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Alexis Sanders — Scaling Ranking Data Analysis: Organizing, Visualizing, and Having Some Fun with SEMrush Data

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These slides were presented at the SEMrush webinar "5 Hours of SEO | Scaling Ranking Data Analysis: Organizing, Visualizing, and Having Some Fun with SEMrush Data". Video replay and transcript are available at https://www.semrush.com/webinars/5-hours-of-seo-or-scaling-ranking-data-analysis-organizing-visualizing-and-having-some-fun-with-semrush-data/

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Alexis Sanders — Scaling Ranking Data Analysis: Organizing, Visualizing, and Having Some Fun with SEMrush Data

  1. 1. @alexisksanders| 1 @alexisksanders| Scaling ranking data analysis: organizing, visualizing, and having some fun with SEMrush data
  2. 2. @alexisksanders| What are we going to do today? 1.Get some SEMrush ranking data 2.Segment keywords using Jupyter Notebook 3.Load the data into Power Bi 4.Visualize data
  3. 3. @alexisksanders| Why? • 1st-party data > 3rd-party data > no data • insights > data • efficiency > work time • fun > mundane parts of analysis
  4. 4. @alexisksanders| Step 1: Get SEMrush data y’all know how to do this, let’s use chipotle!
  5. 5. @alexisksanders| Step 2: Open Jupyter Notebook (can also use Google colabs here) https://www.anaconda.com/distributi on/
  6. 6. @alexisksanders| Step 3: Import pandas that’s all we need to start
  7. 7. @alexisksanders| Step 4: pandas.read_csv(data) everything is easiest if it’s in the same folder (buuuuuuuut who doesn’t love living outside the box, on the edge)
  8. 8. @alexisksanders| Step 5: Setting up f(x) to segment brand vs. non- it’s all a big if- else statemen t (since python doesn’t have switch statements)
  9. 9. @alexisksanders| What sorts  SERP intents (proxy) shopping ads adwords (top & bottom) local pack featured snippets people also ask knowledge panel FAQ job search site links video featured video top stories twitter reviews flights image pack AMP video carousel featured image hotels pack buy go learn get a job navigation al other this can be organized however you want
  10. 10. @alexisksanders| Step 6: Setting up a f(x) to segment SERP intent we’re going to take what we used for brand and modify
  11. 11. @alexisksanders| @alexisksanders| … more f(x) creation …
  12. 12. @alexisksanders| What sorts  digging into informational menu nutrition recipe price deals rewards special days hours take-out delivery catering near me feedback corporat e gift card can be whatever you want
  13. 13. @alexisksanders| What sorts  products bowl burrito taco quesadill a salad
  14. 14. @alexisksanders| What sorts  meats sofritas chicken steak carnitas veggiebarbacoa
  15. 15. @alexisksanders| It looks like it’s softritas…
  16. 16. @alexisksanders| Step 7: dataframe.to_csv(categorized-data) export, will go to the folder noteboo k is in
  17. 17. @alexisksanders| Step 8: Review data in Excel (then make logic adjustments) clean the data, modify logic (e.g., [chip])
  18. 18. @alexisksanders| Step 9: Load data in Power Bi (or other BI visualization software) [Get data] > load .csv data
  19. 19. @alexisksanders| Example 1: What are the top subdomains?
  20. 20. @alexisksanders| And… we make the chart look nice in the style tab
  21. 21. @alexisksanders| Example 2: What are the top SERP intents?
  22. 22. @alexisksanders| Very high likelihood of hitting a local pack in the SERP not necessari ly surprisin g for a local business, but hey – the more you
  23. 23. @alexisksanders| Very high likelihood of hitting a local pack in the SERP and… non- brand is similar (buuuut with less ads)
  24. 24. @alexisksanders| Example 3: What are the top intents?
  25. 25. @alexisksanders| This one got a bit meta… it’s the intent’s I made up overlaid with the SERP intents (based on SERP features that
  26. 26. @alexisksanders| Example 4: What the top products by meat?
  27. 27. @alexisksanders| Chicken bowl for the win… who knows … maybe people want to know what sofritas it, but don’t necessarily want to eat it…
  28. 28. @alexisksanders| Example 5: What are top nb keywords & their intent?
  29. 29. @alexisksanders| Example 6: What are top features?
  30. 30. @alexisksanders| Example 7: What are top questions?
  31. 31. @alexisksanders| Tofu… it’s tofu… for calories (cough…and macros…cough), Chipotle actually has a nutrition calculator, you can add your usual order (note: I didn’t write you should…)
  32. 32. @alexisksanders| Example 8: What are top nb kws by traffic and cost?
  33. 33. @alexisksanders| @alexisksanders| http://bit.ly /dealing-with-ranking- data
  34. 34. @alexisksanders| @alexisksanders| Thank you!

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