In this post I’m going to show you the top SEO and AI terms you must know in 2023.
Why is this important?
Because SEO and AI are two of the most dynamic and fast-changing fields in digital marketing.
And if you want to stay ahead of the curve, you need to keep up with the latest trends, tools and techniques.
But don’t worry.
I’ve got you covered.
In this post, I’ll explain 15 key terms that every SEO and AI enthusiast should know.
And I’ll also show you some real-life examples and case studies of how these terms are used in practice.
So, let’s dive right in.
1. Natural Language Processing (NLP)
Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages.
NLP enables computers to understand, analyze and generate natural language texts and speech.
For example, NLP powers voice assistants like Siri and Alexa, chatbots like Drift and Intercom, and content generation tools like Jarvis and Copy.ai.
NLP is also a crucial component of SEO, as it helps search engines understand the meaning and intent of user queries and web pages.
For example, Google uses NLP to provide better search results for natural language queries, such as “how to make a cake” or “best movies of 2023”.
Google also uses NLP to analyze web pages and rank them based on their relevance, quality and authority.
For example, Google’s BERT algorithm uses NLP to better understand the context and nuance of words on a page.
This helps Google match web pages with user intent more accurately.
2. Machine Learning (ML)
Machine learning (ML) is a subset of artificial intelligence that enables computers to learn from data and experience without being explicitly programmed.
ML algorithms can analyze large amounts of data, identify patterns and make predictions based on their learning.
For example, ML powers recommendation systems like Netflix and Amazon, image recognition systems like Google Photos and Face ID, and spam detection systems like Gmail and Outlook.
ML is also a key factor in SEO, as it helps search engines improve their algorithms and provide better user experience.
For example, Google uses ML to rank web pages based on hundreds of factors, such as user behavior, content quality, site speed, backlinks and more.
Google also uses ML to personalize search results based on user preferences, location, device, history and more.
For example, Google’s RankBrain algorithm uses ML to understand complex queries and provide relevant results based on user intent.
3. Artificial Neural Networks (ANN)
Artificial neural networks (ANN) are computing systems that are inspired by the biological neural networks of the human brain.
ANNs consist of interconnected nodes or neurons that process information and transmit signals to other nodes.
ANNs can learn from data and perform complex tasks such as classification, regression, clustering, generation and more.
For example, ANNs power facial recognition systems like Facebook and Snapchat, natural language generation systems like GPT-3 and OpenAI Codex, and self-driving cars like Tesla and Waymo.
ANNs are also used in SEO, as they help search engines process natural language texts and images more efficiently and effectively.
For example, Google’s Neural Matching algorithm uses ANNs to better understand synonyms and related terms on web pages.
This helps Google provide more relevant results for queries that don’t match the exact words on a page.
4. Deep Learning (DL)
Deep learning (DL) is a branch of machine learning that uses multiple layers of artificial neural networks to learn from data and perform advanced tasks.
DL can handle large amounts of unstructured data such as text, images, audio and video.
DL can also learn from its own outputs and improve its performance over time.
For example, DL powers speech recognition systems like Google Assistant and Cortana, image generation systems like StyleGAN and DeepDream, and natural language understanding systems like BERT and ELMo.
DL is also important for SEO, as it helps search engines understand the meaning and context of web pages more deeply and accurately.
For example, Google’s MUM algorithm uses DL to answer complex queries that require multiple sources of information across different languages and formats.
5. Generative Adversarial Networks (GAN)
Generative adversarial networks (GAN) are a type of deep learning model that consists of two competing neural networks: a generator and a discriminator.
The generator tries to create realistic outputs such as images or texts based on some input data or noise.
The discriminator tries to distinguish between the real and fake outputs and provide feedback to the generator.
The generator and the discriminator learn from each other and improve their outputs over time.
For example, GANs power image synthesis systems like This Person Does Not Exist and Artbreeder, text synthesis systems like Grover and CTRL, and style transfer systems like Prisma and DeepArt.
GANs are also relevant for SEO, as they can help create high-quality content that can attract and engage users.
For example, GANs can generate realistic images or videos that can complement text content or replace stock photos.
GANs can also generate catchy headlines or summaries that can increase click-through rates and dwell time.
Sure, I’ll continue with some more terms for you.
6. Semantic Search
Semantic search is a type of search that aims to understand the meaning and intent of user queries and provide the most relevant and comprehensive results.
Semantic search goes beyond matching keywords and considers factors such as context, synonyms, natural language, user behavior and more.
For example, semantic search can provide different results for the same query depending on the user’s location, device, history and preferences.
Semantic search can also provide rich results such as featured snippets, knowledge graphs, people also ask boxes and more.
Semantic search is essential for SEO, as it helps optimize your website for user intent and satisfaction.
For example, semantic search can help you rank for long-tail keywords and voice searches that use natural language.
Semantic search can also help you provide useful information and answers to user questions on your web pages.
7. Computer Vision
Computer vision is a branch of artificial intelligence that enables computers to process and understand visual information such as images and videos.
Computer vision can perform tasks such as object detection, face recognition, scene analysis, optical character recognition and more.
For example, computer vision powers image search engines like Google Images and Pinterest, video analysis tools like YouTube and TikTok, and augmented reality applications like Snapchat and Instagram.
Computer vision is also relevant for SEO, as it helps optimize your website for visual content and user experience.
For example, computer vision can help you create alt text for your images that describe their content and context.
Computer vision can also help you analyze your videos and generate captions or transcripts that can improve their accessibility and SEO.
8. Voice Search
Voice search is a type of search that allows users to use their voice instead of typing to perform a query on a device such as a smartphone, tablet, smart speaker or computer.
Voice search is powered by speech recognition technology that converts spoken words into text and then executes the query.
Voice search is becoming more popular and convenient for users, especially with the rise of smart speakers like Amazon Echo and Google Home.
Voice search is also important for SEO, as it affects how users search and what they expect from the results.
For example, voice search tends to use more natural language and longer phrases than text search.
Voice search also tends to be more conversational and question-based than text search.
Voice search also tends to provide more direct and specific answers than text search.
Therefore, SEO for voice search requires optimizing your website for natural language, long-tail keywords, user intent and rich results.
9. Sentiment Analysis
Sentiment analysis is a type of natural language processing that analyzes the emotions and opinions expressed in a text or speech.
Sentiment analysis can identify whether a text or speech is positive, negative or neutral, and also detect the intensity and polarity of the sentiment.
Sentiment analysis can also extract specific aspects or topics that are being discussed and evaluate their sentiment.
For example, sentiment analysis can help businesses monitor customer feedback, reviews, social media posts and more.
Sentiment analysis can also help marketers understand their audience’s preferences, needs and pain points.
Sentiment analysis is also useful for SEO, as it can help optimize your website for user satisfaction and loyalty.
For example, sentiment analysis can help you create content that resonates with your target audience and addresses their pain points.
Sentiment analysis can also help you measure the performance of your content and identify areas for improvement.
10. Entity Recognition
Entity recognition is a type of natural language processing that identifies and extracts named entities from a text or speech.
Named entities are words or phrases that refer to specific people, places, organizations, dates, events and more.
For example, entity recognition can extract entities such as “Barack Obama”, “New York City”, “Apple Inc.”, “January 1st, 2023” and “Super Bowl” from a text or speech.
Entity recognition can help organize and categorize information, as well as link entities to other sources of knowledge.
For example, entity recognition can help create knowledge graphs that show the relationships between entities and provide additional information about them.
Entity recognition is also beneficial for SEO, as it helps optimize your website for semantic search and rich results.
For example, entity recognition can help you structure your data using schema markup that can enhance your web pages with rich snippets, knowledge panels, carousels and more.
Entity recognition can also help you optimize your content for voice search and featured snippets that can answer user questions directly.
11. Content Marketing.
Content marketing is a type of marketing that involves creating and distributing valuable, relevant and consistent content to attract and retain a clearly defined audience and ultimately drive profitable customer action.
Content marketing can include various types of content, such as blog posts, ebooks, videos, podcasts, infographics, webinars and more.
Content marketing can help businesses achieve various goals, such as increasing brand awareness, generating leads, building trust, educating customers and driving conversions.
Content marketing is also vital for SEO, as it helps optimize your website for user engagement and retention.
For example, content marketing can help you create high-quality content that matches user intent and provides useful information and answers.
Content marketing can also help you promote your content and earn backlinks from authoritative sources that can boost your website’s ranking and traffic.
12. Data Science
Data science is a multidisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data.
Data science can involve various skills and techniques, such as statistics, mathematics, programming, machine learning, data mining, data visualization and more.
Data science can help businesses solve complex problems, make data-driven decisions and create innovative products and services.
Data science is also important for SEO, as it helps analyze and interpret large amounts of data related to user behavior, search trends, website performance and more.
For example, data science can help you identify SEO opportunities and challenges, measure the impact of your SEO strategies and optimize your website for better results.
13. Web Scraping
Web scraping is a technique that involves extracting data from websites using automated tools or scripts.
Web scraping can help collect large amounts of data from various sources, such as product listings, customer reviews, competitor prices and more.
Web scraping can help businesses gain insights, conduct research, monitor trends and make data-driven decisions.
Web scraping is also useful for SEO, as it can help gather and analyze data related to keywords, backlinks, rankings and more.
For example, web scraping can help you perform keyword research by extracting relevant keywords from your competitors’ websites.
Web scraping can also help you perform backlink analysis by extracting the URLs and anchor texts of the links pointing to your website or your competitors’ websites.
14. Natural Language Generation (NLG)
Natural language generation (NLG) is a type of artificial intelligence that enables computers to generate natural language texts or speech based on some input data or information.
NLG can create various types of content, such as summaries, headlines, captions, reports, stories and more.
NLG can also create content in different languages, styles and tones.
For example, NLG powers content creation tools like Jarvis and Copy.ai, which can help users write blog posts, emails, ads and more.
NLG also powers voice assistants like Siri and Alexa, which can generate natural language responses to user queries.
NLG is also relevant for SEO, as it can help create high-quality content that can attract and engage users.
For example, NLG can help you create catchy headlines or meta descriptions that can increase click-through rates and dwell time.
NLG can also help you create unique content that can avoid duplicate content issues and improve your website’s ranking and authority.
Chatbots are software applications that use artificial intelligence and natural language processing to simulate human conversations with users via text or voice.
Chatbots can provide various services, such as customer support, product recommendations, booking reservations and more.
Chatbots can also interact with users on various platforms, such as websites, mobile apps, social media and messaging apps.
Chatbots can help businesses improve customer satisfaction, engagement and loyalty.
Chatbots can also help businesses reduce costs, save time and increase sales.
Chatbots are also relevant for SEO, as they can help optimize your website for user experience and retention.
For example, chatbots can help you provide instant answers to user queries and guide them through your website.
Chatbots can also help you collect user feedback, preferences and behavior data that can help you improve your website and content.
16. Image Recognition
Image recognition is a type of computer vision that enables computers to identify and classify objects, faces, scenes and activities in images.
Image recognition can perform tasks such as face detection, facial recognition, object detection, scene recognition and more.
Image recognition can help businesses provide various services, such as security, authentication, personalization and more.
Image recognition can also help businesses create innovative products and features, such as filters, stickers, emojis and more.
Image recognition is also important for SEO, as it helps optimize your website for visual content and user experience.
For example, image recognition can help you create alt text for your images that describe their content and context.
Image recognition can also help you analyze your images and optimize them for size, quality and format.
Adam is a Technical SEO associate at 1stpage, a company that helps clients improve their SEO performance with high-quality and relevant links. He shares his expertise on technical website optimization and outreach link building. He explains how these strategies can boost your online visibility, traffic, and conversions. He is also a nomad who enjoys traveling and discovering new places. You can always find him near a city that you want to visit.