Whilst passage indexing may seem like a small tweak to search ranking, it is potentially much more symptomatic of the beginning of a fundamental shift in the way that search engines understand unstructured content, determine relevance in natural language, and rank efficiently and effectively.
It could also be a means of assessing overall quality of content and a means of dynamic index pruning. We will look at the landscape, and also provide some takeaways for brands and business owners looking to improve quality in unstructured content overall in this fast changing landscape.
Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increa...Dawn Anderson MSc DigM
Structured data accounts for only a small part of the web and the problem grows as the volume of the online content grows. Schema markup is a drop in the ocean to help with this. However, things are being addressed in the natural language research space in the form of dense retrieval and other developments such as Sentence BERT and FAISS. Utilising heuristics such as umbrellas and sidecar pages will help to send clues and assist with ensuring search engines rank the right pages from your sites for SEO
The Python Cheat Sheet for the Busy MarketerHamlet Batista
What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
7 E-Commerce SEO Mistakes & How to Fix Them #DeepSEOConAleyda Solís
Avoid the most common SEO issues, challenges and mistakes by going through this presentation with tips, criteria and tools to use independently of your online store Web platform, and grow your organic search results
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
Natural Semantic SEO - Surfacing Walnuts in Densely Represented, Every Increa...Dawn Anderson MSc DigM
Structured data accounts for only a small part of the web and the problem grows as the volume of the online content grows. Schema markup is a drop in the ocean to help with this. However, things are being addressed in the natural language research space in the form of dense retrieval and other developments such as Sentence BERT and FAISS. Utilising heuristics such as umbrellas and sidecar pages will help to send clues and assist with ensuring search engines rank the right pages from your sites for SEO
The Python Cheat Sheet for the Busy MarketerHamlet Batista
What percentage of an Inbound marketer's day doesn't involve working with spreadsheets? How much of this work is time-consuming and repetitive? In this interactive session, you will learn how to manipulate Google Sheets to automate common data analysis workflows using Python, a very easy to use programming language.
Opinion-based Article Ranking for Information Retrieval Systems: Factoids and...Koray Tugberk GUBUR
How Search Engines Leverage Opinion-based Articles for Ranking?
Search engines use opinions, and factoids to understand the consensus. News search engines use different reports, and opinions in their search results to satisfy the urgent news information needed by the newsreaders. The news search engines differentiate disinformation from information to protect the newsreaders. Google, Microsoft Bing, Yandex, and DuckDuckGo have different algorithms and prioritization for classifications of the news sources, or prioritization of the news, and newsworthy topics.
Corroboration of the Web Answers from the Open Web is a research paper from Amelia Marian and Minji Wu explaining how a search engine can rank information according to its accuracy.
Google started to explain that the Expertise-Authoriteveness-Trustworthiness is the most important group of signals to be sure that a result won't shame the search engine. Embarrassment factors for the search engines involve wrong information on a news title on the news story, or a wrong featured snippet. A search engine might be shame due to the bad result that is ranking on the SERP.
Dense-retrieval, context scoring, named entity recognition, semantic role labeling, truth ranges, fix points, confidence score, query processing, and parsing.
Context understanding requires processing the text, and tokenizing the words by recognizing the word sense. Processing the text of the news articles requires time. And, most of the time, news search engines do not have enough time for processing the text. Thus, PageRank provides a sustainable timeline for the news sources for rankings.
PageRank is a quick signal for search engines to show the authenticity of the news web source. The highly cited sources are ranked higher, and longer on the top stories. Usually, Google protects the high PageRank sources by trusting the judgment of the websites. But, fact-finding algorithms do not use PageRank mostly, unless they couldn't decide by looking at other factors, or they do not have enough resources to process the text among the hundreds of sources.
News ranking algorithms differentiate opinions, reports, and breaking news from each other. News-related entities, their co-existence, and contextual relations change. Google inventors suggest differentiation of these entities from each other for a proper news categorization.
News categorization is important to match the interested topics of the users in queryless news feeds such as Google Discover. Google Discover is a queryless news feed that serves news stories according to the users' interest areas.
An opinion for news might be misleading. Some news titles might be too harsh, or strict. Search engines use these headlines to differentiate the non-trustworthy news sources from the trustworthy ones. And, opinions of journalists or their different interpretations of the events might change the rankings of a document according to the fact-finding algorithms.
Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEOKoray Tugberk GUBUR
Query Processing is the process of query term weight calculation, query augmentation, query context defining, and more. Query understanding and Query clustering are related to Information Retrieval tasks for the search engines. To provide a better search engine optimization effort and project result, the organic search performance optimizers need to implement query processing methodologies. Digital marketing and SEO are connected to each other. Understanding a query includes query parsing, query rewriting, question generation, and answer pairing. Multi-stages Query Processing, Candidate Answer Passages, or Candidate Answer Passages and Answer Term Weighting are some of the concepts from the Google Search Engine to parse the queries.
The presentation of The Secret Life of Queries, Parsing, Rewriting & SEO has been presented at the Brighton SEO Event in April 2022. The event speech focused on explaining the theoretical SEO and practical SEO examples together.
Query Processing methodologies are beyond synonym matching or synonym finding. It involves multiple aspects of the words, and meanings of the words. The theme of words, the centrality of words, attention windows, context windows, and word co-occurrence matrices, GloVe, Word2Vec, word embeddings, character embeddings, and more.
Themes of words contain the word probability like in Continues Bag of Window.
The search engine optimization community focuses on keyword research by matching the queries. Query processing involves query word order change, query word type change, query word combination change, query phrase synonym usage, query question generation, query clustering. Query processing and document processing are correlational. Query processing is to understand a query while document processing is to process a web document. Both of the processes are for ranking algorithms. Providing a better ranking algorithm requires a better query understanding. And providing better rankings as SEOs require better search engine understanding. Thus, understanding the methods of query processing is necessary.
Search Query Processing is implementing the query processing for thesearch engines. Search query refers to the phrase that search engine users use for searching. Search intent understanding and search intent grouping are two different things. But, query templates, questions templates, and document templates work together. Search query is for organic search behaviors. A web search engine answers millions of queries every day. Search query processing is a fundamental task for search engine optimization and search engine result page optimization.
The "Semantic Search Engine: Query Processing" slides from Koray Tuğberk GÜBÜR supported the presentation of "Search Query Processing: The Secret Life of Queries, Parsing, Rewriting & SEO". The presentation has been created by Dear Rebecca Berbel.
Many thanks to the Google engineers that created the Semantic Search Engine patents including Larry Page.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
7 E-Commerce SEO Mistakes & How to Fix Them #DeepSEOConAleyda Solís
Avoid the most common SEO issues, challenges and mistakes by going through this presentation with tips, criteria and tools to use independently of your online store Web platform, and grow your organic search results
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
The Quickest Win in SEO – How to do Internal Linking the Right WayMartin Hayman
This was a talk from BrightonSEO September 2021 and covers not only the importance of internal linking but also how to do it. It also covers a number of examples and additional tips.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
How to Implement Machine Learning in Your Internal Linking Audit - Lazarina S...LazarinaStoyanova
In this talk, Lazarina will break down the key areas that an internal linking audit should look into and go over opportunities for embedding machine learning in a way that is beginner-friendly for SEOs without extensive coding experience. Lazarina will share:
– How to analyse a site’s existing internal linking structure using machine learning
– What machine learning techniques can you implement to help you create content clusters?
– How to find user-friendly, in-content linking opportunities
– How to prioritise and measure the impact of internal linking initiatives
Lazarina will also touch upon how to use this process to identify other opportunities for site optimization, which can improve the user experience and search potential.
Related Blog post published at: https://lazarinastoy.com/how-to-incorporate-machine-learning-in-internal-linking-audits/
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.
Accessibility, strategy and schema - do they go hand in hand? Beth Barnham Br...BethBarnham1
In this talk, I explore schema and its link to online accessibility. Can schema really help the web to be more accessible? And how should we strategise this as SEOs? Strategy plays a vital role in SEO but often times the technical areas are overlooked within a wider marketing strategy
How to Incorporate ML in your SERP Analysis, Lazarina Stoy -BrightonSEO Oct, ...LazarinaStoyanova
How are you currently doing SERP analysis? Is your approach efficient or scalable?
How SEOs can incorporate programmatic approaches and advances in machine learning in order to identify winning strategies?
This talk will leave you with a better understanding of what is possible for SERP analysis at scale, what insights you can capture with the help of machine learning quickly, and how to incorporate insights into your strategy and visualize your findings to impress your stakeholders.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
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.
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
Coronavirus and Future of SEO: Digital Marketing and Remote CultureKoray Tugberk GUBUR
I have attended a great SEO and Digital Marketing webinar with Founder of Stradiji and SEMRush Turkey Lead Mr. Mert Erkal and My Dearest Friend and SEO Consultant Atakan Erdoğan.
Small Note: After I uploaded the presentation, Google launched a new Covid-19 news address like Bing/covid-19. You may want to look at it -> https://www.google.com/covid-19
I have prepared a Presentation about Coronavirus's Effects on Search Engine Optimization (SEO).
You will find Coronavirus's changing effects on Digital Marketing and psychology of global society while using Search Engines.
I also have focused on Search Engine's and Social Media Brands, E-commerce Site's reflexes against Coronavirus Pandemic.
You will see the web sites and categories who earn more traffic and lose traffic. You will also see conversion rate differences because of Coronavirus.
Also, I have told about Search Engine's differences and their attitude against the Coronavirus Pandemic, their future, their updates during the pandemic.
In the last part, you will see some new 2020 Web Technology and Design Trends with AI.
There are also Google Researches for better Search Engine technologies.
Questions:
1- What are the differences between Yandex, Google, Bing, and Duckduckgo for Coronavirus Pandemic?
2- Twitter, Instagram, Amazon or Apple, what are they doing?
3- What do people search most for during the Coronavirus Crisis?
4- What changes from country to country?
5- What are the future technologies of Web and App?
6- How and why do Search Engines improve AI, what is the last events?
7- Which sites loose traffic and which earn more?
8- Lots of quotes from International SEOs about the pandemic.
And more...
I am Koray Tuğberk GÜBÜR and a Holistic SEO Expert.
I sincerely thank you for my Dearest Friend Atakan Erdoğan and Mr. Mert Erkal for this awesome webinar opportunity and experience.
To watch the webinar, please visit Stradiji's Official Youtube Channel.
https://www.youtube.com/watch?v=V4sJTNcRqaM&t=100s
How to Use Search Intent to Dominate Google DiscoverFelipe Bazon
In this talk you will learn how search intent can help you benefit from the growing popularity of Google Discover. You’ll get actionable tips, a case study example and exclusive data from SEMrush.
Automate The Technical SEO Stuff by Michael Van Den Reym
In this talk, Michael will show you how to automate technical SEO tasks. You will learn how to schedule and compare crawls to spot technical errors faster, how to use RPA to speed up technical SEO audits and how to automatically optimize images. You will get inspired to execute technical SEO better and faster.
How to convince even the pickiest editors to take SEO more seriously :: brigh...Ian Helms
Let's face it: Editorial teams may not prioritize SEO, but it's an essential aspect of online content creation. Editorial teams are under constant pressure to produce timely and relevant content, often overlooking the long-term benefits of search-friendly, evergreen articles. While breaking news and current events are crucial for driving immediate traffic, organic-focused content can provide long-term value by continuously attracting visitors through search engines. During this presentation, Ian will share his historical experience working with editorial teams and how he successfully incorporates SEO into their workflow. You'll learn how they address common pain points and use data to generate enthusiasm for organic search.
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
The Quickest Win in SEO – How to do Internal Linking the Right WayMartin Hayman
This was a talk from BrightonSEO September 2021 and covers not only the importance of internal linking but also how to do it. It also covers a number of examples and additional tips.
Semantic Content Networks - Ranking Websites on Google with Semantic SEOKoray Tugberk GUBUR
Semantic Content Networks are the semantic networks of things with relations, directed graphs, attributes and facts. Every declaration, and proposition for semantic search represent a factual repository. Open Information Extraction is a methodology for creation of a semantic network. The Knowledge Base and Knowledge Graph are connected things to each other in terms of factual repository usage. The Knowledge Base represents a factual repository with descriptions and triples. Knowledge Graph is the visualized version of the Knowledge Base. A semantic network is knowledge representation. Semantic Network is prominent to understand the value of an individual node, or the similar and distant members of the same semantic network. Semantic networks are implemented for the search engine result pages. Semantic networks are to create a factual and connected question and answer networks. A semantic network can be represented and consist of from textual and visual content. Semantic Network include lexical parts and lexical units.
Links, Nodes, and Labels are parts of the semantic networks. Procedural Parts are constructors, destructors, writers and readers. Procedural parts are to expand the semantic networks and refresh the information on it.
Structural Part has links and nodes. Semantic part has the associated meanings which are represented as the labels.
The semantic content networks have different types of relations and relation types.
Semantic content networks have "and/OR" trees.
Semantic Content Networks have "Relation Type Examples" with "is/A" hierarchies.
Semantic Content Networks have "is/Part" Hierarchy.
Inheritance, reification, multiple inheritance, range queries and values, intersection search, complex semantic networks, inferential distance, partial ordering, semantic distance, and semantic relevance are concepts from semantic networks.
Semantic networks help understanding semantic search engines and the semantic SEO. Because, it contains all of the related lexical relations, semantic role labels, entity-attribute pairs, or triples like entity, predicate and object. Search engines prefer to use semantic networks to understand the factuality of a website. Knowledge-based Trust is related to the semantic networks because it provides a factuality related trust score to balance the PageRank. The knowledge-based Trust is announced by Luna DONG. Ramanathan V. Guha is another inventor from the Google and Schema.org. He focuses on the semantic web and semantic search engine behaviors. He explored and invented the semantic search engine related facts.
Semantic Content Networks are used as a concept by Koray Tuğberk GÜBÜR who is founder of Holistic SEO & Digital. Expressing semantic content networks helps to shape the semantic networks via textual and visual content pieces. The semantic content networks are helpful to shape the truth on the open web, and help a search engine to rank a website even if there is no external PageRank flow.
Lexical Semantics, Semantic Similarity and Relevance for SEOKoray Tugberk GUBUR
Lexical semantics and relations between words include relations of superiority, inferiority, part, whole, opposition, and sameness between the meanings of words. The same word can be a meronymy, hyponym, or antonym of another word, depending on the word before or after it. The lexical relation value of the first word can affect the structure of the next word, affecting the context of the sentence and the Information Retrieval Score. Information Retrieval Score is the score that determines how much content is related to a query, how close the different variants of the related query are, and the structure processed by the search engine’s query processor to the relevant document. A higher information retrieval score represents better relevance and possible click satisfaction.
The problem with a semi-structured and distracting context for Information Retrieval Score is that, if a document is not configured for a single topic, the IR Score can be diluted by the two different contexts resulting in a relative rank lost to another textual document.
IR Score Dilution involves badly structured lexical relations, along with bad word proximity. The relevant words that complete each other within the meaning map should be used closely, within a paragraph or section of the document, to signal the context in a more clear way to increase the IR Score. A search engine can check whether the document contains the hyponym of the words within the query or not. A possible query prediction can be generated from the hypernyms of the query. A search engine can check only the anchor texts to see whether there is a word within the “hyponym distance” which represents the hyponym depth between two different words.
Lexical Relations can represent the semantic annotations for a document. A semantic annotation is a word that describes the document overall in terms of category and main context that carries the purpose of the document. A semantic annotation can contain the main entity of the document or a general concept for covering a broader meaning area (knowledge domain). Semantic Annotations can be generated with the lexical relations between words. A semantic annotation can be used to match the document to the query. Semantic annotations are factors for a better IR Score.
A search engine can generate phrase patterns from the lexical relationships between words within the queries or the documents. A phrase pattern contains sections that define a concept with qualifiers. Phrase patterns can contain a hyponym just after an adjective, or a hypernym with the antonym of the same adjective. Most of these connections and patterns are used within the Recurrent Neural Network (RNN) for the next word prediction. A phrase pattern helps a search engine to increase its confidence score for relating the document to the specific query, or the meaning of the query.
How to Implement Machine Learning in Your Internal Linking Audit - Lazarina S...LazarinaStoyanova
In this talk, Lazarina will break down the key areas that an internal linking audit should look into and go over opportunities for embedding machine learning in a way that is beginner-friendly for SEOs without extensive coding experience. Lazarina will share:
– How to analyse a site’s existing internal linking structure using machine learning
– What machine learning techniques can you implement to help you create content clusters?
– How to find user-friendly, in-content linking opportunities
– How to prioritise and measure the impact of internal linking initiatives
Lazarina will also touch upon how to use this process to identify other opportunities for site optimization, which can improve the user experience and search potential.
Related Blog post published at: https://lazarinastoy.com/how-to-incorporate-machine-learning-in-internal-linking-audits/
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.
Accessibility, strategy and schema - do they go hand in hand? Beth Barnham Br...BethBarnham1
In this talk, I explore schema and its link to online accessibility. Can schema really help the web to be more accessible? And how should we strategise this as SEOs? Strategy plays a vital role in SEO but often times the technical areas are overlooked within a wider marketing strategy
How to Incorporate ML in your SERP Analysis, Lazarina Stoy -BrightonSEO Oct, ...LazarinaStoyanova
How are you currently doing SERP analysis? Is your approach efficient or scalable?
How SEOs can incorporate programmatic approaches and advances in machine learning in order to identify winning strategies?
This talk will leave you with a better understanding of what is possible for SERP analysis at scale, what insights you can capture with the help of machine learning quickly, and how to incorporate insights into your strategy and visualize your findings to impress your stakeholders.
The Reason Behind Semantic SEO: Why does Google Avoid the Word PageRank?Koray Tugberk GUBUR
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.
How to approach SEO in a world where Google has moved from strings and keywords to things, topics and entities. Dixon JOnes is the CEO of InLinks, who have build a proprietory NLP algorithm and Knowledge Graph designed for the SEO Industry.
Coronavirus and Future of SEO: Digital Marketing and Remote CultureKoray Tugberk GUBUR
I have attended a great SEO and Digital Marketing webinar with Founder of Stradiji and SEMRush Turkey Lead Mr. Mert Erkal and My Dearest Friend and SEO Consultant Atakan Erdoğan.
Small Note: After I uploaded the presentation, Google launched a new Covid-19 news address like Bing/covid-19. You may want to look at it -> https://www.google.com/covid-19
I have prepared a Presentation about Coronavirus's Effects on Search Engine Optimization (SEO).
You will find Coronavirus's changing effects on Digital Marketing and psychology of global society while using Search Engines.
I also have focused on Search Engine's and Social Media Brands, E-commerce Site's reflexes against Coronavirus Pandemic.
You will see the web sites and categories who earn more traffic and lose traffic. You will also see conversion rate differences because of Coronavirus.
Also, I have told about Search Engine's differences and their attitude against the Coronavirus Pandemic, their future, their updates during the pandemic.
In the last part, you will see some new 2020 Web Technology and Design Trends with AI.
There are also Google Researches for better Search Engine technologies.
Questions:
1- What are the differences between Yandex, Google, Bing, and Duckduckgo for Coronavirus Pandemic?
2- Twitter, Instagram, Amazon or Apple, what are they doing?
3- What do people search most for during the Coronavirus Crisis?
4- What changes from country to country?
5- What are the future technologies of Web and App?
6- How and why do Search Engines improve AI, what is the last events?
7- Which sites loose traffic and which earn more?
8- Lots of quotes from International SEOs about the pandemic.
And more...
I am Koray Tuğberk GÜBÜR and a Holistic SEO Expert.
I sincerely thank you for my Dearest Friend Atakan Erdoğan and Mr. Mert Erkal for this awesome webinar opportunity and experience.
To watch the webinar, please visit Stradiji's Official Youtube Channel.
https://www.youtube.com/watch?v=V4sJTNcRqaM&t=100s
How to Use Search Intent to Dominate Google DiscoverFelipe Bazon
In this talk you will learn how search intent can help you benefit from the growing popularity of Google Discover. You’ll get actionable tips, a case study example and exclusive data from SEMrush.
Automate The Technical SEO Stuff by Michael Van Den Reym
In this talk, Michael will show you how to automate technical SEO tasks. You will learn how to schedule and compare crawls to spot technical errors faster, how to use RPA to speed up technical SEO audits and how to automatically optimize images. You will get inspired to execute technical SEO better and faster.
How to convince even the pickiest editors to take SEO more seriously :: brigh...Ian Helms
Let's face it: Editorial teams may not prioritize SEO, but it's an essential aspect of online content creation. Editorial teams are under constant pressure to produce timely and relevant content, often overlooking the long-term benefits of search-friendly, evergreen articles. While breaking news and current events are crucial for driving immediate traffic, organic-focused content can provide long-term value by continuously attracting visitors through search engines. During this presentation, Ian will share his historical experience working with editorial teams and how he successfully incorporates SEO into their workflow. You'll learn how they address common pain points and use data to generate enthusiasm for organic search.
An Overview of the area and the current potential for the open technologies to be used, and some suggestions as to why they are not as heavily used as they should be.
Interviewing Users: Spinning Data Into GoldSteve Portigal
Interviewing is undeniably one of the most valuable and commonly used user research tools. Yet it's often not used well, because
* It’s based on skills we think we have (talking or even listening)
* It's not taught or reflected on, and
* People tend to "wing it" rather than develop their skills.
Results may be inaccurate or reveal nothing new, suggesting the wrong design or business responses, or they may miss the crucial nuance that points to innovative breakthrough opportunities.
In this day-long session, we'll focus on the importance of rapport-building and listening and look at techniques for both. We will review different types of questions, and why you need to have a range of question types. This session will explore other contextual research methods that can be built on top of interviewing in a seamless way. We'll also suggest practice exercises for improving your own interviewing skills and how to engage others in your organization successfully in the interviewing experience.
Direction regarding how to Produce a fantastic Research Proposalmoriancvz3
Therefore you are questioned to write down a investigation paper so you are rearing to go. You have all of it figured out and you'll barely wait around to dive in and begin… perfectly, investigating. But hold on! Have you ever accomplished your investigation proposal?
Reading academic papers is one of the most important parts of scientific research. However, junior graduate students may spend a lot of time learning how to read papers efficiently and effectively. In this talk, I will discuss some basic issues and introduce useful websites/tools/tips for paper reading.
SearchLeeds 2018 - Dawn Anderson - Power from what lies beneath ... The icebe...Branded3
Dawn takes a look at ‘The Iceberg Approach to SEO’. As we move increasingly to an era of smaller screen search (or no screen), we need to consider ways to say more with less and communicate this to both search engines and users. She explores semantics, the knowledge graph, schema and ontologies combined with UX as methods to pass themed ‘equivalence’ from below the surface of the site or the individual page.
Mike King examines the state of the SEO industry and talks through knowing information retrieval will help improve our understanding of Google. This talk debuted at MozCon
If you think you need a search application, there are some useful first steps to take:
* validating that full-text search is the right technology
* producing sets of ideal results you'd like to return for a range of queries
* considering the value of supplementing a basic search result list with document clustering
* producing more specific requirements and investigating technology options
Webinar: Simpler Semantic Search with SolrLucidworks
Hear from Lucidworks Senior Solutions Consultant Ted Sullivan about how you can leverage Apache Solr and Lucidworks Fusion to improve semantic awareness of your search applications.
The New Content SEO - Sydney SEO Conference 2023Amanda King
Amanda King of FLOQ's deck for the Sydney SEO conference run by Prosperity Media in April of 2023 on content, entity SEO and Google's history (or lack thereof) with keywords. We also go through natural language processing, what it is and how quickly Google goes from queries to entities based on their patent application history. And of course, no good conference session would go without actionable suggestions, which you can find at the end of the deck.
For another angle on content and strategy and how to approach them, read more at https://floq.co/seo-strategy/tactics-strategy/
How Many Dimensions of Compatibility?: Discovering What's Right for Your Users Marliese Thomas
How Many Dimensions of Compatibility: Discovering What's Right for Your Users
This was the keynote address at University of Houston Library's Discovery Day Camp on June 10, 2011. Some extra screenshots of admin interfaces have been added after the actual presentation.
Human quality raters have been the mainstay of search engine evaluation for decades but a sea-change is on its way due to the need for scale as machine learning and demand evolves.
Life of An SEO - Surfing The Waves of Googles Many Algorithmic UpdatesDawn Anderson MSc DigM
the life of an SEO is never boring. Search is always changing and subsequently Google's algorithms are updated to reflect changing search behaviours and to combat the actions of bad actors / spam in the search engine results pages. We look at past algorithms, the many types of algorithms and identify how you can ascertain whether you've been impacted by an algorithmic update and how to remedy / recover
Zipfs Law & Zipfian Distribution in SEO - Pubcon Virtual Fall 2020 - Dawn And...Dawn Anderson MSc DigM
Zipf's Law is prevalent throughout many forms of data and that includes the internet at large and within sectors of the internet, websites and web pages plus linguistics. How does this impact SEO if at all?
What is BERT? It is Google's neural network-based technique for natural language processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. It was opened-sourced last year and written about in more detail on the Google AI blog. In this presentation we look at what Google BERT means for SEOs and marketers and how Google BERT is and will continue to impact the search landscape. We also look at the back story to Google BERT, including transformers and natural language understanding and computational linguistics.
Google BERT is many things, including the name of a Google Search algorithm update. There is lots of confusion as to what Google BERT is, where it has come from and what SEOs and marketers need to do about it (if anything). Here we look at the solutions the introduction of Google BERT by Google seeks to provide and explore the background to natural language processing and computational linguistics.
Disambiguating Equiprobability in SEO Dawn Anderson Friends of Search 2020Dawn Anderson MSc DigM
Connecting the probable dots in content and data can help significantly and improve your search strategy. Ambiguity in SEO comes in many form too, going beyond content and into entities and locations. This talk touches on some of the areas where ambiguity can impact and hinder your performance
Talk from Tech SEO Boost 2019 by Dawn Anderson on the move to the just in time predictive personalised search experience for search engines and users. Exploring recommender systems, collaborative filtering, temporal and location based queries and the rise of predictive, personal dynamic search. Exploring the work of information retrieval researchers and Google Discover.
Connecting The Worlds of Information Retrieval & SEO - Search solutions 2019 ...Dawn Anderson MSc DigM
The worlds of information retrieval and SEO are very connected and each benefits from the other as the amount of content and unstructured data on the web grows. Here I look at my experiences over the past few years of following both the IR and SEO worlds
It's very easy to get started too quickly in SEO for a new website and not plan properly using a framework to improve probability of success. Here we look at the SOSTAC framework for a new site and explore some traditional strategic models and marketing frameworks to employ in an SEO capacity for a dog friendly website in the UK territory. Expect SOSTAC, Porters 5 Forces, Ansoff Matrix, 5S's, 8P's and more
Google BERT and Family and the Natural Language Understanding Leaderboard RaceDawn Anderson MSc DigM
Natural Language Understanding and Word Sense Disambiguation remains one of the prevailing challenges for both conversational and written word. Natural language understanding attempts to untangle the 'hot mess' of words between more structured data in content, but the challenge is not trivial, since there is so much polysemy in language. Some recent developments in machine learning have seen significant leaps forward in understanding more clearly the context (and therefore user intent and informational need at time of query). Here we will explore these developments, and some of their implementations and seek to understand what this means for search strategists and the brands they support both now and into the future.
How is mobile technology and search engine tuning evolving to meet the needs of users? Here we look at recent developments in research, implementations by search engines, and how to look at reach users can adapt their strategies to take into account these next-level changes.
Natural Language Processing and Search Intent Understanding C3 Conductor 2019...Dawn Anderson MSc DigM
This talk looks at the ways in which search engines are evolving to understand further the nuance of linguistics in natural language processing and in understanding searcher intent.
As the volume of content continues to grow exponentially helping search engines to understand context and the topical themes within your site is increasingly important. Understanding some of the concepts are covered and also ways to utilise these in your marketing strategy.
In an information economy where users are time poor and research hungry we need to take a mobile first approach to meet the needs of both users and search engines looking to align users informational needs with relevant search results. With limited space and a now mobile-first index how can we align our SEO strategy with this?
The changing search landscape calls for different approaches to user needs, including context, intent and device considerations. Here we take a look at ways to keep working to keep your ecommerce site well positioned for strong transactional and informational queries in the moments that matter to shoppers online.
Voice Search and Conversation Action Assistive Systems - Challenges & Opportu...Dawn Anderson MSc DigM
We are headed to the age of assistive task driven search where the user needs help to 'do' things as well as learn things. Smart speakers, mobile phones, assistive systems and conversational search and action devices are where the buck is headed for now. Where are we at in this wave? What are the challenges? What are the opportunities right now? Here we look at some of the ways we can start to prepare our tactics and strategy to be pioneering search marketers with conversation search and conversation action.
The Iceberg Approach - Power from what lies beneath in SEO for a mobile-first...Dawn Anderson MSc DigM
In a mobile-first world, with time-poor users, research crazy and mainly browsing with one eye and one hand, we need to consider a few new approaches to SEO. Information overload is all around. We need to think about how we can maintain relevance with less in some cases. We need to consider the Iceberg Theory and simultaneously consider the Iceberg Syndrome as well as placement position in user view for content and links
Mobile-first goes beyond simply indexing in a search engine. It has several meanings, which traverse user-behaviour, web design, adoption in different territories, adoption amongst user segments, adoption in different verticals. We need to be aware of these fundamentals changes in search behaviour and adapt quickly.
Here we take a look at server log file analysis for SEO and explore not only the benefits but also the process of finding, gathering, shipping and analysing user agent logs
Voice Search Challenges For Search and Information Retrieval and SEODawn Anderson MSc DigM
Whilst search engines are making great strides to achieve gold standards in error free voice search recognition there are still a number of challenges. We look at some of them here and seek to understand how we may adapt to optimise for them. Thanks to Enrique Alfonseca, the Google Conversational Research Team, ESSIR Barcelona for the great learnings and education.
Videos are more engaging, more memorable, and more popular than any other type of content out there. That’s why it’s estimated that 82% of consumer traffic will come from videos by 2025.
And with videos evolving from landscape to portrait and experts promoting shorter clips, one thing remains constant – our brains LOVE videos.
So is there science behind what makes people absolutely irresistible on camera?
The answer: definitely yes.
In this jam-packed session with Stephanie Garcia, you’ll get your hands on a steal-worthy guide that uncovers the art and science to being irresistible on camera. From body language to words that convert, she’ll show you how to captivate on command so that viewers are excited and ready to take action.
Short video marketing has sweeped the nation and is the fastest way to build an online brand on social media in 2024. In this session you will learn:- What is short video marketing- Which platforms work best for your business- Content strategies that are on brand for your business- How to sell organically without paying for ads.
Financial curveballs sent many American families reeling in 2023. Household budgets were squeezed by rising interest rates, surging prices on everyday goods, and a stagnating housing market. Consumers were feeling strapped. That sentiment, however, appears to be waning. The question is, to what extent?
To take the pulse of consumers’ feelings about their financial well-being ahead of a highly anticipated election, ThinkNow conducted a nationally representative quantitative survey. The survey highlights consumers’ hopes and anxieties as we move into 2024. Let's unpack the key findings to gain insights about where we stand.
AI-Powered Personalization: Principles, Use Cases, and Its Impact on CROVWO
In today’s era of AI, personalization is more than just a trend—it’s a fundamental strategy that unlocks numerous opportunities.
When done effectively, personalization builds trust, loyalty, and satisfaction among your users—key factors for business success. However, relying solely on AI capabilities isn’t enough. You need to anchor your approach in solid principles, understand your users’ context, and master the art of persuasion.
Join us as Sarjak Patel and Naitry Saggu from 3rd Eye Consulting unveil a transformative framework. This approach seamlessly integrates your unique context, consumer insights, and conversion goals, paving the way for unparalleled success in personalization.
How to Run Landing Page Tests On and Off Paid Social PlatformsVWO
Join us for an exclusive webinar featuring Mariate, Alexandra and Nima where we will unveil a comprehensive blueprint for crafting a successful paid media strategy focused on landing page testing.With escalating costs in paid advertising, understanding how to maximize each visitor’s experience is crucial for retention and conversion.
This session will dive into the methodologies for executing and analyzing landing page tests within paid social channels, offering a blend of theoretical knowledge and practical insights.
The Pearmill team will guide you through the nuances of setting up and managing landing page experiments on paid social platforms. You will learn about the critical rules to follow, the structure of effective tests, optimal conversion duration and budget allocation.
The session will also cover data analysis techniques and criteria for graduating landing pages.
In the second part of the webinar, Pearmill will explore the use of A/B testing platforms. Discover common pitfalls to avoid in A/B testing and gain insights into analyzing A/B tests results effectively.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Monthly Social Media News Update May 2024Andy Lambert
TL;DR. These are the three themes that stood out to us over the course of last month.
1️⃣ Social media is becoming increasingly significant for brand discovery. Marketers are now understanding the impact of social and budgets are shifting accordingly.
2️⃣ Instagram’s new algorithm and latest guidance will help us maintain organic growth. Instagram continues to evolve, but Reels remains the most crucial tool for growth.
3️⃣ Collaboration will help us unlock growth. Who we work with will define how fast we grow. Meta continues to evolve their Creator Marketplace and now TikTok are beginning to push ‘collabs’ more too.
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
2. Find ways to show EEAT
3. Repurpose across all platforms
When most people in the industry talk about online or digital reputation management, what they're really saying is Google search and PPC. And it's usually reactive, left dealing with the aftermath of negative information published somewhere online. That's outdated. It leaves executives, organizations and other high-profile individuals at a high risk of a digital reputation attack that spans channels and tactics. But the tools needed to safeguard against an attack are more cybersecurity-oriented than most marketing and communications professionals can manage. Business leaders Leaders grasp the importance; 83% of executives place reputation in their top five areas of risk, yet only 23% are confident in their ability to address it. To succeed in 2024 and beyond, you need to turn online reputation on its axis and think like an attacker.\
Key Takeaways:
- New framework for examining and safeguarding an online reputation
- Tools and techniques to keep you a step ahead
- Practical examples that demonstrate when to act, how to act and how to recover
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Come learn how YOU can Animate and Illuminate the World with Generative AI's Explosive Power. Come sit in the driver's seat and learn to harness this great technology.
Core Web Vitals SEO Workshop - improve your performance [pdf]Peter Mead
Core Web Vitals to improve your website performance for better SEO results with CWV.
CWV Topics include:
- Understanding the latest Core Web Vitals including the significance of LCP, INP and CLS + their impact on SEO
- Optimisation techniques from our experts on how to improve your CWV on platforms like WordPress and WP Engine
- The impact of user experience and SEO
The Secret to Engaging Modern Consumers: Journey Mapping and Personalization
In today's digital landscape, understanding the customer's journey and delivering personalized experiences are paramount. This masterclass delves into the art of consumer journey mapping, a powerful technique that visualizes the entire customer experience across touchpoints. Attendees will learn how to create detailed journey maps, identify pain points, and uncover opportunities for optimization. The presentation also explores personalization strategies that leverage data and technology to tailor content, products, and experiences to individual customers. From real-time personalization to predictive analytics, attendees will gain insights into cutting-edge approaches that drive engagement and loyalty.
Key Takeaways:
Current consumer landscape; Steps to mapping an effective consumer journey; Understanding the value of personalization; Integrating mapping and personalization for success; Brands that are getting It right!; Best Practices; Future Trends
5 big bets to drive growth in 2024 without one additional marketing dollar AND how to adapt to the biggest shifting eCommerce trend- AI.
1) Romance Your Customers - Retention
2) ‘Alternative’ Lead Gen - Advocacy
3) The Beautiful Basics - Conversion Rate Optimization
4) Land that Bottom Line - Profitability
5) Roll the Dice - New Business Models
SEO as the Backbone of Digital MarketingFelipe Bazon
In this talk Felipe Bazon will share how him and his team at Hedgehog Digital share our journey of making C-Levels alike, specially CMOS realize that SEO is the backbone of digital marketing by showing how SEO can contribute to brand awareness, reputation and authority and above all how to use SEO to create more robust global marketing strategies.
First Things First: Building and Effective Marketing Strategy
Too many companies (and marketers) jump straight into activation planning without formalizing a marketing strategy. It may seem tedious, but analyzing the mindset of your targeted audiences and identifying the messaging points most likely to resonate with them is time well spent. That process is also a great opportunity for marketers to collaborate with sales leaders and account managers on a galvanized go-to-market approach. I’ll walk you through the methods and tools we use with our clients to ensure campaign success.
Key Takeaways:
-Recognize the critical role of strategy in marketing
-Learn our approach for building an actionable, effective marketing strategy
-Receive templates and guides for developing a marketing strategy
Digital marketing is the art and science of promoting products or services using digital channels to reach and engage with potential customers. It encompasses a wide range of online tactics and strategies aimed at increasing brand visibility, driving website traffic, generating leads, and ultimately, converting those leads into customers.
https://nidmindia.com/
11. Plus… Google takes a ‘Hybrid Approach’ to
research
https://research.google/pubs/pub38149/
12. Research &
engineering
are closely
aligned
Research designed to meet needs of
users
Research designed to make it to
production in a relatively short space
of time often
Engineers are often researchers and
vice versa
Able to research and deploy iteratively
and quickly
13. Plus… There are
research applications
built to align IR
research with ‘real life’
industry – e.g. Anserini
(Yang, Fang & Lin, 2017)
14. Designed to build ‘reproducible’ code,
tests & studies to avoid the below:
27. “A Breakthrough in Ranking”
• “We’ve recently made a breakthrough in ranking and are now able to
not just index web pages, but individual passages from the pages,”
“By better understanding the relevancy of specific passages, not just
the overall page, we can find that needle-in-a-haystack information
you’re looking for.” (Raghavan, P, 2020)
37. E.g. Understanding when ‘bank’ in queries or
content means ‘river bank’ versus ‘financial bank’
38. It’s a way to judge parts of a document
individually for relevance to a query
(passages), rather than as just one very
small part of a full document
44. OR… the author just used more
words than needed (verbosity)
45. Likely over a threshold >
‘x’ unique ‘uncommon’
words
46. i.e. other than … Common English Words
a,able,about,across,after,all,almost,also,am,among,an,and,any,are,as,at,
be,because,been,but,by,can,cannot,could,dear,did,do,does,either,else,ev
er,every,for,from,get,got,had,has,have,he,her,hers,him,his,how,however,i,
if,in,into,is,it,its,just,least,let,like,likely,may,me,might,most,must,my,neithe
r,no,nor,not,of,off,often,on,only,or,other,our,own,rather,said,say,says,she,
should,since,so,some,than,that,the,their,them,then,there,these,they,this,t
is,to,too,twas,us,wants,was,we,were,what,when,where,which,while,who,w
hom,why,will,with,would,yet,you,your
47. ASIDE: These words nowadays actually
add ‘contextual glue’ in natural language
models, but were traditionally removed
‘stop words’
55. Why not? – Transactional pages are
different beasts to informational pages
Not that many words on
an ecommerce page
(either product page or
category page)
Ecommerce pages tend
to have an ‘out of the
box’ organised content
structure template
Ecommerce pages will
likely have lots of other
'value add’ features too
Ecommerce content is
not conversational in
nature
Ecommerce pages tend
to have a descending
'importance' hierarchy
in the page content 'out
of the box'
56. Lots of clues
as well
‘beyond the
words’
Internal links with
indicative anchors
Strong
categorization &
subcategorization
‘Probably’ some
product schema
markup too
57. So, it’s probably not that difficult for search
engines to understand ecommerce pages
58. Passage indexing / ranking
is a ‘leg-up’ to pages which
likely don’t rank for much
currently
60. They don’t look like this
<h1>Main theme</h1>
<p>paragraph</p>
<h2>Key point - Section – Subtopic</h2>
<p>paragraph</p>
<h3>Further point heading connected to h2 section</h3>
<p>paragraph</p>
<h2>Key point – Section – Subtopic</h2>
73. Two (or multi) stage ranking
Retrieval (first
stage)
Re-ranking
(refinement of
the initial fetch
for optimal
search results)
74. Retrieval stage
From all possible text (or synonym)
matching candidates above an x
quality threshold fetch e.g. 1000
Pass to the next stage for
further consideration
75. Re-ranking stages (Cascading stages?)
From e.g. initial
fetched 1000,
fine tune an
optimal set of
combined
results to meet
query
Present in
response to
query in search
92. Also
Computers
Were (Are)
Hopeless at
Ambiguity
“Computers are hopeless at disambiguation – at
understanding which of multiple meanings is
correct – because they don’t have our world
knowledge.” Dr Stephen Clark, Cambridge
University
https://www.cam.ac.uk/research/features/our-
ambiguous-world-of-words
93. Different
types of
ambiguous
queries
Genuine ambiguous queries –
‘bank’, ‘rose’, ‘bond’
Underspecified queries – ‘Harry
Potter’ (is it the book or the
film?)
Fully specified queries – ’Harry
Potter film’ (aspect is fully
defined)
98. Who is the
user?
What the niche
is? (Do videos
matter more?)
Do more people
search for apple
fruit there?
Where is the
user? (location)
Are images or
video a good fit?
When is it?
(Query intent
shift) (
100. Perhaps it’s best to show a
range of possible intent
matching results?
101. Relevance Ranking & Result Diversification
A list of documents ranked
in order of descending
relevance to a query (PRP)
But…Diversity matters in
results returned to account
for ambiguity and
redundancy. Better to have
broad diversity and novelty
to cover all query aspects
105. In addition to Probability Ranking Principle several
other approaches have been explored
Portfolio Theory (PT)
(Wang and Zhu, 2009)
Quantum Probability Ranking Principle (QPRP)
(Zuccon, G., Azzopardi, L.A. and Van Rijsbergen, K., 2009)
Maximal Marginal Relevance (MMR)
(Carbonell and J. Goldstein, 1998)
109. Learning-to-
Rank
(learning
‘current
relevance’)
Feed a model with click data / queries (learn current
relevance)
Train & lab evaluation
Send to human quality raters scoring (human in the loop)
Aggregate the scores from raters 'on the whole evaluation'
Use NDCG (Non-discounted cumulative gain) to adjust the
whole test algorithm based on scores from human raters
111. So…SERP
Stages
First stage - Retrieve initial fetch of
e.g. 1000 documents to get a top-k
(e.g. 20) candidates to re-order
Final Stage - Refine to build an
optimal page dynamically refined
for relevancy & diversity
124. Precision refinement
Novelty in results
Subtopic retrieval
Reduce redundancy in results
Determine ‘the best mix of results for the query’
Determine the optimal position for each candidate type
125. Re-ranking uses more expensive methods
COMPUTATIONALLY FINANCIALLY ENVIRONMENTALLY
126. When the first
stage of ranking
is based on
relative
keyword (term)
frequency
matching to e.g.
document
length
Lexical matching
E.g. BM25 (Best
match 25
algorithm)
commonly used
in information
retrieval
143. It looks like…
Several
different
approaches,
including:
Ignore Them
• Ignore them completely to save on efficiency. As long
as there are other smaller relevant documents to rank
Trim Them
• Trim the document by selecting one good passage
Identify a passage
• Identify one passage only on large documents over x
size and for everything else (smaller documents) use
normal full document ranking
Use TextTiling
• Try to identify subtopics in natural paragraph breaks
Use only The First 'n' Terms
• Anything after 'n' terms is ignored
153. What is BERT (Bi-
Directional
Encoder
Representations
from
Transformers?
A PLM (Pre-trained language model)
Used for neural matching to understand word’s meaning in
‘context’
So, not just ‘keyword matching’ to documents on a page
But understanding multiple meanings of a word in different
contexts
BERT is pre-trained on millions of words so natural language
researchers / engineers have a ‘starter for ten’ language
model which already understands the contextual glue /
nuance in words
154. BERT can dig
out ‘meaning’ in
informational /
conversational
content
170. An
Ensemble -
Taking the
best parts of
other
language
models &
features
RoBERTa (Facebook)
ELECTRA (Google)
BERT (Google)
Tensorflow (Google)
DeepCT (Dhai)
Learning to Rank (LeToR)
171. DeepCT (Dhai,
2019)
‘Deep
Contextualized
Term-
Weighting
Framework’
tfDeepCT – An alternative to term frequency which
replaces tf with tfDeepCT
DeepCT-Index – Alternative weights added to an
original index, with no additional postings. Weighting
is carried out offline, and therefore does not add any
latency to search engine online usage
DeepCT-Query – An updated bag-of-words query
which has been adapted using the deep contextual
features from BERT to identify important terms in a
given text context or query context.
172. “A Breakthrough in Ranking”
• “We’ve recently made a breakthrough in ranking and are now able to
not just index web pages, but individual passages from the pages,”
“By better understanding the relevancy of specific passages, not just
the overall page, we can find that needle-in-a-haystack information
you’re looking for.” (Raghavan, P, 2020)
186. Break the
tasks down
Open domain question answering
Greater understanding in expert
disorganized content
Greater conversational search
Greater understanding about how
everything 'fits together'
192. Move over BERT… VERY different
models are appearing
T5 (Google)
DocTTTTTQuery (Nogueira & Lin, 2019)
Expando-mono-duo (Pradeep, R., Nogueira, R. and Lin, J., 2021)
RocketQA + Ernie (Qu et al., 2020, (Baidu))
COIL (Gao, L., Dai, Z. and Callan, J., 2021
DeepCT (Dai, Z and Callan, 2019)
197. Alternatives
to BM25 &
TF:IDF are
being found
in passages
Dense Passage Retrieval
ANCE
DocTTTTTQuery
COIL
ColBERT
DeepCT
198. Dense Passage Retrieval – ‘Packing’ sparse initial
stage data with training passages
ANCE – Nearest neighbour approach
DocTTTTTQuery – Query expansion in first stage
COIL – Scoring system to add ‘context’ to BM25 first
stage retrieval – adds semantic & lexical (hybrid)
ColBERT – Late stage BERT introduction
DeepCT – Index term weighting with DeepCT
framework for ‘importance weights’
202. Leveraging Semantic and Lexical
Matching to Improve the Recall of
Document Retrieval Systems: A Hybrid
Approach (Kuzi, S., Zhang, M., Li, C.,
Bendersky, M. and Najork, M., 2020)
221. On all
sections of
the site
Informational content (blogs, guides,
FAQs etc)
Informational ‘collection’ pages (e.g.
blog category pages, guide hub
pages)
Transactional content (e.g. -
ecommerce product pages, local
listings)
Transactional ‘collection’ pages (e.g. -
ecommerce category pages, local
listing category pages)
222. Since meeting the needs of
under-specified queries
requires broad coverage
223. You will be judged on
“How many of the intents of this under-
specified query do you meet?”
224. It could be hard to rank for
big ‘head’ terms without
comprehensive stock
(‘information gain’ (value))
within your document
collection (site), for all
query aspects (intents) to
meet an under-specified
query
236. Think…
Are we bringing a new
perspective?
Is the page substantively different
to what we already have
Does this page meet another
aspect of information need /
different intent?
Does this page substantively bring
another type of media into the
mix?
Don’t try to be the same as what is
already ranking… do something
more / different
237. Bring a new perspective
THOUGHT LEADERSHIP NEW DATA DRIVEN
REFRESHING
PERSPECTIVES
INTERACTIVE TOOLS ADDITIONAL FEATURES IN
YOUR CONTENT WHICH
OTHERS DON’T HAVE?
238. Improving
informational
content
Add semantic headings
Connect up topics via ‘relatedness’
Learn to structure content properly
Inverted pyramid approach to web content
Avoid verbosity
Move’ and ‘prune’ with caution
Don’t ‘prune’ away your semantic ‘relatedness’