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Semelhante a MancSEO - Using the SERPs for SEO, Content & Customer Insights
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MancSEO - Using the SERPs for SEO, Content & Customer Insights
- 1. Analysing the SERPS For SEO, Content & Customer Insights
- 2. That’s how often Google rewrites the SERP displayed meta description
- 3. WHY?
- 6. Nope…
- 7. It’s because Google thinks it is smarter than us
- 8. Intriguing… Can we use that to our advantage?
- 9. Yes, we can! (sorry, that was the last puppy pic)
- 10. How? @RoryT11
- 11. Deconstruct & analyse the language of the SERPs @RoryT1
- 12. Curious? We are in the age of semantic search @RoryT1
- 13. Google isn’t ranking a page based on how it uses a keyword On-page Optimisation @RoryT1
- 14. • User intent • Query context •Topical relevance • Word relationships Target the keyword, but optimise for this. How does Google provide accurate results? @RoryT1
- 15. Understand customer intent to better tailor your messaging @RoryT1
- 16. Structure landing pages to help Google understand context @RoryT1
- 19. You need SERP content There are four ways you can get this. @RoryT1
- 20. Scrape at scale with Screaming Frog Follow these instructions @RoryT1 Option A
- 21. Option B Get SERP content via an API
- 22. Option C Get SERP content using the Scraper Chrome extension Get Scraper
- 23. Option D Use ‘Thruuu’ – a free SERP analysis tool Use Thruuu By Samuel Schmitt
- 24. There are four ways you can get this. You need Jupyter Notebook What is that?
- 25. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. Jupyter.org @RoryT1
- 26. Stumped.
- 27. Jupyter Notebook is an environment on my laptop where I can learn Python by copying scripts created by people significantly smarter than me and breaking them or making them do something slightly different. RoryTruesdale Python Charlatan @RoryT1
- 28. Resources to get started… Jupyter Notebook – Getting Started Guide Robin Lord Find scripts Paul Shapiro JR Oakes Hamlet Batista Find scripts Find scripts
- 30. Your SERP content in a CSV@RoryT1
- 31. Imported into Jupyter Notebook @RoryT1
- 32. You’re (nearly) ready to use Python to analyse the SERPs! @RoryT1
- 33. There’s a treat for you.
- 34. I’ll share a link to a Dropbox with everything you need to get you started @RoryT11
- 37. Avoids duplication & punctuation adds no value Lower case & remove punctuation @RoryT1
- 38. Get rid of words like: “Do” “Of” “Am” “If” “But” Remove stop words @RoryT1
- 39. Chop up a sentence into individual pieces Tokenization @RoryT1
- 40. Convert a word to its root: -‘Playing’ > ‘Play’ -‘Crawling’ > ‘Crawl’ Lemmatization (optional) @RoryT1
- 41. @RoryT11
- 42. @RoryT11
- 43. How many times a combination of words appear in your SERP content? Co-occurrence @RoryT1
- 45. Co- occurrence Shows the topics competitors cover on landing pages @RoryT1
- 46. Co- occurrence What does Google see as semantically relevant? @RoryT1
- 47. •Additional keyword research •Topical content gaps •Use semantically relevant phrases on landing pages HOW CAN WE APPLY THIS? @RoryT1
- 48. Cost: Range: Time to Charge: Battery Size/Capacity: All Wheel Drive: Towing Capacity: Semi-Conductor SERP XLT: Product Page £ 44,360 MSV 9,620 MSV 7,470 MSV 380 MSV 3,040 MSV 180 MSV
- 49. What are the most frequently occurring nouns, verbs & adjectives in a SERP? Part of Speech (PoS) tagging @RoryT1
- 50. PoS tagging What ‘things’ are competitors writing about? Nouns (people, place, thing) @RoryT1
- 51. PoS tagging How is Google interpreting the context and intent of a search Verbs (action or state) @RoryT1
- 52. PoS tagging The language & tone that will resonate with a searcher Adjectives (descriptive word) @RoryT1
- 53. PoS tagging Credit Card Example Verbs = Intent Clues
- 54. PoS tagging Credit Card Example Nouns = Context Clues @RoryT1
- 55. PoS tagging Credit Card Example Adjectives = Context Clues @RoryT1
- 56. •Align pages with the motivations of a searcher •What language will resonate with your target audience •Use to improve on page optimisation HOW CAN WE APPLY THIS? @RoryT1
- 57. Can we use NLP to uncover topical trends in the SERPs? Topic modelling @RoryT1
- 58. Topic modelling Topic modelling is an NLP method that assumes a corpus contains a mixture of topics. It looks at how words and phrases co-occur in a corpus and attempts to group them in coherent themes or topics. @RoryT1
- 59. Topic modelling OK, computer. Here are some words. Group them. @RoryT1 RoryTruesdale Cheapening machine learning since 2019
- 61. Topic modelling The bigger the bubble the more prominent the topic @RoryT1
- 62. Topic modelling The further away the bubbles are, the more distinct those topics are
- 63. Topic modelling Get a breakdown of the terms our topics consist of @RoryT1
- 65. Topic modelling Use Google’s algorithm to help us identify areas of interest for our audience
- 66. •Reference for content ideation •Internal linking and content recommendations •Optimise effectively for semantic relevance HOW CAN WE APPLY THIS? @RoryT1
- 67. Can we make our scripts work across other data sources to understand our customers? Other useful applications @RoryT1
- 70. Reddit @RoryT11
- 72. Competitors & Top Ranking Pages @RoryT11
- 74. With some minor tweaks we can make our scripts work across a huge range of user- centric content Pretty cool, right? @RoryT1
- 75. Visualise sentiment across GMB reviews @RoryT11
- 76. Positive: • Simple • Easy to use • Intuitive Negative: • Buggy • Broken exports • Crashes @RoryT1 Identify recurring themes in product reviews
- 77. Create networks based on word relationships @RoryT11
- 79. SERPs give us amazing insight into what customers want @RoryT1
- 80. Python makes getting these insights at scale accessible @RoryT1
- 81. Use these insights to align landing pages with intent and semantic relevance @RoryT1
- 82. Scripts we create allow us to get these insights from lots of other user-centric sources @RoryT1
- 84. Get the slides link @RoryT11 http://cndr.co/mancseo
- 85. • https://www.searchenginejournal.com/scrape-google-serp-custom-extractions/267211/ • https://www.searchenginejournal.com/mine-serps-seo-content-customer-insights/311137/ • https://www.seerinteractive.com/blog/user-testing-serps-an-audience-first-approach-to-seo/ • https://www.dropbox.com/sh/vl5miyt6sgbvmkl/AAC5365YcWTun_EzkQLtixe1a?dl=0 (Jupyter Notebook tutorial) • http://www.blindfiveyearold.com/algorithm-analysis-in-the-age-of-embeddings • https://www.searchenginejournal.com/semantic-search-seo/264037/#close • https://www.slideshare.net/DawnFitton/natural-language-processing-and-search-intent- understanding-c3-conductor-2019-dawn-anderson • https://moz.com/blog/what-is-semantic-search • https://www.slideshare.net/paulshapiro/redefining-technical-seo-mozcon-2019-by-paul-shapiro Useful Resources @RoryT1