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Machine Learning Examples For The Real World

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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.

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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.

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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.”

BARD definition of machine learningScreenshot from BARD, April 2023

Supervised 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.

Supervised learning visualImage from author, April 2023
Supervised predictive learning visualImage from author, April 2023

There 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.

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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.

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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.

Unsupervised learning visualImage from author, April 2023

There 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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(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.

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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.

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More resources:


Featured Image: Phonlamai Photo/Shutterstock



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SEO

An In-Depth Guide And Best Practices For Mobile SEO

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Mobile SEO: An In-Depth Guide And Best Practices

Over the years, search engines have encouraged businesses to improve mobile experience on their websites. More than 60% of web traffic comes from mobile, and in some cases based on the industry, mobile traffic can reach up to 90%.

Since Google has completed its switch to mobile-first indexing, the question is no longer “if” your website should be optimized for mobile, but how well it is adapted to meet these criteria. A new challenge has emerged for SEO professionals with the introduction of Interaction to Next Paint (INP), which replaced First Input Delay (FID) starting March, 12 2024.

Thus, understanding mobile SEO’s latest advancements, especially with the shift to INP, is crucial. This guide offers practical steps to optimize your site effectively for today’s mobile-focused SEO requirements.

What Is Mobile SEO And Why Is It Important?

The goal of mobile SEO is to optimize your website to attain better visibility in search engine results specifically tailored for mobile devices.

This form of SEO not only aims to boost search engine rankings, but also prioritizes enhancing mobile user experience through both content and technology.

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While, in many ways, mobile SEO and traditional SEO share similar practices, additional steps related to site rendering and content are required to meet the needs of mobile users and the speed requirements of mobile devices.

Does this need to be a priority for your website? How urgent is it?

Consider this: 58% of the world’s web traffic comes from mobile devices.

If you aren’t focused on mobile users, there is a good chance you’re missing out on a tremendous amount of traffic.

Mobile-First Indexing

Additionally, as of 2023, Google has switched its crawlers to a mobile-first indexing priority.

This means that the mobile experience of your site is critical to maintaining efficient indexing, which is the step before ranking algorithms come into play.

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Read more: Where We Are Today With Google’s Mobile-First Index

How Much Of Your Traffic Is From Mobile?

How much traffic potential you have with mobile users can depend on various factors, including your industry (B2B sites might attract primarily desktop users, for example) and the search intent your content addresses (users might prefer desktop for larger purchases, for example).

Regardless of where your industry and the search intent of your users might be, the future will demand that you optimize your site experience for mobile devices.

How can you assess your current mix of mobile vs. desktop users?

An easy way to see what percentage of your users is on mobile is to go into Google Analytics 4.

  • Click Reports in the left column.
  • Click on the Insights icon on the right side of the screen.
  • Scroll down to Suggested Questions and click on it.
  • Click on Technology.
  • Click on Top Device model by Users.
  • Then click on Top Device category by Users under Related Results.
  • The breakdown of Top Device category will match the date range selected at the top of GA4.
Screenshot from GA4, March 2024

You can also set up a report in Looker Studio.

  • Add your site to the Data source.
  • Add Device category to the Dimension field.
  • Add 30-day active users to the Metric field.
  • Click on Chart to select the view that works best for you.
A screen capture from Looker Studio showing a pie chart with a breakdown of mobile, desktop, tablet, and Smart TV users for a siteScreenshot from Looker Studio, March 2024

You can add more Dimensions to really dig into the data to see which pages attract which type of users, what the mobile-to-desktop mix is by country, which search engines send the most mobile users, and so much more.

Read more: Why Mobile And Desktop Rankings Are Different

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How To Check If Your Site Is Mobile-Friendly

Now that you know how to build a report on mobile and desktop usage, you need to figure out if your site is optimized for mobile traffic.

While Google removed the mobile-friendly testing tool from Google Search Console in December 2023, there are still a number of useful tools for evaluating your site for mobile users.

Bing still has a mobile-friendly testing tool that will tell you the following:

  • Viewport is configured correctly.
  • Page content fits device width.
  • Text on the page is readable.
  • Links and tap targets are sufficiently large and touch-friendly.
  • Any other issues detected.

Google’s Lighthouse Chrome extension provides you with an evaluation of your site’s performance across several factors, including load times, accessibility, and SEO.

To use, install the Lighthouse Chrome extension.

  • Go to your website in your browser.
  • Click on the orange lighthouse icon in your browser’s address bar.
  • Click Generate Report.
  • A new tab will open and display your scores once the evaluation is complete.
An image showing the Lighthouse Scores for a website.Screenshot from Lighthouse, March 2024

You can also use the Lighthouse report in Developer Tools in Chrome.

  • Simply click on the three dots next to the address bar.
  • Select “More Tools.”
  • Select Developer Tools.
  • Click on the Lighthouse tab.
  • Choose “Mobile” and click the “Analyze page load” button.
An image showing how to get to Lighthouse within Google Chrome Developer Tools.Screenshot from Lighthouse, March 2024

Another option that Google offers is the PageSpeed Insights (PSI) tool. Simply add your URL into the field and click Analyze.

PSI will integrate any Core Web Vitals scores into the resulting view so you can see what your users are experiencing when they come to your site.

An image showing the PageSpeed Insights scores for a website.Screenshot from PageSpeed Insights, March 2024

Other tools, like WebPageTest.org, will graphically display the processes and load times for everything it takes to display your webpages.

With this information, you can see which processes block the loading of your pages, which ones take the longest to load, and how this affects your overall page load times.

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You can also emulate the mobile experience by using Developer Tools in Chrome, which allows you to switch back and forth between a desktop and mobile experience.

An image showing how to change the device emulation for a site within Google Chrome Developer ToolsScreenshot from Google Chrome Developer Tools, March 2024

Lastly, use your own mobile device to load and navigate your website:

  • Does it take forever to load?
  • Are you able to navigate your site to find the most important information?
  • Is it easy to add something to cart?
  • Can you read the text?

Read more: Google PageSpeed Insights Reports: A Technical Guide

How To Optimize Your Site Mobile-First

With all these tools, keep an eye on the Performance and Accessibility scores, as these directly affect mobile users.

Expand each section within the PageSpeed Insights report to see what elements are affecting your score.

These sections can give your developers their marching orders for optimizing the mobile experience.

While mobile speeds for cellular networks have steadily improved around the world (the average speed in the U.S. has jumped to 27.06 Mbps from 11.14 Mbps in just eight years), speed and usability for mobile users are at a premium.

Read more: Top 7 SEO Benefits Of Responsive Web Design

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Best Practices For Mobile Optimization

Unlike traditional SEO, which can focus heavily on ensuring that you are using the language of your users as it relates to the intersection of your products/services and their needs, optimizing for mobile SEO can seem very technical SEO-heavy.

While you still need to be focused on matching your content with the needs of the user, mobile search optimization will require the aid of your developers and designers to be fully effective.

Below are several key factors in mobile SEO to keep in mind as you’re optimizing your site.

Site Rendering

How your site responds to different devices is one of the most important elements in mobile SEO.

The two most common approaches to this are responsive design and dynamic serving.

Responsive design is the most common of the two options.

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Using your site’s cascading style sheets (CSS) and flexible layouts, as well as responsive content delivery networks (CDN) and modern image file types, responsive design allows your site to adjust to a variety of screen sizes, orientations, and resolutions.

With the responsive design, elements on the page adjust in size and location based on the size of the screen.

You can simply resize the window of your desktop browser and see how this works.

An image showing the difference between Web.dev in a full desktop display vs. a mobile display using responsive design.Screenshot from web.dev, March 2024

This is the approach that Google recommends.

Adaptive design, also known as dynamic serving, consists of multiple fixed layouts that are dynamically served to the user based on their device.

Sites can have a separate layout for desktop, smartphone, and tablet users. Each design can be modified to remove functionality that may not make sense for certain device types.

This is a less efficient approach, but it does give sites more control over what each device sees.

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While these will not be covered here, two other options:

  • Progressive Web Apps (PWA), which can seamlessly integrate into a mobile app.
  • Separate mobile site/URL (which is no longer recommended).

Read more: An Introduction To Rendering For SEO

Interaction to Next Paint (INP)

Google has introduced Interaction to Next Paint (INP) as a more comprehensive measure of user experience, succeeding First Input Delay. While FID measures the time from when a user first interacts with your page (e.g., clicking a link, tapping a button) to the time when the browser is actually able to begin processing event handlers in response to that interaction. INP, on the other hand, broadens the scope by measuring the responsiveness of a website throughout the entire lifespan of a page, not just first interaction.

Note that actions such as hovering and scrolling do not influence INP, however, keyboard-driven scrolling or navigational actions are considered keystrokes that may activate events measured by INP but not scrolling which is happeing due to interaction.

Scrolling may indirectly affect INP, for example in scenarios where users scroll through content, and additional content is lazy-loaded from the API. While the act of scrolling itself isn’t included in the INP calculation, the processing, necessary for loading additional content, can create contention on the main thread, thereby increasing interaction latency and adversely affecting the INP score.

What qualifies as an optimal INP score?

  • An INP under 200ms indicates good responsiveness.
  • Between 200ms and 500ms needs improvement.
  • Over 500ms means page has poor responsiveness.

and these are common issues causing poor INP scores:

  1. Long JavaScript Tasks: Heavy JavaScript execution can block the main thread, delaying the browser’s ability to respond to user interactions. Thus break long JS tasks into smaller chunks by using scheduler API.
  2. Large DOM (HTML) Size: A large DOM ( starting from 1500 elements) can severely impact a website’s interactive performance. Every additional DOM element increases the work required to render pages and respond to user interactions.
  3. Inefficient Event Callbacks: Event handlers that execute lengthy or complex operations can significantly affect INP scores. Poorly optimized callbacks attached to user interactions, like clicks, keypress or taps, can block the main thread, delaying the browser’s ability to render visual feedback promptly. For example when handlers perform heavy computations or initiate synchronous network requests such on clicks.

and you can troubleshoot INP issues using free and paid tools.

As a good starting point I would recommend to check your INP scores by geos via treo.sh which will give you a great high level insights where you struggle with most.

INP scores by GeosINP scores by Geos

Read more: How To Improve Interaction To Next Paint (INP)

Image Optimization

Images add a lot of value to the content on your site and can greatly affect the user experience.

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From page speeds to image quality, you could adversely affect the user experience if you haven’t optimized your images.

This is especially true for the mobile experience. Images need to adjust to smaller screens, varying resolutions, and screen orientation.

  • Use responsive images
  • Implement lazy loading
  • Compress your images (use WebP)
  • Add your images into sitemap

Optimizing images is an entire science, and I advise you to read our comprehensive guide on image SEO how to implement the mentioned recommendations.

Avoid Intrusive Interstitials

Google rarely uses concrete language to state that something is a ranking factor or will result in a penalty, so you know it means business about intrusive interstitials in the mobile experience.

Intrusive interstitials are basically pop-ups on a page that prevent the user from seeing content on the page.

John Mueller, Google’s Senior Search Analyst, stated that they are specifically interested in the first interaction a user has after clicking on a search result.

Examples of intrusive interstitial pop-ups on a mobile site according to Google.

Not all pop-ups are considered bad. Interstitial types that are considered “intrusive” by Google include:

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  • Pop-ups that cover most or all of the page content.
  • Non-responsive interstitials or pop-ups that are impossible for mobile users to close.
  • Pop-ups that are not triggered by a user action, such as a scroll or a click.

Read more: 7 Tips To Keep Pop-Ups From Harming Your SEO

Structured Data

Most of the tips provided in this guide so far are focused on usability and speed and have an additive effect, but there are changes that can directly influence how your site appears in mobile search results.

Search engine results pages (SERPs) haven’t been the “10 blue links” in a very long time.

They now reflect the diversity of search intent, showing a variety of different sections to meet the needs of users. Local Pack, shopping listing ads, video content, and more dominate the mobile search experience.

As a result, it’s more important than ever to provide structured data markup to the search engines, so they can display rich results for users.

In this example, you can see that both Zojirushi and Amazon have included structured data for their rice cookers, and Google is displaying rich results for both.

An image of a search result for Japanese rice cookers that shows rich results for Zojirushi and Amazon.Screenshot from search for [Japanese rice cookers], Google, March 2024

Adding structured data markup to your site can influence how well your site shows up for local searches and product-related searches.

Using JSON-LD, you can mark up the business, product, and services data on your pages in Schema markup.

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If you use WordPress as the content management system for your site, there are several plugins available that will automatically mark up your content with structured data.

Read more: What Structured Data To Use And Where To Use It?

Content Style

When you think about your mobile users and the screens on their devices, this can greatly influence how you write your content.

Rather than long, detailed paragraphs, mobile users prefer concise writing styles for mobile reading.

Each key point in your content should be a single line of text that easily fits on a mobile screen.

Your font sizes should adjust to the screen’s resolution to avoid eye strain for your users.

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If possible, allow for a dark or dim mode for your site to further reduce eye strain.

Headers should be concise and address the searcher’s intent. Rather than lengthy section headers, keep it simple.

Finally, make sure that your text renders in a font size that’s readable.

Read more: 10 Tips For Creating Mobile-Friendly Content

Tap Targets

As important as text size, the tap targets on your pages should be sized and laid out appropriately.

Tap targets include navigation elements, links, form fields, and buttons like “Add to Cart” buttons.

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Targets smaller than 48 pixels by 48 pixels and targets that overlap or are overlapped by other page elements will be called out in the Lighthouse report.

Tap targets are essential to the mobile user experience, especially for ecommerce websites, so optimizing them is vital to the health of your online business.

Read more: Google’s Lighthouse SEO Audit Tool Now Measures Tap Target Spacing

Prioritizing These Tips

If you have delayed making your site mobile-friendly until now, this guide may feel overwhelming. As a result, you may not know what to prioritize first.

As with so many other optimizations in SEO, it’s important to understand which changes will have the greatest impact, and this is just as true for mobile SEO.

Think of SEO as a framework in which your site’s technical aspects are the foundation of your content. Without a solid foundation, even the best content may struggle to rank.

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  • Responsive or Dynamic Rendering: If your site requires the user to zoom and scroll right or left to read the content on your pages, no number of other optimizations can help you. This should be first on your list.
  • Content Style: Rethink how your users will consume your content online. Avoid very long paragraphs. “Brevity is the soul of wit,” to quote Shakespeare.
  • Image Optimization: Begin migrating your images to next-gen image formats and optimize your content display network for speed and responsiveness.
  • Tap Targets: A site that prevents users from navigating or converting into sales won’t be in business long. Make navigation, links, and buttons usable for them.
  • Structured Data: While this element ranks last in priority on this list, rich results can improve your chances of receiving traffic from a search engine, so add this to your to-do list once you’ve completed the other optimizations.

Summary

From How Search Works, “Google’s mission is to organize the world’s information and make it universally accessible and useful.”

If Google’s primary mission is focused on making all the world’s information accessible and useful, then you know they will prefer surfacing sites that align with that vision.

Since a growing percentage of users are on mobile devices, you may want to infer the word “everywhere” added to the end of the mission statement.

Are you missing out on traffic from mobile devices because of a poor mobile experience?

If you hope to remain relevant, make mobile SEO a priority now.


Featured Image: Paulo Bobita/Search Engine Journal

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HARO Has Been Dead for a While

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HARO Has Been Dead for a While

Every SEO’s favorite link-building collaboration tool, HARO, was officially killed off for good last week by Cision. It’s now been wrapped into a new product: Connectively.

I know nothing about the new tool. I haven’t tried it. But after trying to use HARO recently, I can’t say I’m surprised or saddened by its death. It’s been a walking corpse for a while. 

I used HARO way back in the day to build links. It worked. But a couple of months ago, I experienced the platform from the other side when I decided to try to source some “expert” insights for our posts. 

After just a few minutes of work, I got hundreds of pitches: 

So, I grabbed a cup of coffee and began to work through them. It didn’t take long before I lost the will to live. Every other pitch seemed like nothing more than lazy AI-generated nonsense from someone who definitely wasn’t an expert. 

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Here’s one of them: 

Example of an AI-generated pitch in HAROExample of an AI-generated pitch in HARO

Seriously. Who writes like that? I’m a self-confessed dullard (any fellow Dull Men’s Club members here?), and even I’m not that dull… 

I don’t think I looked through more than 30-40 of the responses. I just couldn’t bring myself to do it. It felt like having a conversation with ChatGPT… and not a very good one! 

Despite only reviewing a few dozen of the many pitches I received, one stood out to me: 

Example HARO pitch that caught my attentionExample HARO pitch that caught my attention

Believe it or not, this response came from a past client of mine who runs an SEO agency in the UK. Given how knowledgeable and experienced he is (he actually taught me a lot about SEO back in the day when I used to hassle him with questions on Skype), this pitch rang alarm bells for two reasons: 

  1. I truly doubt he spends his time replying to HARO queries
  2. I know for a fact he’s no fan of Neil Patel (sorry, Neil, but I’m sure you’re aware of your reputation at this point!)

So… I decided to confront him 😉 

Here’s what he said: 

Hunch, confirmed ;)Hunch, confirmed ;)

Shocker. 

I pressed him for more details: 

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I’m getting a really good deal and paying per link rather than the typical £xxxx per month for X number of pitches. […] The responses as you’ve seen are not ideal but that’s a risk I’m prepared to take as realistically I dont have the time to do it myself. He’s not native english, but I have had to have a word with him a few times about clearly using AI. On the low cost ones I don’t care but on authority sites it needs to be more refined.

I think this pretty much sums up the state of HARO before its death. Most “pitches” were just AI answers from SEOs trying to build links for their clients. 

Don’t get me wrong. I’m not throwing shade here. I know that good links are hard to come by, so you have to do what works. And the reality is that HARO did work. Just look at the example below. You can tell from the anchor and surrounding text in Ahrefs that these links were almost certainly built with HARO: 

Example of links build with HARO, via Ahrefs' Site ExplorerExample of links build with HARO, via Ahrefs' Site Explorer

But this was the problem. HARO worked so well back in the day that it was only a matter of time before spammers and the #scale crew ruined it for everyone. That’s what happened, and now HARO is no more. So… 

If you’re a link builder, I think it’s time to admit that HARO link building is dead and move on. 

No tactic works well forever. It’s the law of sh**ty clickthroughs. This is why you don’t see SEOs having huge success with tactics like broken link building anymore. They’ve moved on to more innovative tactics or, dare I say it, are just buying links.

Sidenote.

Talking of buying links, here’s something to ponder: if Connectively charges for pitches, are links built through those pitches technically paid? If so, do they violate Google’s spam policies? It’s a murky old world this SEO lark, eh?

If you’re a journalist, Connectively might be worth a shot. But with experts being charged for pitches, you probably won’t get as many responses. That might be a good thing. You might get less spam. Or you might just get spammed by SEOs with deep pockets. The jury’s out for now. 

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My advice? Look for alternative methods like finding and reaching out to experts directly. You can easily use tools like Content Explorer to find folks who’ve written lots of content about the topic and are likely to be experts. 

For example, if you look for content with “backlinks” in the title and go to the Authors tab, you might see a familiar name. 😉 

Finding people to request insights from in Ahrefs' Content ExplorerFinding people to request insights from in Ahrefs' Content Explorer

I don’t know if I’d call myself an expert, but I’d be happy to give you a quote if you reached out on social media or emailed me (here’s how to find my email address).

Alternatively, you can bait your audience into giving you their insights on social media. I did this recently with a poll on X and included many of the responses in my guide to toxic backlinks.

Me, indirectly sourcing insights on social mediaMe, indirectly sourcing insights on social media

Either of these options is quicker than using HARO because you don’t have to sift through hundreds of responses looking for a needle in a haystack. If you disagree with me and still love HARO, feel free to tell me why on X 😉



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Google Clarifies Vacation Rental Structured Data

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Google updates their vacation rental structured data documentation

Google’s structured data documentation for vacation rentals was recently updated to require more specific data in a change that is more of a clarification than it is a change in requirements. This change was made without any formal announcement or notation in the developer pages changelog.

Vacation Rentals Structured Data

These specific structured data types makes vacation rental information eligible for rich results that are specific to these kinds of rentals. However it’s not available to all websites. Vacation rental owners are required to be connected to a Google Technical Account Manager and have access to the Google Hotel Center platform.

VacationRental Structured Data Type Definitions

The primary changes were made to the structured data property type definitions where Google defines what the required and recommended property types are.

The changes to the documentation is in the section governing the Recommended properties and represents a clarification of the recommendations rather than a change in what Google requires.

The primary changes were made to the structured data type definitions where Google defines what the required and recommended property types are.

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The changes to the documentation is in the section governing the Recommended properties and represents a clarification of the recommendations rather than a change in what Google requires.

Address Schema.org property

This is a subtle change but it’s important because it now represents a recommendation that requires more precise data.

This is what was recommended before:

“streetAddress”: “1600 Amphitheatre Pkwy.”

This is what it now recommends:

“streetAddress”: “1600 Amphitheatre Pkwy, Unit 6E”

Address Property Change Description

The most substantial change is to the description of what the “address” property is, becoming more descriptive and precise about what is recommended.

The description before the change:

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PostalAddress
Information about the street address of the listing. Include all properties that apply to your country.

The description after the change:

PostalAddress
The full, physical location of the vacation rental.
Provide the street address, city, state or region, and postal code for the vacation rental. If applicable, provide the unit or apartment number.
Note that P.O. boxes or other mailing-only addresses are not considered full, physical addresses.

This is repeated in the section for address.streetAddress property

This is what it recommended before:

address.streetAddress Text
The full street address of your vacation listing.

And this is what it recommends now:

address.streetAddress Text
The full street address of your vacation listing, including the unit or apartment number if applicable.

Clarification And Not A Change

Although these updates don’t represent a change in Google’s guidance they are nonetheless important because they offer clearer guidance with less ambiguity as to what is recommended.

Read the updated structured data guidance:

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Vacation rental (VacationRental) structured data

Featured Image by Shutterstock/New Africa

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