Entity seo

With digital marketing spends soaring, there’s now an even bigger need for reducing costs per customer acquisition. On average, a brand can reduce its acquisition costs by 55% if they drive significant traffic and transactions via organic search and great content marketing. This session will provide you with tactics to implement a robust, data driven content and SEO strategy that drives ROI.

Patrick's Brighton SEO talk on using machine learning for technical SEO and how to automate many things.

In this talk, delivered at BrightonSEO in April 2023, Will Critchlow, founder and CEO of SearchPilot covers a method for generating practically-unlimited SEO A/B test ideas. Going on a journey from what SEO has been to what it needs to become, Will covers the mindset and strategy shifts needed, as well as the tactical implementation details. Download resources including detailed guides to SEO testing, and the free tool he uses to generate the ideas (plus explainer video).

Crawl budget refers to the number of pages a site is allowed to request that Google crawls on a daily basis. It is important because exceeding the crawl budget can lead to pages not being indexed. The document provides tips on how to identify a site's current crawl rate, issues impacting crawl budget like errors and duplicate content, and strategies for optimizing demand and capacity such as improving site speed and creating fresh content regularly. The goal is identifying any crawl issues and optimizing the crawl budget to have the most important pages indexed.

The document discusses using automated text summarization techniques to generate quality content at scale from user-generated content like online product reviews. It proposes a technical plan to download Amazon reviews, remove duplicate sentences using neural semantic textual similarity, and then generate frequently asked questions and corresponding FAQ schema by feeding the review text into a neural question generation model. The goal is to leverage user content and machine learning to automatically create helpful content for websites.

This document summarizes several patents related to query parsing and semantic search. It describes patents for multi-stage query processing, query breadth, query analysis, midpage query refinements (search suggestions), context vectors, and categorical quality (re-ranking search results based on the category of the query). Each patent is briefly described, including inventors, filing dates, and some technical details. The document aims to provide an overview of the evolution of semantic search and query understanding technologies at Google.

The document discusses featured snippets in Google search results. It begins by explaining what featured snippets are and their value for searchers. It then provides tips for developing a featured snippet strategy, including focusing keyword research on question keywords and optimizing content with headers, images, and schema markup. The document concludes by emphasizing the importance of keyword research and checking all SEO best practices to start winning featured snippets.

40 Deep #SEO Insights for 2023: -In 2022, I told to focus on Natural Language Generation, and it happened. -In 2023, F-O-C-U-S on "Information Density, Richness, and Unique Added Value" with Microsemantics. I call the collection of these, "Information Responsiveness". 1/40 🧵. 1. PageRank Increases its Prominence for Weighting Sources Reason: #AI and automation will bloat the web, and the real authority signals will come from PageRank, and Exogenous Factors. The expert-like AI content and real expertise are differentiated with historical consistency. 2. Indexing and relevance thresholds will increase. Reason: A bloated web creates the need for unique value to be added to the web with real-world expertise and organizational signals. The knowledge domain terms, or #PageRank, will be important in the future of a web source. 3. AI and #automation filters will be created. Reason: Google needs to filter the websites that publish 500 articles a day on multiple topics to find non-expert websites. This is already happening. 4. #Google will start to make mistakes in filtering websites that use spam and AI. Reason: The need for AI-generated content filtration forced Google to check and audit "momentum", in other words, content publication frequency. I used the "momentum" first in TA Case Study. 5. Google uses #Author Vectors, and Author Recognition. Reason: LLMs use certain types of language styles and word sequences by leaving a watermark behind them. It is easy to understand which websites do not use a real expert for their articles, and content to differentiate. 6. #Microsemantics will be the name of the next game. Reason: The bloating on the web will create bigger web document clusters, and being a representative source will be more important. Thus, micro-differences inside the content will create higher unique value. 7. Custom #LLMs will be rented. Reason: Custom and unique LLMs will be trained and rented to the people who try to create 100 websites with 100,000 content items per website. NLP in SEO will show its true monetary value in mid-2023. 8. Advanced Semantic SEO will be a must for every SEO. Reason: 20 years of websites will lose their rankings to the new websites that come with 60,000 articles. This creates the need for advanced #Semantics and Lingusitics capabilities for SEOs. 9. Cost-of-retrieval will be a base concept for #SEO, as TA. Reason: TA explains a big portion of how the web works. Information Responsiveness and Cost-of-retrieval will complete it further. For two books, I will be publishing only these two concepts. 10. Google Keys Reason: The biggest Google leak after Quality Rater Guidelines will happen in 2023. And, I will be involved, but no more information, for now, I am not allowed to share more. Check the slides for the next SEO Insights for 2023. #searchengineoptimization #future #nlp #semantic #chatgpt #ai #content #quality #publishing #trend #seotrend #seo #searchengineoptimisation

In this webinar, I will go through the benefits and limitations of Data Studio, tips and tricks for turning spreadsheets into cool reports, and share some hot dashboard templates

The document discusses Google's ML APIs versus OpenAI's APIs and their applications for SEO and digital marketing tasks. It provides examples of how natural language processing APIs from Google and OpenAI can be used for tasks like text analysis, sentiment analysis, document classification, translation and content transformation. While both Google and OpenAI APIs are useful, the document recommends choosing the right API for each specific task based on its capabilities and limitations in order to get the best results.

There are three main components of information retrieval systems: query understanding, document-query relevance understanding, and document clustering and ranking. The path from a search query to a search document involves several steps like query parsing, processing, augmenting, scoring, ranking, and clustering. Query understanding is where search engine optimization (SEO) begins, while document creation and ranking are other areas where SEO is applied. Cranfield experiments in the late 1950s helped develop the concept of a "search query language" which is different from the language used in documents. Formal semantics and components like tense, aspect, and mood can help machines better understand human language for information retrieval tasks.

This document discusses using machine learning to optimize paid search incrementality. It outlines limitations with traditional approaches that analyze paid and organic search performance separately or through one-time tests. The presented solution uses machine learning to build a full picture of search data and continuously optimize bids to maximize the incremental value of paid search while minimizing costs. Case studies demonstrate how the approach provides unique insights into paid and organic relationships and answers questions about budget efficiencies.

This document discusses how to identify expansion opportunities for businesses through local SEO data. It outlines a 6-step process: 1) identify potential markets, 2) filter markets down using data, 3) identify specific opportunities in the filtered markets, 4) narrow locations further by analyzing competitors and keywords, 5) forecast growth potential for the narrowed locations, and 6) present location options and forecasts to the client to obtain buy-in for expansion. The goal is to objectively evaluate markets and present well-researched location ideas that align with the client's goals for growth.

Patrick Stox gives a presentation on how search works. He discusses how Google crawls and indexes websites, processes content, handles queries, and ranks results. Some key points include: Google's crawler downloads pages and files from websites; processing includes duplicate detection, link parsing, and content analysis; queries are understood through techniques like spelling correction and query expansion; and search results are ranked based on numerous freshness, popularity, and relevancy signals.

Interesting insights from log files are often kept within silos, not shared with content teams. This prevent content teams from reaching their full potential. Learn how they can improve crawling and indexing though leveraging insights from log file analysis, all in real-time.

Web servers can often feel overwhelming - but optimising your servers can be critical to unlocking better SEO performance. In this talk, Ash will guide you through the vital concepts every SEO needs to grasp to improve server speed, with a specific focus on improving TTFB. Empowering you with the knowledge to make smarter back-end technical recommendations.

Canonicalization is a process that webmasters use to tell search engines which URL is the preferred version for a page that may have duplicate content across different URLs. It helps search engines understand which version of a page should be considered the original and primary version for things like search rankings. Properly implementing canonicalization can help avoid duplicate content penalties and ensure the right URL receives credit in search results.

This article delves into the concepts of Semantic SEO, Topical Authority, and PageRank, exploring their relationships and how they benefit both website owners and search engines. By leveraging Natural Language Processing (NLP) techniques, Semantic SEO improves search engine comprehension of content and enhances user experience, ultimately leading to better search results. In the ever-evolving world of Search Engine Optimization (SEO), understanding the intricate connections between Semantic SEO, Topical Authority, and PageRank is crucial for webmasters, content creators, and marketers. These concepts play a vital role in enhancing the visibility and relevance of websites in search results. Semantic SEO: Going Beyond Keywords Semantic SEO involves optimizing content by focusing on the meaning and context of words, phrases, and sentences rather than merely targeting specific keywords. This is achieved through NLP techniques such as topic modeling, sentiment analysis, and entity recognition, which allow search engines to comprehend the true essence of content. Topical Authority: Establishing Expertise and Trustworthiness Topical Authority refers to the perceived expertise of a website or content creator in a specific subject area. By producing high-quality, relevant, and in-depth content, websites can establish themselves as authorities, earning the trust of both users and search engines. This translates into higher search rankings and increased visibility. PageRank: Measuring the Importance of Webpages PageRank is an algorithm used by Google to determine the significance of a webpage by analyzing the quality and quantity of its inbound links. A higher PageRank implies that a website is more authoritative and valuable, thus warranting a better position in search results. The Interrelation of Semantic SEO, Topical Authority, and PageRank Semantic SEO, Topical Authority, and PageRank are interconnected concepts that work in tandem to improve a website's search performance. By focusing on Semantic SEO, content creators can enhance their Topical Authority and establish a solid online presence. This, in turn, can lead to higher PageRank and improved search visibility. The Benefits of Semantic SEO for Search Engines Semantic SEO not only benefits website owners but also search engines by reducing the cost of understanding documents. With the help of NLP techniques, search engines can efficiently analyze and comprehend content, making it easier to identify and index relevant webpages. This ultimately leads to more accurate search results and a better user experience. In conclusion, embracing Semantic SEO, Topical Authority, and PageRank is essential for achieving higher search rankings and increased online visibility. By leveraging NLP techniques, Semantic SEO offers a more sophisticated and efficient approach to understanding and optimizing content, ultimately benefiting both website owners and search engines.