🎯 Key Takeaways for quick navigation: 00:00 🎙️ *Introduction to Presentation* - Overview of the purpose of the video. - Encouragement to watch the live recording for a more detailed experience. - Emphasis on a relaxed discussion about the presentation content. 00:53 📊 *Importance of SERP Analysis* - Highlighting three significant numbers related to Google's SERP experiments. - Emphasis on the market share dominance of Google's search engine. - Discussion on the evolving nature of Google's SERP and its various functions. 04:21 🌐 *Google's Objectives on SERP* - Detailed explanation of Google's objectives on the SERP. - Exploration of how Google aims to communicate with users through SERP. - Discussion on recent additions like knowledge panels, DIY tutorials, and e-commerce integration. 06:09 📚 *New Approach to Content Planning* - Shift from traditional content planning to a user-centric approach. - Importance of studying the SERP for user intent before creating content. - The significance of competitor analysis and optimization for optimal performance. 08:27 🔍 *Three Categories of Recommendations* - Introduction to the three recommendation categories: Must-Haves, Should-Haves, Could-Haves. - Brief explanation of the purpose and distribution of effort for each category. - Setting the stage for detailed recommendations in the subsequent sections. 13:19 📈 *Competitor Analysis - Must-Have* - Detailed steps for conducting competitor analysis using Data for SEO. - Importance of understanding competitors' search visibility and traffic potential. - Visualization techniques, such as positioning maps, to analyze competitor data. 15:48 🌐 *Competitor Categorization - Should-Have* - Categorization of competitors based on site type (Wiki, Courses, News, etc.). - Discussion on the importance of understanding the landscape and identifying opportunities. - Visual representation of competitor types for comprehensive analysis. 18:34 🎯 *Intent Categorization - Should-Have* - Classification of competitors based on informational, transactional, or navigational intent. - Insights into how Google positions different types of websites. - Importance of understanding user intent categories for strategic content creation. 21:21 📊 *Share of Search Analysis - Could-Have* - Introduction to calculating share of search per domain and per intent category. - Brief explanation of the potential of Data for SEO's bulk SERP API for large-scale analysis. - Emphasis on the flexibility and scalability of the analysis process. 23:39 🔄 *Advanced SERP Analysis with GPT-3 - Could-Have* - Integration of GPT-3 for classifying site categories and search intent. - Acknowledgment and credit to Danny Richmond for the script. - Exploration of using AI for more detailed analysis of title and meta description content. 24:48 🧠 *Closing and Looking Forward* - Summary of analysis prerequisites: keyword research, content strategy, and brand voice guidelines. - Acknowledgment of limitations and anticipation of feedback from the audience. - Expressing interest in the broader industry's contributions to SERP analysis. 25:02 📊 *SERP Analysis Overview* - Analyzing top categories, site types, and SERP makeup based on domains and keywords. - Identifying domains ranking for the highest number of distinct keywords. 28:14 🧠 *Advanced Entity Analysis Setup* - Introduction to entity extraction using Google's Natural Language API. - Importance of understanding entities, salience, sentiment score, and sentiment magnitude. 30:49 🚀 *Entity Analysis Visualizations* - Visualizing common entities in meta descriptions across SERPs. - Analyzing sentiment and positioning of entities using a positioning map. 34:12 🤖 *Machine Learning Insights* - Discussing rank prediction based on entities and Knowledge Graph. - Caveats and the need for a large historic dataset for effective rank prediction. 34:53 😃 *Sentiment Analysis for Brand Reputation* - Utilizing sentiment analysis for brand reputation management. - Filtering for negative and positive sentiments to identify brand advocates and opponents. 41:25 🔍 *Sentiment and Positioning Relationships* - Filtering sentiment and positioning maps for specific keywords/entities. - Understanding sentiment relationships for targeted keywords. 43:59 📈 *Expanded Use of Entity Analysis* - Repurposing entity extraction scripts for internal link opportunities and content understanding. - Highlighting the industry's underutilization of available technologies. 46:18 🔍 *Search Intent Analysis Process* - Introduction to the process of labeling titles and meta descriptions for search intent. - Use of GPT-3 for funnel-based and desired action intent classification. 48:07 📝 *Ranking Content Understanding* - Scrutinizing content elements that influence ranking for specific intent-labeled keywords. - Analyzing and visualizing factors contributing to content classification. 48:46 🌐 *Advanced Machine Learning for Search Intent* - Classifying search intent based on featured snippet types. - Exploring the potential of training custom models for micro-moments. 49:54 🧑💻 *Language Use Analysis with GPT-3* - Using GPT-3 to assist in writing Apps Script formulas for language analysis. - Practical application for non-coders to leverage advanced language models. 50:21 🧠 *Engram Analysis using GPT-3* - The speaker discusses using GPT-3 to create an Apps Script formula for identifying bigrams in titles or meta descriptions. - GPT-3 was utilized to generate a JavaScript function for identifying main engrams. - The analysis aims to uncover prominent keyword combinations and understand what content Google serves for specific keywords. 52:49 🕵️ *Rank Analysis Caution* - The speaker advises caution regarding rank analysis due to uncertainties and guidelines surrounding scraping and monitoring Google's SERPs. - Basic evaluation of ranking positions, feature snippet appearances, and rudimentary monitoring of changes over time is discussed. - The speaker suggests predictions, such as predicting organic CTR increase from position changes and predicting SERP makeup changes based on seasonality or events. 55:50 🚀 *Closing Remarks and Encouragement* - The speaker concludes the presentation by encouraging viewers to explore machine learning themselves. - Mention of follow-up videos on using the provided dashboard and template sheets. - Emphasis on the importance of correlation not equaling causation and the role of analysis in guiding strategy rather than replacing it. Made with HARPA AI
Hi Lazarina You just got a new subscriber from Dubai, I didn't even realize that how I spent 58 minutes here because of the quality content and a smiling face of yours. Keep publishing the quality content please 😍
Hello Lazarina, thank you for putting this amazing presentation together. This has really opened up a whole world of value for me. Is there any tutorial video or blog post to set this whole process up and running? I am a no coder so this sounds interesting but I am a bit intimidated to implement. 🙏
Thank you so much for taking the time to make this video for us who couldn't be at Brighton! All of your videos are super educational, and I think it's really unfortunate that your views are so low still, because you provide excellent content. But don't be discouraged by the number of views, because it's not a representation of the value that you are providing. I'm an extreme rookie when it comes to programming and ML, so I'm just barely inside your target audience here, but I love the science behind SEO, so I'm fascinated by what you talk about in videos. In this video you gave a good example of using sentiment analysis to see how other's are talking about your brand online, and said that this is useful for someone like a brand manager that needs to know the pain points to try to combat that. This was excellent and practical! Where I'm feeling a little bit lost though, is the other aspects of ML that you talk about here and in your other videos and being able to understand the practical application of what you are showing. I can understand how I could assign these tasks to one of my employees, and they can learn this and do what you're saying, generating the reports and data....however what I personally am not able to comprehend is how to actually take action on that after we have this data. You mention zooming in and out of the data, and I know that someone with a ton of SEO experience will know how to use this data...but I think maybe I'm not alone in not being able to see how to apply these things that you are talking about on an actual website. Of course the application of this would be very different whether it's on a 10,000 page website or a 100 page website..and I'll never touch a 10,000 page website, but I work with 300 page and below sites everyday. I don't want to ask you to make a video just for me..but I'm pretty confident that I'm probably not the only one that would appreciate more concrete examples of how to apply what you are showing us in your videos. Not A-Z necessarily, but just "here's an example of how you can actually use this data." Maybe using a demo site, or your own site or something. With your GPT-3 skills I imagine you could spin up a 50 page website in a few minutes lol, so maybe that could be an option. But if you could show the practical application of what you are teaching, then I know at least I would REALLY appreciate it :) Either way, keep up the good work!
🎯 Key Takeaways for quick navigation:
00:00 🎙️ *Introduction to Presentation*
- Overview of the purpose of the video.
- Encouragement to watch the live recording for a more detailed experience.
- Emphasis on a relaxed discussion about the presentation content.
00:53 📊 *Importance of SERP Analysis*
- Highlighting three significant numbers related to Google's SERP experiments.
- Emphasis on the market share dominance of Google's search engine.
- Discussion on the evolving nature of Google's SERP and its various functions.
04:21 🌐 *Google's Objectives on SERP*
- Detailed explanation of Google's objectives on the SERP.
- Exploration of how Google aims to communicate with users through SERP.
- Discussion on recent additions like knowledge panels, DIY tutorials, and e-commerce integration.
06:09 📚 *New Approach to Content Planning*
- Shift from traditional content planning to a user-centric approach.
- Importance of studying the SERP for user intent before creating content.
- The significance of competitor analysis and optimization for optimal performance.
08:27 🔍 *Three Categories of Recommendations*
- Introduction to the three recommendation categories: Must-Haves, Should-Haves, Could-Haves.
- Brief explanation of the purpose and distribution of effort for each category.
- Setting the stage for detailed recommendations in the subsequent sections.
13:19 📈 *Competitor Analysis - Must-Have*
- Detailed steps for conducting competitor analysis using Data for SEO.
- Importance of understanding competitors' search visibility and traffic potential.
- Visualization techniques, such as positioning maps, to analyze competitor data.
15:48 🌐 *Competitor Categorization - Should-Have*
- Categorization of competitors based on site type (Wiki, Courses, News, etc.).
- Discussion on the importance of understanding the landscape and identifying opportunities.
- Visual representation of competitor types for comprehensive analysis.
18:34 🎯 *Intent Categorization - Should-Have*
- Classification of competitors based on informational, transactional, or navigational intent.
- Insights into how Google positions different types of websites.
- Importance of understanding user intent categories for strategic content creation.
21:21 📊 *Share of Search Analysis - Could-Have*
- Introduction to calculating share of search per domain and per intent category.
- Brief explanation of the potential of Data for SEO's bulk SERP API for large-scale analysis.
- Emphasis on the flexibility and scalability of the analysis process.
23:39 🔄 *Advanced SERP Analysis with GPT-3 - Could-Have*
- Integration of GPT-3 for classifying site categories and search intent.
- Acknowledgment and credit to Danny Richmond for the script.
- Exploration of using AI for more detailed analysis of title and meta description content.
24:48 🧠 *Closing and Looking Forward*
- Summary of analysis prerequisites: keyword research, content strategy, and brand voice guidelines.
- Acknowledgment of limitations and anticipation of feedback from the audience.
- Expressing interest in the broader industry's contributions to SERP analysis.
25:02 📊 *SERP Analysis Overview*
- Analyzing top categories, site types, and SERP makeup based on domains and keywords.
- Identifying domains ranking for the highest number of distinct keywords.
28:14 🧠 *Advanced Entity Analysis Setup*
- Introduction to entity extraction using Google's Natural Language API.
- Importance of understanding entities, salience, sentiment score, and sentiment magnitude.
30:49 🚀 *Entity Analysis Visualizations*
- Visualizing common entities in meta descriptions across SERPs.
- Analyzing sentiment and positioning of entities using a positioning map.
34:12 🤖 *Machine Learning Insights*
- Discussing rank prediction based on entities and Knowledge Graph.
- Caveats and the need for a large historic dataset for effective rank prediction.
34:53 😃 *Sentiment Analysis for Brand Reputation*
- Utilizing sentiment analysis for brand reputation management.
- Filtering for negative and positive sentiments to identify brand advocates and opponents.
41:25 🔍 *Sentiment and Positioning Relationships*
- Filtering sentiment and positioning maps for specific keywords/entities.
- Understanding sentiment relationships for targeted keywords.
43:59 📈 *Expanded Use of Entity Analysis*
- Repurposing entity extraction scripts for internal link opportunities and content understanding.
- Highlighting the industry's underutilization of available technologies.
46:18 🔍 *Search Intent Analysis Process*
- Introduction to the process of labeling titles and meta descriptions for search intent.
- Use of GPT-3 for funnel-based and desired action intent classification.
48:07 📝 *Ranking Content Understanding*
- Scrutinizing content elements that influence ranking for specific intent-labeled keywords.
- Analyzing and visualizing factors contributing to content classification.
48:46 🌐 *Advanced Machine Learning for Search Intent*
- Classifying search intent based on featured snippet types.
- Exploring the potential of training custom models for micro-moments.
49:54 🧑💻 *Language Use Analysis with GPT-3*
- Using GPT-3 to assist in writing Apps Script formulas for language analysis.
- Practical application for non-coders to leverage advanced language models.
50:21 🧠 *Engram Analysis using GPT-3*
- The speaker discusses using GPT-3 to create an Apps Script formula for identifying bigrams in titles or meta descriptions.
- GPT-3 was utilized to generate a JavaScript function for identifying main engrams.
- The analysis aims to uncover prominent keyword combinations and understand what content Google serves for specific keywords.
52:49 🕵️ *Rank Analysis Caution*
- The speaker advises caution regarding rank analysis due to uncertainties and guidelines surrounding scraping and monitoring Google's SERPs.
- Basic evaluation of ranking positions, feature snippet appearances, and rudimentary monitoring of changes over time is discussed.
- The speaker suggests predictions, such as predicting organic CTR increase from position changes and predicting SERP makeup changes based on seasonality or events.
55:50 🚀 *Closing Remarks and Encouragement*
- The speaker concludes the presentation by encouraging viewers to explore machine learning themselves.
- Mention of follow-up videos on using the provided dashboard and template sheets.
- Emphasis on the importance of correlation not equaling causation and the role of analysis in guiding strategy rather than replacing it.
Made with HARPA AI
Hi Lazarina
You just got a new subscriber from Dubai, I didn't even realize that how I spent 58 minutes here because of the quality content and a smiling face of yours.
Keep publishing the quality content please 😍
Awesome! Thank you!
Hello Lazarina, thank you for putting this amazing presentation together. This has really opened up a whole world of value for me.
Is there any tutorial video or blog post to set this whole process up and running?
I am a no coder so this sounds interesting but I am a bit intimidated to implement. 🙏
Hi Nikhil,
Thanks for your kind feedback. I'm currently working on videos for these items, which you can expect in the new year.
Thank you!
Thank you so much for taking the time to make this video for us who couldn't be at Brighton!
All of your videos are super educational, and I think it's really unfortunate that your views are so low still, because you provide excellent content. But don't be discouraged by the number of views, because it's not a representation of the value that you are providing.
I'm an extreme rookie when it comes to programming and ML, so I'm just barely inside your target audience here, but I love the science behind SEO, so I'm fascinated by what you talk about in videos.
In this video you gave a good example of using sentiment analysis to see how other's are talking about your brand online, and said that this is useful for someone like a brand manager that needs to know the pain points to try to combat that. This was excellent and practical!
Where I'm feeling a little bit lost though, is the other aspects of ML that you talk about here and in your other videos and being able to understand the practical application of what you are showing.
I can understand how I could assign these tasks to one of my employees, and they can learn this and do what you're saying, generating the reports and data....however what I personally am not able to comprehend is how to actually take action on that after we have this data.
You mention zooming in and out of the data, and I know that someone with a ton of SEO experience will know how to use this data...but I think maybe I'm not alone in not being able to see how to apply these things that you are talking about on an actual website.
Of course the application of this would be very different whether it's on a 10,000 page website or a 100 page website..and I'll never touch a 10,000 page website, but I work with 300 page and below sites everyday.
I don't want to ask you to make a video just for me..but I'm pretty confident that I'm probably not the only one that would appreciate more concrete examples of how to apply what you are showing us in your videos. Not A-Z necessarily, but just "here's an example of how you can actually use this data."
Maybe using a demo site, or your own site or something. With your GPT-3 skills I imagine you could spin up a 50 page website in a few minutes lol, so maybe that could be an option. But if you could show the practical application of what you are teaching, then I know at least I would REALLY appreciate it :)
Either way, keep up the good work!
Thanks for the comment. I'll keep this in mind for future videos.
hey how do we discuss a project with you ..for hiring purpose etc?
please see the contact information associated with my channel :)