosts can upload images for their properties, Airbnb found that a lot of images were mislabeled. To try and optimize user experience, it deployed an image classification model that used computer vision and deep learning.
The project aimed to categorize photos based on different rooms. This enabled Airbnb to show listing images grouped by room type and ensure the listing follows Airbnb’s guidelines.
In order to do this, it retrained the image classification neural network ResNet50, with a small number of labeled photos. This enabled it to accurately classify current and future images uploaded to the site.
Search Ranking
To provide a personalized experience for users, Airbnb deployed a ranking model that optimized search and discovery. The data for this model came from user engagement metrics such as clicks and bookings.
Listings started by being ordered randomly, and then various factors were given a weight within the model – including price, quality, and popularity with users. The more weight a listing had, the higher it would be displayed in listings.
This has since been optimized further, with training data including the number of guests, price, and availability also included within the model to discover patterns and preferences to create a more personalized experience.
3. Spotify
Spotify also uses several machine learning models to continue revolutionizing how audio content is discovered and consumed.
Recommendations
Spotify uses a recommendation algorithm that predicts a user’s preference based on a collection of data from other users. This is due to numerous similarities that occur between music types that clusters of people listen to.
Playlists are one way it can do this, using statistical methods to create personalized playlists for users, such as Discover Weekly and daily mixes.
It can then use further data to adjust these depending on a user’s behavior.
With personal playlists also being created in the millions, Spotify has a huge database to work with – particularly if songs are grouped and labeled with semantic meaning.
This has allowed the company to recommend songs to users with similar music tastes. The machine learning model can serve songs to users with a similar listening history to aid music discovery.
Natural Language
With the Natural Processing Language (NLP) algorithm enabling computers to understand text better than ever before, Spotify is able to categorize music based on the language used to describe it.
It can scrape the web for text on a particular song and then use NLP to categorize songs based on this context.
This also helps algorithms identify songs or artists that belong in similar playlists, which further helps the recommendation system.
4. Detecting Fake News
While AI tools such as machine learning content generation can be a source for creating fake news, machine learning models that use natural language processing can also be used to assess articles and determine if they include false information.
Social network platforms use machine learning to find words and patterns in shared content that could indicate fake news is being shared and flag it appropriately.
5. Health Detection
There is an example of a neural network that was trained on over 100,000 images to distinguish dangerous skin lesions from benign ones. When tested against human dermatologists, the model could accurately detect 95% of skin cancer from the images provided, compared to 86.6% by the dermatologists.
As the model missed fewer melanomas, it was determined to have a higher sensitivity and was continually trained throughout the process.
There is hope that machine learning and AI, together with human intelligence, may become a useful tool for faster diagnosis.
Other ways image detection is being used in healthcare include identifying abnormalities in X-rays or scans and identifying key markups that may indicate an underlying illness.
6. Wildlife Security
Protection Assistant for Wildlife Security is an AI system that is being used to evaluate information about poaching activity to create a patrol route for conservationists to help prevent poaching attacks.
The system is continually being provided with more data, such as locations of traps and sightings of animals, which helps it to become smarter.
The predictive analysis enables patrol units to identify areas where it is likely animal poachers will visit.
8 Machine Learning Examples In SEO
1. Content Quality
Machine learning models can be trained to improve the quality of website content by predicting what both users and search engines would prefer to see.
The model can be trained on the most important insights, including search volume and traffic, conversion rate, internal links, and word count.
A content quality score can then be generated for each page, which will help inform where optimizations need to be made and can be particularly useful for content audits.
2. Natural Language Processing
Natural Language Processing (NLP) uses machine learning to reveal the structure and meaning of text. It analyzes text to understand the sentiment and extract key information.
NLP focuses on understanding context rather than just words. It is more about the content around keywords and how they fit together into sentences and paragraphs, than keywords on their own.
The overall sentiment is also taken into account, as it refers to the feeling behind the search query. The types of words used within the search help to determine whether it is classified as having a positive, negative, or neutral sentiment.
The key areas of importance for NLP are;
- Entity – Words representing tangible objects such as people, places, and things that are identified and evaluated.
- Categories – Text separated into categories.
- Salience – How relevant the entity is.
Google has a free NLP API demo that can be used to analyze how text is seen and understood by Google. This enables you to identify improvements to content.
Recommendations In The World Of NLP
- NLP is also being used to review and understand anchor text that is used to link pages. Therefore, it is more important than ever to ensure anchor text is relevant and informative.
- Ensuring each page has a natural flow, with headings providing hierarchy and readability.
- Answering the question the article is querying as quickly as possible. Ensure that users and search engines can discover key information without making too much effort.
- Ensure you have the correct spelling and punctuation used to display authority and trustworthiness.
3. Google’s Models
AI and machine learning is used throughout Google’s many products and services. The most popular use of it in the context of search is to understand language and the intent behind search queries.
It’s interesting to see how things have evolved in search due to advancements in the technology used, thanks to machine learning models and algorithms.
Previously, the search systems looked for matching words only, which didn’t even consider misspellings. Eventually, algorithms were created to find patterns that identified misspellings and potential typos.
There have been several systems introduced throughout the last few years after Google confirmed in 2016 its intention to become a machine learning first company.
RankBrain
The first of these was RankBrain, which was introduced in 2015 and helps Google to understand how different words are related to different concepts.
This enables Google to take a broad query and better define how it relates to real-world concepts.
Google’s systems learn from seeing words used in a query on the page, which it can then use to understand terms and match them to related concepts to understand what a user is searching for.
Neural Matching
Neural matching was launched in 2018 and introduced to local search in 2019.
This helps Google understand how queries relate to pages by looking at the content on a page, or a search query, and understanding it within the context of the page content or query.
Most queries made today make use of neural matching, and it is used in rankings.
BERT
BERT, which stands for Bidirectional Encoder Representations from Transformers, launched in 2019 and is one of the most impactful systems Google has introduced to date.
This system enables Google to understand how combinations of words express different meanings and intent by reviewing the whole sequence of words on a page.
BERT is now used in most queries, as it helps Google understand what a user is looking for to surface the best results related to the search.
MUM
MUM, which means Multitask Unified Model, was introduced in 2021 and is used to understand languages and variations in search terms.
LaMBDA
Language Models for Dialog Application, or LaMDA for short, is the newest model and is used to enable Google to have fluid and natural conversations.
This uses the latest advancements to find patterns in sentences and correlations between different words to understand nuanced questions – and even predict which words are likely to come next.
4. Predictive Prefetching
By combining historical website data on user behavior with the capabilities of machine learning, some tools can guess which page a user is likely to navigate to next and begin prefetching the necessary resources to load the page.
This is known as predictive prefetching and can enhance website performance.
Predictive prefetching can also apply to other scenarios, such as forecasting pieces of content or widgets that users are most likely to view or interact with and personalizing the experience based on that information.
5. Testing
Running SEO A/B tests is one of the most effective ways to provide the SEO impact of changes, and the ability to generate statistically significant results is possible with the use of machine learning algorithms and neural networks.
SearchPilot is an example of SEO A/B testing that is powered by machine learning and neural network models.
Starting with a bucketing algorithm that creates statistically similar buckets of control and variant pages to perform tests on, a neural network model then forecasts expected traffic to the pages the test is being run on.
The neural network model, which is trained to account for any and all external influences such as seasonality, competitor activity, and algorithm updates, will also analyze the organic search traffic to the variant pages and identify how they perform against the control group throughout the test.
This also enables users to calculate whether any difference in traffic is statistically significant.
(Disclaimer: I work for SearchPilot.)
6. Internal Linking
Machine learning can help with internal linking in two ways:
- Updating broken links: Machine learning can crawl your site to spot any broken internal links and then replace them with a link to the best alternative page.
- Suggesting relevant internal linking: These tools can leverage big data to suggest relevant internal links during the article creation process and over time.
The other internal linking task is an internal link audit. This includes analyzing the number of internal links to a page, the placement of the links together with the anchor text, and the overall crawl depth of the page.
Anchor text classification can also be performed to identify the phrases used most frequently in alt text and categorize them based on topics and whether they are branded or non-branded terms.
7. Image Captioning For Alt Text
As SEO pros, we understand the importance of image alt text. They improve accessibility for people who use screen readers while also helping search engine crawlers understand the content of the page they are placed on.
Language vision models can be used to automatically caption images, therefore providing content that can be used as alt text. Image captioning is used to describe what is shown within an image in a single sentence.
Two models are used for image captioning, both as important as the other. The image-based model will start by extracting features from the image, while the language-based model will translate those features into a logical sentence.
An example of image captioning in the real world is the Pythia deep learning framework.
8. Other SEO tasks
Other articles worth reviewing focus on using deep learning to automate title tag optimization and intent classification using deep learning.
If you’re interested in how machine learning can be used in daily SEO tasks, this article by Lazarina Stoy is a must-read – and if you would like to play around with some super interesting scripts, this collection of Colab notebooks from Britney Muller is the perfect place to start.
In Conclusion
Machine learning isn’t limited to just ChatGPT and BARD.
There are many practical applications for machine learning, both in the real world and specifically in the world of SEO – and these are likely just the beginning.
And while it will be vital to remain cognizant of the ethical questions associated with machine learning, it has exciting implications for the future of SEO.
More resources:
Featured Image: Phonlamai Photo/Shutterstock
![SEO Salary Survey 2023 [Industry Research]](https://articles.entireweb.com/wp-content/uploads/2023/11/SEO-Salary-Survey-2023-Industry-Research-400x240.jpg "SEO Salary Survey 2023 [Industry Research]")
![SEO Salary Survey 2023 [Industry Research]](https://articles.entireweb.com/wp-content/uploads/2023/11/SEO-Salary-Survey-2023-Industry-Research-80x80.jpg "SEO Salary Survey 2023 [Industry Research]")
SEO
What It Really Is & How to Build One
Building a personal brand is undeniably hard work, but it isn’t as tricky as you might think.
I spoke with two influencers—Wes Kao and Matt Diggity—for their best tips on establishing a name for yourself online.
A personal brand is how people perceive you and what you’re known for. It’s the skills, experience, and values that give you an edge over others.
Neuroscientist Andrew Huberman is one example. He helms and hosts the science/health podcast Huberman Lab, lectures at Stanford Medicine, and has earned media mentions from the likes of BBC, TIME, and more.
Andrew’s personal brand is built on his credibility and areas of expertise. Many of his posts attract thousands of likes and hundreds of comments on X and LinkedIn.
If we want to dig deeper, Maven and altMBA co-founder Wes Kao has a somewhat alternative take on the definition:
In my opinion, it’s better to reframe ‘personal branding’ into ‘personal credibility.’ Personal branding has a superficial undertone. It assumes you have your work, then you tack on an artificial layer of ‘branding’ to shape perceptions.
She suggests that personal credibility is about substance: Showing people what you do, how you think, and how you can contribute. Wes adds:
In this way, you build deeper connections with people who believe in your work—which means stronger relationships, more control, and more opportunities.
In this podcast interview snippet with Nick Bennett, SparkToro’s Amanda Natividad echoes Wes’ sentiment:
People generally don’t like the term [personal brand] because it sounds disingenuous and icky. Acknowledging the existence of your personal brand is admitting that you care what others think about you, and that you find ways to manage those expectations at scale.
Wild as it sounds, building a solid personal brand gives you more control over your life.
A strong following could:
- Expand your realm of influence, particularly in your area of expertise (i.e., be viewed as a subject matter expert).
- Boost your credibility, in turn allowing you to promote your company/product better.
- Build a loyal following independent of the company you’re working for (or if you own that company, create more positive sentiment towards it).
- Open doors to job, networking, and investment opportunities.
Chiangmai SEO conference founder Matt Diggity shares some excellent points in his Facebook post on the topic, too.
There’s no linear path to building your personal brand.
As a precursor to the below steps, let’s first talk about finding your “voice.”
Wes and Matt both emphasize the importance of staying true to yourself. That means not crafting an online persona of who you think you should be.
I try to write like how I sound in person. Talking and writing are different media, so you shouldn’t try to match the two in a literal sense, but you want to capture your overall spirit. For example, I have a hint of snark in my writing because that’s how I sound in person.
Matt echoes this sentiment:
How I talk on the internet is how I talk IRL. If I’m not having a f**king blast on my YouTube videos, I won’t do them. It has to be fun.
Keep this idea in mind as you go through the steps below.
Step 1: Position yourself
Think of yourself as a product: What are your strengths, obsessions, and areas of expertise?
If you’re well-versed in technical SEO or a seasoned entrepreneur, these might be your unique selling points.
From there, double down on something you would be excited to think, write, and talk about for years—because “it will likely take years to get to where you want to go,” says Wes.
As an (optional) next step, consider solidifying your position with a spiky POV—a term coined by Wes, and which she cautions should be used with care.
A spiky POV is not about a contrarian hot take for the sake of it. In 2023, social platforms are flooded with hot takes and generic advice. I think about respecting the intelligence of my audience and teaching them something they don’t already know. A true spiky POV is rooted in deep expertise, including recognizing the limitations and counterpoints of your idea. This builds your reputation as someone who is rigorous and worth the time to engage with.
Here’s a LinkedIn post by Wes that combines all of the above: a unique perspective backed by her personal experiences, with a takeaway for the audience too. In other words—a spiky, worthy POV.
Step 2: Start sharing publicly
You already knew this, but social media platforms are one of the best ways to get growth and build your name. It’s your chance to build your reputation in a public arena.
Wes, Amanda, and Matt each utilized a combination of online channels to promote their voice and content. It’s one of the first things you should do—because your content is really only as good as its reach.
This is the first thing I did to build a personal brand and authority in the SEO industry, and I still do it to this day…
Take an hour a week, go to SEO social media hangouts (SEO Facebook groups, Twitter, LinkedIn, etc) and go from top-to-bottom answering people’s questions.…
— Matt Diggity (@mattdiggityseo) September 27, 2023
This doesn’t mean cross-posting your content across more platforms than you can manage, of course.
Study where your target audience spends most of their time, then hone in on those platforms (ideally, stick to no more than 2-3).
In Matt’s case, his followers are primarily on Twitter, Facebook, and YouTube—and that’s where his SEO-led content thrives.
If creating whole posts from scratch seems daunting, start by commenting thoughtfully in relevant online communities. Obviously, do it with heart:
This is the first thing I did to build a personal brand and authority in the SEO industry, and I still do it to this day…
Take an hour a week, go to SEO social media hangouts (SEO Facebook groups, Twitter, LinkedIn, etc) and go from top-to-bottom answering people’s questions.…
— Matt Diggity (@mattdiggityseo) September 27, 2023
Here are some simple ways to start.
LinkedIn: Contribute to a collaborative article
You might have seen these articles floating around LinkedIn—perhaps even been invited to add your insights to them.
These blog posts are similar to Wikipedia pages: LinkedIn users build on each AI-generated article with their perspectives, and readers can choose to react to these additions or engage with the content.
Here’s an example of what a contribution looks like:
Reddit: Weigh in on discussions
- Go to a relevant subreddit, e.g. r/bigSEO
- Sort by “Top” and “This Week”
- Browse the questions or discussions and offer your two cents where relevant.
Ride on trending topics
Found an interesting insight on X or someplace else? Turn it into a poll, question, or post. (Be sure to also tag and credit the author!)
Bring it all together
If some of your responses or posts get traction, repurpose those answers into new content: a blog post, video, or series of social posts.
(PSST: Learn more about my process behind curating and repurposing content for Ahrefs’ X account.)
This segues into our next and final step:
Step 3: Double down on what works
By now, you should have an idea of which topics you’re most comfortable discussing at length—and what resonates most with your target audience.
You can further maximize your reach by doubling down on the things that have brought you success. Or, more specifically, by repurposing popular content in other formats and creating more content about similar things.
For instance, we turned this popular video on how to use ChatGPT for SEO into a Twitter thread and LinkedIn post—and later, a blog post.
Wes has also done this plenty with her “eaten the bear” analogy over the years. She first wrote about it in this 2019 blog post, rewrote it in 2023, and shares variations of the analogy on LinkedIn and X every few months.
Each time, these posts garner hundreds or thousands of likes
Too much backstory is one of the biggest killers of good stories.
Backstory scope creep is real. We’ve all been there: Long-winded, stream of conscious explanations—all in the name of “giving context.“
I’ve been guilty of it myself.
The solution?
Minimum viable backstory pic.twitter.com/XFe2wAJysg
— Wes Kao 🏛 (@wes_kao) October 3, 2023
Don’t let your success die there, though. You can find more content ideas that will resonate with your audience by doing some keyword research around your topic. Here’s how:
- Plug your target topic into Ahrefs’ Keywords Explorer
- Go to the Matching terms report
For example, if we enter “chatgpt seo,” we see that people are searching for ChatGPT prompts for SEO and ChatGPT SEO extensions:
Given how our audience is interested in ChatGPT and SEO, these would be great topics to create content about—whether that be social media posts, videos, blog posts, or something else.
If you don’t have a paid account with us, you can plug your topic into our free keyword generator tool to view related phrases/questions.
Extra tips to build your personal brand
We mentioned some of these in some shape or form earlier, but they’re worth expanding on.
Maintain human connections
Who are you without the people who consume your content? Engage consistently with your followers and others’ content. Human connections are worth their weight in gold when you’re trying to get your personal brand off the ground.
Maintain consistency across your social media profiles
This means using the same profile picture across all platforms, and a standardized bio so others can quickly get a sense of who you are and what you often post about.
Jack Appleby is a great example. The creator/consultant is behind Future Social, an independent social strategy newsletter with 56,000+ subscribers.
Notice how he maintains consistency on X and LinkedIn:
Ahrefs’ Tim Soulo further explains the importance of your profile picture in personal branding here:
Your profile pic is your “personal branding” tool. (duh!)
My journey so far:
2009 – “I have no idea what I’m doing;“
2014 – I want to stand out & be memorable;
2018 – I want to look provocative;
2020 – I want to look professional.I can expand this into a thread if you want 😉 pic.twitter.com/W7FtZTcYGO
— Tim Soulo 🇺🇦 (@timsoulo) September 14, 2020
Be yourself
Remember how Wes and Matt shared the importance of staying true to yourself? We couldn’t emphasize that enough.
Final thoughts
These steps aren’t exhaustive, obviously. To truly stand out online, Wes suggests having a combination of these things: social proof, good design sense, strong writing, interesting insights, and a track record of contribution.
As she puts it:
All these things will make people think, ‘This person knows their craft.’
Have a thought about this blog post? Ping me on X.
SEO
SEO Salary Survey 2023 [Industry Research]
![SEO Salary Survey 2023 [Industry Research]](https://articles.entireweb.com/wp-content/uploads/2023/11/SEO-Salary-Survey-2023-Industry-Research.jpg "SEO Salary Survey 2023 [Industry Research]")
Editor’s note
Here are the top takeaways:
- The median annual salary for SEOs we polled was $49,211
- The highest annual salary we polled was $229,652
- To earn the higher salaries in SEO you need be a technical SEO expert—Heads of SEO, SEO Directors and SEO Leads all said that their main specialization was technical SEO
- Only 5.4% of respondents learned SEO through a course—most learned on the job (52.3%) or by themselves (42.3%)
- 36.9% described themselves as content specialists, 30.6% described themselves as technical specialists, 6.3% described themselves as specializing in link-building
- 49.5% of respondents worked in SEO agencies, 42.3% in-house and 8.2% were self-employed
- Most respondents (28.8%) worked in companies that had 11-50 people
- The average SEO experience of our respondents was 6.9 years
- Self-employed SEOs earned the most on average ($60,232k)—the median annual salary for in-house roles was slightly lower at $56,789, and agency SEOs had the lowest median annual salary at $44,169
There were also a few surprises:
- Few SEOs in our survey said that they specialized in link building compared to technical and content. This is despite the fact that links are still one of the most important Google ranking factors.
- The average level of experience between SEO Directors and Head of SEO is not that different—10.4 years for a SEO Director and 10.6 years for a Head of SEO—but the salary difference between the roles was ~$11,552—quite substantial.
Overview
| Role | Median annual salary ($USD) | Average experience (years) | Main specialization | Main work location |
|---|---|---|---|---|
| Head of SEO | $92,988 | 10.6 | Technical SEO | Agency and in-house |
| SEO Director | $81,436 | 10.4 | Technical SEO | Agency and in-house |
| SEO Lead | $38,289 | 7.4 | Technical SEO | Agency |
| SEO Specialist | $49,229 | 5.8 | Content | In-house |
| SEO Account Manager | $43,850 | 4.2 | Content | Agency |
| SEO Consultant | $49,240 | 6 | All-rounder | Agency |
| SEO Executive | $31,956 | 3 | All-rounder | Agency |
| SEO Analyst | $56,393 | 5 | All-rounder | In-house |
Here’s how annual salaries broke down across our respondents:
According to the SEOs we polled, most of them learned SEO on the job or were self-taught.
Most of our respondents had a couple of years of experience under their belts. The amount of experience Head of SEOs had versus SEO Directors was not that different, at around 10 years.
- Across all respondents, the average experience was 6.9 years
- For Head of SEO, the average experience was 10.6 years
- For SEO Director, the average experience was 10.4 years
- For SEO Lead, the average experience was 7.4 years
- For SEO Account Manager, the average experience was 4.2 years
- For SEO Consultant, the average experience was 6 years
- For SEO Executive, the average experience was 3 years
- For SEO Analyst, the average experience was 5 years
Technical and Content were the two top skills that SEOs we surveyed specialized in.
The proportion of SEOs that said they specialized in links was much lower despite links being a major ranking factor.
Our survey showed an almost 50/50 split between the UK and Europe. 48.6% of respondents were from the UK—perhaps not surprising given that BrightonSEO is based in the UK.
Most of the respondents we spoke to worked in agencies or in-house. It does mean, however, that our salary data was mainly focused on these two employment types.
Across all respondents, the most common company size was 11-50. A large proportion of SEOs also worked for substantially larger companies that had over 1000 employees.
Here’s the full breakdown of each role.
Head of SEO salary
It’s probably not too much of a surprise that the Head of SEO role was our highest-paying SEO role surveyed. What’s more of a surprise was the variation in salary—our survey showed that a Head of SEO can earn anything from ~$25k to ~$229k.
Average experience
According to our survey, a Head of SEO has ~10.6 years of experience.
Type of company
46.7% of respondents worked for an agency, and 46.7% worked in-house. 6.7% were self-employed.
Education
66.7% of respondents said they were self-taught, 26.7% said they’d learned on the job, and 6.7% said that they had learned SEO from a course.
Specialization
40% said that they specialized in technical SEO, 33.3% in Content, and 13.3% said they were a generalist. The remaining 13.4% said they focused on people management.
This is surprising, as it implies that 73.3% of people in Head of SEO roles are actively providing SEO services for their clients rather than focusing on managing a team.
Company size
There were two company sizes that were most popular for Head of SEOs to work in. 40% of respondents said they worked in companies with 11-50 people, and 20% said they worked in companies with over 1001 people.
Location
40% of respondents were from the UK, 13.3% were from the Netherlands, and the remainder were from mainland Europe.
SEO Director salary
The salary variation wasn’t quite as extreme for SEO Directors, but salaries ranged from ~$42k to ~$121k—still quite a difference.
Average experience
SEO Directors in our survey had 10.4 years of experience on average.
Type of company
There was a 50/50 split between SEO Directors’ backgrounds, with 50% from agency and 50% from in-house
Education
62.5% of SEO Directors described themselves as self-taught, and 37.5% said that they learned SEO on the job.
Specialization
75% of them specialized in technical SEO, whilst 25% described themselves as generalists or Other.
Company size
According to our survey, SEO Directors typically work in medium to large companies. 25% said that they worked in companies that had over 1000 people, and 25% said they worked in companies that had 51-100 employees.
Location
Most SEO Directors we surveyed were from the UK (62.5%). The rest were equal splits between India, the U.S., and Germany (12.5%).
SEO Lead salary
SEO Leads typically have a lot of experience, but our survey shows that they only earn slightly more on average than SEO Specialists.
Average experience
SEO Leads in our survey had 7.4 years of experience on average.
Type of company
50% of SEO Leads came from an agency background, 41.7% came from in-house, and 8.3% were self-employed.
Education
69.2% learned on the job, 23.1% were self-taught, and 7.7% learned SEO through a course.
Specialization
30.8% of SEO Leads specialized in technical SEO, 23% specialized in content, and 23.1% specialized in links. 15.4% described themselves as generalists. The remaining 7.7% described themselves as specializing in SEO strategy.
Company size
46.2% worked in companies that had 1001+ people, and the remaining 53.8% worked in smaller companies.
Location
23.1% of SEO Leads came from the UK, with the remainder coming from the Netherlands, Italy, and Sweden (15.4% each) and 30.7% from other European countries.
SEO Specialist salary
SEO Specialists we surveyed had around 5-6 years of experience, but they typically got paid better than SEO Leads. Based on my experience, this may be due to in-house roles paying better than agency roles in the UK.
Average experience
SEO Specialists in our survey had an average of 5.8 years of experience.
Type of company
41.2% of SEO Specialists came from an agency background, while 58.8% said that they were from an in-house background.
Education
58.8% of SEO Specialists said that they had learned SEO on the job, 35.3% said that they were self-taught, and 5.9% said that they had learned SEO through a course.
Specialization
52.9% of SEO Specialists specialized in content, 29.4% focused on technical, 11.8% described themselves as all-rounders, and 5.9% described specialized in links.
Company size
41.2% of SEO Specialists said that they worked in companies that had 11-50 people. Only 17.6% of respondents said that they worked in companies that had 1001+ people. 23.6% said they worked in companies between 51-500 people. The remaining 17.6% worked in smaller companies with less than 10 people.
Location
23.5% of SEO Specialists said that they were from the UK, with the remainder from Europe.
SEO Account Manager salary
SEO Account Managers in our survey were one of the most consistent salary bands earning between ~$40k and ~$55k.
Average experience
SEO Account managers in our survey had 4.2 years of experience on average.
Type of company
85.7% of respondents worked for an agency, and 14.3% worked in-house.
Education
71.4% of respondents said they learned SEO on the job, and 28.6% said they were self-taught.
Specialization
42.9% said that they specialized in content, 28.6% described themselves as an all-rounder, 14.3% said they were technical SEO, and the remaining 14.2% said they specialized in links.
Company size
42.9% of respondents said they worked in companies with 11-50 people, and 28.6% said they worked in companies with over 1001 people. The remaining 28.6% was split equally between people who worked in companies with between 2-11 people or 51-100 people.
Location
85.7% of respondents were from the UK, and 14.3% of the remainder were from Europe.
SEO Consultant salary
SEO Consultants we surveyed earned up to ~$87k, which was lower than I was expecting—because our SEO pricing post suggested that SEO consultants charge between $100-150 per hour.
But as the data is UK-focused, the likely reason for this is the £85k VAT tax threshold.
Average experience
SEO Consultants in our survey had 6 years of experience on average.
Type of company
63.3% of respondents worked for an agency, and 36.7% worked in-house.
Education
45.5% of respondents said they were self-taught, 36.4% said they’d learned on the job, and 9.1% said that they had learned SEO from a course. The remaining 9% said they’d learned from other ways.
Specialization
27.3% said that they specialized in technical SEO, 27.3% in content, and 27.3% said they were a generalist. The remaining 18.1% said they focused on management and strategy.
Company size
SEO Consultants typically worked on their own or in smaller agencies according to our survey — 36.4% of respondents said they worked on their own, and 27.3% said they worked in companies with 51-100 people. The remaining 36.3% said they worked in companies with between 2-50 people.
Location
36.4% of respondents were from the UK, 27.3% were from the Netherlands, and the remaining 36.3% were from Europe.
SEO Executive salary
Average experience
SEO Executives in our survey had 3 years of experience on average.
Type of company
80% of respondents worked for an agency, and 20% worked in-house.
Education
80% of respondents said they were self-taught, and 20% said they’d learned SEO from a course.
Specialization
40% said that they specialized in technical SEO, 20% in Content, and 40% said they were a generalist.
Company size
80% of respondents said they worked in companies with 11-50 people, and 20% said they worked in companies with 1001 or more people.
Location
80% of respondents were from the UK, and 20% were from Belgium.
SEO Analyst salary
SEO Analysts typically had a few more years of experience than SEO Executives, but it looks like they earned roughly the same as them.
Average experience
SEO Analysts in our survey had 5 years of experience on average.
Type of company
33.3% of respondents worked for an agency, and 66.7% worked In-house.
Education
33.3% of respondents said they were self-taught, and 66.7% said they’d learned on the job.
Specialization
33.3% said that they specialized in technical SEO, 33.3% in Content, and 33.3% said they specialized in News SEO.
Company size
33.3% of respondents said they worked in companies with 101-200 people, and 66.7% said they worked in companies with over 201 people.
Location
SEO Analysts came from a range of locations 33% of respondents were from Portugal, 33.3% were from Brazil, and the remainder were from Serbia.
Sidenote.
We didn’t get many respondents for the SEO Analyst role—so take these results with a pinch of salt.
Final thoughts
SEO salaries aren’t often discussed in detail within the industry, so getting a snapshot of their current state from one of the biggest SEO conferences in the UK was insightful.
For our next salary survey, we’ll be opening it up to all SEOs. If you’d like to take part—you can enter here.
Got questions? Ping me on X (formerly known as Twitter)
SEO
4 Tactics for High-Quality Backlinks That Move the Needle [+ Examples]
Many popular link building tactics produce low-quality links that don’t improve SEO performance.
Even if these techniques make an impact, it’s often for a short time, and Google can easily devalue them down the line.
Here are four tactics for building high-quality links that help you stay ahead of your competition, expose your brand to new audiences, and are less likely to be devalued in future algorithm updates.
Digital PR is the process of creating content that appeals to journalists and promoting it to them.
If they like the content, they’ll write a feature about it or include it in a piece they’re writing. This can land you many high-quality backlinks from big sites and news publications for free.
Examples
In the months following ChatGPT’s release, Fery Kaszoni and his team at Search Intelligence compiled statistics about Open AI’s popularity since launching ChatGPT and compared it to other popular platforms like Instagram and TikTok.
The result? 60+ free link placements, including mentions on Yahoo News (DR 92), The Wrap (DR 84), and Time magazine (DR 92).
In another campaign, Fery and his team calculated how much money beloved video characters would earn in real life. This campaign earned 20+ free links including a DR89 link from British newspaper, The Daily Express.
How to do it
Successful Digital PR requires some creativity, but this is the process in a nutshell:
- Find a trending topic
- Create relevant newsworthy content around that topic
- Tell journalists about it
For example, AI has been a major topic of conversation in all industries since it launched. Any new data or insights about it would go well in news cycles while it remains a topic of interest.
Once you have a topic, you need to come up with interesting content ideas that are relevant to your business.
This is the hard part. It’s really a case of brainstorming ideas until you land on something you think could be interesting.
For example, here are a few random content ideas for a company that sells furniture online:
- Have AI refurnish rooms from popular TV shows in new styles.
- Have AI design a new item of furniture, create it, and sell it.
- Ask 100 interior designers if they’re worried about AI taking their jobs, share the data.
After you find your winning idea, create the content, give it an attention-grabbing headline, and write a press release about the most interesting insights.
Then, promote your content to journalists. You can try services like Roxhill or Muck Rack to find journalists who might be interested in your content.
You can also use a tool like Ahrefs’ Content Explorer to find sites that have recently published content about your topic and reach out to them.
Here’s how to do that:
- Enter your topic into Content Explorer
- Filter for pages published in the last 90 days
- Filter for pages on DR70+ websites (big sites that you probably want links from)
For example, if we do this for the topic of “chatgpt,” we see thousands of well-known websites that have recently published about ChatGPT including Business Insider, Tech Republic, and Wired.
Data journalism is a way of enhancing or creating newsworthy content by analyzing unique data sets. It can fall under digital PR, though it typically requires more detailed research.
This technique works because reporters love a good statistic they can either quote or write an opinion piece about. Be the source of such data, and you can earn many high-quality links anytime your data becomes relevant to trending news topics.
Examples
Data journalism can be quite simple. For example, in another case study from Search Intelligence, Fery’s team used Ahrefs’ Keywords Explorer as a data source for a cybersecurity PR campaign.
The study reveals the top UK banks where customers seek help with fraud, allowing journalists to report on which banks are more secure than others.
The data fuelling these insights is keyword search volume. That’s it.
This method doesn’t take very long, doesn’t need a data scientist and can very easily be replicated in other industries where search popularity can unearth interesting insights.
In another example (and perhaps one of our all time favorites), marketing firm Yard created a data study comparing the CO2 emissions of various celebrities and ranking the worst offenders.
If you follow celebrity news, there’s no way you missed reports of Taylor Swift’s private jet emissions being among the highest compared to other celebrities.
Every single one of these news stories originated from the data study.
When the study was first released, it went viral and earned links from almost 2,000 referring domains within the first month.
But that’s not all.
This topic trended in news cycles again when rumours spread that Taylor Swift attended a Jets game to bury the original negative publicity about her private jet usage, earning Yard a well-deserved second round of links.
Today, this post has 1,861 links from 1,155 referring domains, 77% of them are dofollow, and 38.4% are higher than DR 60.
Talk about drool-worthy results! That’s high-quality link building done right.
How to do it
Successful data journalism is similar to digital PR but relies on the intriguing, data-backed insights you can unearth.
In a nutshell, the process looks like this:
- Find a data-driven content angle that gets links and media attention
- Gather data to provide new or updated insights on the topic
- Tell journalists about your findings
Start by considering “your money or your life” content angles that everyday folk care about. It’s easy to fall into the trap of thinking too narrow or pitching ideas only a small demographic may understand.
For instance, cybersecurity is not a sexy topic journalists or their readers will likely care about. There’s also not a high degree of literacy about the topic among the general population.
But everyone cares about whether their bank is secure and how safe their money is.
This concept needs no explanation and that’s exactly why data that helps answer the question “how safe is your bank?” worked exceptionally well as a link building tactic in the example above.
You can also use Content Explorer to gather more ideas like:
- Evergreen yet stale topics that you can update with more recent data
- Data you can visualize better or repurpose into a different content format
- Trending angles in other industries you can apply to your industry
For example, on the topic of ChatGPT, we found Rand Fishkin’s post claiming usage has declined 29% between May and August 2023 and that 30% of its usage is by programmers.
You don’t need original ideas to succeed. If you’ve got the data to back it up, you can easily take the angles of a “useage patterns” or “most popular audience segments” and apply them to popular tools in your industry.
Some decent data sources you can start with include:
- Search data: Like Ahrefs’ Keywords Explorer for uncovering interesting search patterns.
- Historical data: Like Google Trends for highlighting growth or decline patterns over time.
- Scientific research: Like on Google Scholar or in specific research journals.
- Public niche data: For instance, Yard’s study used the CelebrityJets Twitter page.
- Proprietary data: From within your (or your client’s) organization.
When you find an interesting insight or pattern worth sharing, write a press release about it and share it with journalists who frequently report on the topic.
Statistics pages are curated lists of facts and figures in a particular industry. These pages attract evergreen links for as long as the statistics remain relevant.
It’s one of our favorite link building tactics. Here’s how we’ve used it quite successfully over the years.
Example
We first launched a detailed list of SEO statistics in 2020 and it has been naturally earning high-quality links ever since.
Currently, the page has:
- 5,787 backlinks
- 2,282 referring domains
- 82% “dofollow” links
- 37.7% from DR 60+ websites
While we used some outreach techniques in the early days, most of the success has come from the page’s ability to maintain top position rankings for competitive keywords.
Do it right, and this tactic remains wildly effective for earning links naturally for many years.
How to do it
Start by entering a few broad topics related to your website into Ahrefs’ Keywords Explorer. For example, we might enter the following for Ahrefs:
- SEO
- Content marketing
- Link building
Then navigate to the Matching Terms report and apply the inclusion filter for things like stats, statistics, facts, or figures. Make sure your filter is set to include any of these phrases.
Then it’s just a matter of checking out the results to find a relevant topic you want to write about.
We went for “SEO statistics”:
Once you pick your topic, it’s a just matter of curating linkworthy stats and publishing them on a page.
While you can earn some seed links with early outreach efforts, long term success comes down to keeping your content updated with the latest data. That’s the best way to compound performance year on year, earning many high-quality links with no ongoing outreach needed.
Relationship-based link building prioritizes long-term relationships with journalists, writers, and editors.
It is an effective addition to digital PR campaigns as you can shortcut the time it takes to find the right people to distribute your content.
Better yet, you can be a journalist’s first point of call when they write a story on topics you or your clients are experts in.
Example
Imagine having journalists contact you asking to feature your clients in upcoming stories. That’s exactly what growth marketing firm, EngineRoom, has achieved.
A journalist from Mamamia (DR 78) made a call out on Sourcebottle, the Australian equivalent of HARO, seeking expert advice on immigration law. EngineRoom’s link building expert, Don Milne, responded and won the story along with a high-quality link.
Then, the real magic started.
Instead of ending things there, Don also shared a client list with the journalist in case they ever wanted to collaborate on future stories again.
Sure enough, a few weeks later, the journalist reached out, asking to connect with another client in the drug rehab space to develop a story on heroin addiction. The client is featured in about 30% of the completed article with detailed quotes from the founder and (of course) a link back to their website.
No pitching. No outreach. Just a genuine partnership and collaboration now earning multiple high-quality links for their clients.
How to do it
This technique is all about the follow-up after you collaborate on your first story with a journalist.
If getting the first foot in the door is where you’re stuck, you can check out our detailed guide on relationship-based link building by Irina Maltseva, the former Head of Marketing at Hunter.
Once you get that first story, make sure you keep the relationship going.
If you have a list of websites or clients you represent, create a professional document with a mini bio about each client. Make sure it’s also easily searchable for writers in a hurry and makes your contact details clear and easy to access.
Then, share it with journalists, writers, and editors you collaborate with so they can refer to it in the future if they need an expert on a specific topic for their content.
Final thoughts
Earning high-quality backlinks can be much easier than many people realize and cheaper too! All the examples shared in this post earned free link placements on high-authority websites and with minimal outreach.
These techniques have more staying power. They are also far less likely to be seen as “link manipulation” or devalued in future Google updates.
And, if you get your content angle just right, they also have the potential to be earning links many months, if not years, down the track!
Got questions? Ping me on LinkedIn.
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