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BrightonSEO 2019 - Mining the SERP for SEO, Content & Customer Insights
- 1. Mining the SERPsfor SEO, Content & Customer Insights Rory Truesdale // Conductor http://cndr.co/brighton @RoryT11
- 2. About Me Rory Truesdale •SEOStrategist at Conductor •EMEA SEO lead for WeWork Get In Touch brightonseo@conductor.com @RoryT11 @RoryT11
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- 4. •SERPs are agreat resource to learn what Google ‘thinks’ our customers want •Workflows that will help you understand the intent of the people you want to reach •How to use these insights to improve the quality of your on-page optimisation What To Expect @RoryT1
- 5. That’s how oftenGoogle rewrites the SERP displayed meta description
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- 10. It’s because Googlethinks it is smarter than us
- 11. Intriguing… Can we usethat to our advantage?
- 12. Yes, we can! (sorry, thatwas the last puppy pic)
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- 14. We can deconstruct& analyse the language in SERP displayed content to learn what Google thinks our customers are interested in @RoryT1
- 15. Curious? This is important becausewe are in the age of semantic search @RoryT1
- 16. Google isn’t rankinga page based on how it uses a keyword. Google provides accurate results based on intent, query context & word relationships. On-page Optimisation @RoryT1
- 17. • User intent •Query context •Topical relevance • Word relationships Target the keyword, but optimise for this. On-page Optimisation @RoryT1
- 18. Understand customer intent & desireto better tailor your messaging @RoryT1
- 19. Structure landing pages tohelp Google understand context & how it meets the needs of the searcher @RoryT1
- 20. Build more meaningful online experiences that betterconvert website visitors @RoryT1
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- 22. You need SERP content Thereare three ways you can get this. @RoryT1
- 23. Scrape at scalewith Screaming Frog Follow these instructions @RoryT1 Option A
- 24. Option B Get SERP contentvia an API
- 25. Option C Get SERP contentusing the Scraper Chrome extension Get Scraper
- 26. There are four waysyou can get this. You need Jupyter Notebook What is that?
- 27. The Jupyter Notebookis 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
- 28. Stumped? Me too… Here’s mydefinition
- 29. Jupyter Notebook isan 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
- 30. Resources to getstarted… Jupyter Notebook – Getting Started Guide Robin Lord Find scripts Paul Shapiro JR Oakes Hamlet Batista Find scripts Find scripts
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- 32. Your SERP contentin a CSV@RoryT1
- 33. Imported into JupyterNotebook @RoryT1
- 34. You’re ready to use Python toanalyse the SERPs! @RoryT1
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- 36. I’ll share alink to a Dropbox with everything you need to get you started @RoryT11
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- 39. Lower case avoids duplication& punctuation adds no value to this analysis Lower Case & Remove Punctuation @RoryT1
- 40. Stop words are commonly occurringwords that don’t improve our analysis Remove Stop Words @RoryT1
- 41. The process of choppingup a sentence into individual pieces, called ‘tokens’ Tokenization @RoryT1
- 42. The process of convertinga word to its root (i.e. “playing” becomes “play”) Lemmatization (optional) @RoryT1
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- 45. How many times doesa word or combination of words appear in your SERP content? Co-occurrence @RoryT1
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- 47. Co- occurrence Demonstrates the topics competitors cover onlanding pages @RoryT1
- 48. Co- occurrence Understand the types ofphrases that Google sees as semantically relevant to a target keyword set
- 49. •Additional source ofdata for keyword research •Identify topical content gaps on landing pages •Optimise landing page content by incorporating semantically relevant phrases HOW CAN WE APPLY THIS? @RoryT1
- 50. Cost: Range: Time to Charge: BatterySize/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
- 51. What are themost frequently occurring nouns, verbs & adjectives in a SERP? Part of Speech Tagging @RoryT1
- 52. PoS Tagging Uncover the phrases ortopics you should include in your landing pages to rank for a term Nouns (people, place, thing) @RoryT1
- 53. PoS Tagging Get clues around howGoogle is interpreting the context and intent of a search Verbs (action or state) @RoryT1
- 54. PoS Tagging Understand the language and tonethat might resonate with a searcher Adjectives (descriptive word) @RoryT1
- 55. PoS Tagging Credit Card Example– P1 Verbs Intent Clues: What is the specific motivation our searcher has?
- 56. PoS Tagging Credit Card Example- P1 Nouns Context Clues: Words that clarify meaning & help us understand what a searcher wants @RoryT1
- 57. PoS Tagging Credit Card Example- P1 Adjectives Context Clues: Words that clarify meaning & help us understand what a searcher wants @RoryT1
- 58. •Create landing pagesthat are aligned with the intent of a searcher •Help copywriters understand the language and desires of a target audience •Tactically incorporate more semantically relevant phrases into landing pages HOW CAN WE APPLY THIS? @RoryT1
- 59. Can we useNLP to uncover topical trends in the SERPs to help us with content ideation? Topic Modelling @RoryT1
- 60. Topic Modelling Topic modelling isan 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
- 61. Topic Modelling OK, computer. Here’ssome words. Group them. @RoryT1 RoryTruesdale Cheapening machine learning since 2019
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- 63. Topic Modelling The bigger the bubble themore prominent the topic @RoryT1
- 64. Topic Modelling The further away the bubblesare, the more distinct those topic are
- 65. Topic Modelling Get a breakdown of theterms our topics consist of @RoryT1
- 66. Topic Modelling The output isan interactive visual on topical trends that can be easily shared with other teams @RoryT1
- 67. Topic Modelling Use Google’s algorithm tohelp us identify areas of interest for our audience
- 68. Topic Modelling Uncover topical trends hiddenin the language of the SERPs that can inform content ideation @RoryT1
- 69. •Valuable data pointto reference for content ideation •Inform internal linking and content recommendations across a website •Incorporate topically relevant phrases into existing pages to improve semantic relevance HOW CAN WE APPLY THIS? @RoryT1
- 70. How can wemake our scripts work across other data sources to understand our customers? Other Useful Applications @RoryT1
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- 79. Competitors & Top RankingPages @RoryT11
- 80. Competitors & TopRanking Pages @RoryT11
- 81. With some minor tweakswe can make our scripts work across a huge corpus of user- centric content Pretty cool, right? @RoryT1
- 82. Potential to rampup and apply sentiment analysis to these sources for useful visualisations @RoryT11
- 83. Deconstruct product reviewsto find out what really matters to customers •Simple •Easy to use •Intuitive •Buggy •Slow @RoryT1
- 84. A lot to take in…what doesit all mean? @RoryT11
- 85. SERPs give us amazinginsight into what customers want @RoryT1
- 86. Python makes getting these insightsat scale accessible @RoryT1
- 87. Use these insights toalign landing pages with intent and semantic relevance @RoryT1
- 88. Scripts we create allowus to get these insights from lots of other user-centric sources beyond the SERPs @RoryT1
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- 91. • 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
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