Mining the SERP for SEO, Content & Customer Insights

Transcript

1. Mining the SERPs for SEO, Content & Customer Insights Rory Truesdale // Conductor http://cndr.co/brighton @RoryT11
2. About Me Rory Truesdale •SEO Strategist at Conductor •EMEA SEO lead for WeWork Get In Touch brightonseo@conductor.com @RoryT11 @RoryT11
3. Get The Slides @RoryT11 http://cndr.co/brighton
4. •SERPs are a great 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 often Google rewrites the SERP displayed meta description
6. WHY?
7. To make SEOs sad?
8. Just for a laugh?
9. Nope…
10. It’s because Google thinks it is smarter than us
11. Intriguing… Can we use that to our advantage?
12. Yes, we can! (sorry, that was the last puppy pic)
13. How? @RoryT11
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 because we are in the age of semantic search @RoryT1
16. Google isn’t ranking a 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 & desire to better tailor your messaging @RoryT1
19. Structure landing pages to help Google understand context & how it meets the needs of the searcher @RoryT1
20. Build more meaningful online experiences that better convert website visitors @RoryT1
21. Your Toolkit @RoryT11
22. You need SERP content There are three ways you can get this. @RoryT1
23. Scrape at scale with Screaming Frog Follow these instructions @RoryT1 Option A
24. Option B Get SERP content via an API
25. Option C Get SERP content using the Scraper Chrome extension Get Scraper
26. There are four ways you can get this. You need Jupyter Notebook What is that?
27. 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
28. Stumped? Me too… Here’s my definition
29. 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. Rory Truesdale Python Charlatan @RoryT1
30. Resources to get started… Jupyter Notebook – Getting Started Guide Robin Lord Find scripts Paul Shapiro JR Oakes Hamlet Batista Find scripts Find scripts
31. You’ll end up with… @RoryT11
32. Your SERP content in a CSV @RoryT1
33. Imported into Jupyter Notebook @RoryT1
34. You’re ready to use Python to analyse the SERPs! @RoryT1
35. There’s a treat for you.
36. I’ll share a link to a Dropbox with everything you need to get you started @RoryT11
37. Before we dive in…
38. Start by cleaning your SERP content @RoryT1
39. Lower case avoids duplication & punctuation adds no value to this analysis Lower Case & Remove Punctuation @RoryT1
40. Stop words are commonly occurring words that don’t improve our analysis Remove Stop Words @RoryT1
41. The process of chopping up a sentence into individual pieces, called ‘tokens’ Tokenization @RoryT1
42. The process of converting a word to its root (i.e. “playing” becomes “play”) Lemmatization (optional) @RoryT1
43. @RoryT11
44. @RoryT11
45. How many times does a word or combination of words appear in your SERP content? Co-occurrence @RoryT1
46. Co- occurrence Snapshot of phrases frequently occurring in the SERPs @RoryT1
47. Co- occurrence Demonstrates the topics competitors cover on landing pages @RoryT1
48. Co- occurrence Understand the types of phrases that Google sees as semantically relevant to a target keyword set
49. •Additional source of data 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: 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
51. What are the most frequently occurring nouns, verbs & adjectives in a SERP? Part of Speech Tagging @RoryT1
52. PoS Tagging Uncover the phrases or topics you should include in your landing pages to rank for a term Nouns (people, place, thing) @RoryT1
53. PoS Tagging Get clues around how Google is interpreting the context and intent of a search Verbs (action or state) @RoryT1
54. PoS Tagging Understand the language and tone that 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 pages that 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 use NLP to uncover topical trends in the SERPs to help us with content ideation? Topic Modelling @RoryT1
60. 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
61. Topic Modelling OK, computer. Here’s some words. Group them. @RoryT1 Rory Truesdale Cheapening machine learning since 2019
62. Topic Modelling Each bubble represents a topic @RoryT1
63. Topic Modelling The bigger the bubble the more prominent the topic @RoryT1
64. Topic Modelling The further away the bubbles are, the more distinct those topic are
65. Topic Modelling Get a breakdown of the terms our topics consist of @RoryT1
66. Topic Modelling The output is an interactive visual on topical trends that can be easily shared with other teams @RoryT1
67. Topic Modelling Use Google’s algorithm to help us identify areas of interest for our audience
68. Topic Modelling Uncover topical trends hidden in the language of the SERPs that can inform content ideation @RoryT1
69. •Valuable data point to 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 we make our scripts work across other data sources to understand our customers? Other Useful Applications @RoryT1
71. Product Reviews @RoryT11
72. Product Reviews @RoryT11
73. GMB Reviews @RoryT11
74. GMB Reviews @RoryT11
75. Reddit @RoryT11
76. Reddit @RoryT11
77. YouTube Captions @RoryT11
78. YouTube Captions @RoryT11
79. Competitors & Top Ranking Pages @RoryT11
80. Competitors & Top Ranking Pages @RoryT11
81. With some minor tweaks we can make our scripts work across a huge corpus of user- centric content Pretty cool, right? @RoryT1
82. Potential to ramp up and apply sentiment analysis to these sources for useful visualisations @RoryT11
83. Deconstruct product reviews to find out what really matters to customers •Simple •Easy to use •Intuitive •Buggy •Slow @RoryT1
84. A lot to take in…what does it all mean? @RoryT11
85. SERPs give us amazing insight into what customers want @RoryT1
86. Python makes getting these insights at scale accessible @RoryT1
87. Use these insights to align landing pages with intent and semantic relevance @RoryT1
88. Scripts we create allow us to get these insights from lots of other user-centric sources beyond the SERPs @RoryT1
89. http://cndr.co/jupyter Python Dropbox Link @RoryT1
90. Get The Slides @RoryT11 http://cndr.co/brighton
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
92. Thanks For Listening! Conductor.com @RoryT11 brightonseo@conductor.com