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BrightonSEO - George Karapalidis

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Predicting keyword revenue using machine learning and statistical models

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BrightonSEO - George Karapalidis

  1. 1. George Karapalidis VERTICAL LEAP Predicting keyword revenue @gkarapalidis https://www.slideshare.net/GeorgeKarapalidisMBA
  2. 2. mail: @verticalleap
  3. 3. mail: SEO is changing Embrace the mindset of a data scientist. #2MA @verticalleap
  4. 4. mail: Great SEO opportunities are often well hidden. @verticalleap
  5. 5. mail: Small improvements can help us achieve consistent incremental growth. @verticalleap
  6. 6. mail: We need to start collecting data, and find new ways to understand it and use it. @verticalleap
  7. 7. Revenue by search query 98% Not provided @verticalleap
  8. 8. mail: How much revenue do we get from generic versus branded search queries? @verticalleap
  9. 9. mail: @verticalleap Generic clicks equal 70% of total clicks. Known: Generic search drives 70% of total revenue. Assumed: = The model
  10. 10. mail: @verticalleap What we do and don’t know.
  11. 11. @verticalleap
  12. 12. @verticalleap 1. We know the organic performance of landing pages.
  13. 13. @verticalleap 2. We know the transactional data for each landing page.
  14. 14. @verticalleap 3. We don’t know for which queries a landing page appeared in SERPs.
  15. 15. @verticalleap 4. We don’t know the transactional data for each search query.
  16. 16. @verticalleap 5. We know the pages that appeared in SERPs and their performance.
  17. 17. @verticalleap 6. We know the performance of each search query in SERPs.
  18. 18. @verticalleap 7. We know for which search queries a page appeared in SERPs.
  19. 19. @verticalleap We know the organic performance of landing pages. We know the transactional data for each landing page. We don’t know for which queries a landing page appeared in SERPs. We don’t know transactional data for each search query. We know the pages that appeared in SERPs and their performance. We know the performance of each search query in SERPs. We know for which search queries a page appeared.
  20. 20. mail: To predict a future increase in revenue, estimate the revenue a query generated in the past. @verticalleap
  21. 21. mail: @verticalleap Create a statistical model using the known values from GA and GSC to estimate revenue for each search query.
  22. 22. @verticalleap Import Prepare AnalyseClean The estimation process
  23. 23. @verticalleap The output Est. Acc. Landing Page Sessions Transactions Revenue Page Query Clicks Impressions CTR Position SERP Click % Est. Trans Est. Revenue Est. Sessions Accuracy / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop 45177 75021 60% 1.6 1 90.95% 7854 £ 422,293.42 56727 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowershop 1288 2121 61% 1.3 1 2.59% 224 £ 12,039.62 1617 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowershop.com 677 1621 42% 1.0 1 1.36% 118 £ 6,328.28 850 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowe shop 464 797 58% 1.3 1 0.93% 81 £ 4,337.25 583 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop.com 357 583 61% 1.0 1 0.72% 62 £ 3,337.07 448 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowr shop 156 206 76% 1.0 1 0.31% 27 £ 1,458.21 196 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ www.flowershop.com 135 260 52% 1.0 1 0.27% 23 £ 1,261.92 170 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop uk 129 169 76% 1.0 1 0.26% 22 £ 1,205.83 162 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop garden 128 413 31% 1.6 1 0.26% 22 £ 1,196.48 161 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shops 124 230 54% 1.3 1 0.25% 22 £ 1,159.09 156 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shp 74 109 68% 1.1 1 0.15% 13 £ 691.72 93 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop seeds 70 3626 2% 7.0 1 0.14% 12 £ 654.33 88 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop catalogue 66 292 23% 2.0 1 0.13% 11 £ 616.94 83 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop plants uk 65 153 42% 1.0 1 0.13% 11 £ 607.59 82 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower sho 63 122 52% 1.2 1 0.13% 11 £ 588.89 79 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower sop 63 81 78% 1.0 1 0.13% 11 £ 588.89 79 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop plants 61 2039 3% 3.2 1 0.12% 11 £ 570.20 77 74%
  24. 24. @verticalleap The output Est. Acc. Landing Page Sessions Transactions Revenue Page Query Clicks Impressions CTR Position SERP Click % Est. Trans Est. Revenue Est. Sessions Accuracy / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop 45177 75021 60% 1.6 1 90.95% 7854 £ 422,293.42 56727 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowershop 1288 2121 61% 1.3 1 2.59% 224 £ 12,039.62 1617 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowershop.com 677 1621 42% 1.0 1 1.36% 118 £ 6,328.28 850 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowe shop 464 797 58% 1.3 1 0.93% 81 £ 4,337.25 583 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop.com 357 583 61% 1.0 1 0.72% 62 £ 3,337.07 448 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flowr shop 156 206 76% 1.0 1 0.31% 27 £ 1,458.21 196 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ www.flowershop.com 135 260 52% 1.0 1 0.27% 23 £ 1,261.92 170 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop uk 129 169 76% 1.0 1 0.26% 22 £ 1,205.83 162 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop garden 128 413 31% 1.6 1 0.26% 22 £ 1,196.48 161 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shops 124 230 54% 1.3 1 0.25% 22 £ 1,159.09 156 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shp 74 109 68% 1.1 1 0.15% 13 £ 691.72 93 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop seeds 70 3626 2% 7.0 1 0.14% 12 £ 654.33 88 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop catalogue 66 292 23% 2.0 1 0.13% 11 £ 616.94 83 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop plants uk 65 153 42% 1.0 1 0.13% 11 £ 607.59 82 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower sho 63 122 52% 1.2 1 0.13% 11 £ 588.89 79 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower sop 63 81 78% 1.0 1 0.13% 11 £ 588.89 79 74% / 62371 8636 £ 464,310.57 https://www.flowershop.com/ flower shop plants 61 2039 3% 3.2 1 0.12% 11 £ 570.20 77 74%
  25. 25. mail: @verticalleap How can we predict revenue potential by search query? What’s next?
  26. 26. @verticalleap More traffic = More transactions
  27. 27. @verticalleap Output based on estimations Strongest opportunities for growth
  28. 28. @verticalleap FlowerShop roses FlowerShop tulips FlowerShop lilies High transactional intent Medium to low intent Low intent High to medium intent Buy roses Buy tulips online Red roses FlowerShop plants FlowerShop seeds FlowerShop flowers Plants for winter Flowers for spring How to plant tulips Branded Generic Intent indication model Brand aware Brand unaware ProductawareProductunaware
  29. 29. mail: @verticalleap Using ML or DL we can classify search queries based on brand and product awareness.
  30. 30. @verticalleap We need to train our model to help it understand what a brand or product search query looks like. Train the model
  31. 31. @verticalleap Deep learning model Importing datasets Tokenisation process Neural network Training Testing
  32. 32. @verticalleap The output We categorise each search query using a probability model. “FlowerShop roses” -------------- Predicted Intent Cat.: BAPA Brand Aware Product Aware -------------- BUPA Probability: 0.002% BAPU Probability: 24% BAPA Probability: 70%
  33. 33. mail: @verticalleap Using the search query category, we improve our CTR-to-revenue prediction. What’s next?
  34. 34. @verticalleap CTR for branded vs generic queries in top three positions
  35. 35. mail: @verticalleap That’s not all. Let’s examine time.
  36. 36. mail: @verticalleap We need to know the low and high peaks, to identify when to expect an increase in revenue.
  37. 37. mail: @verticalleap Historical data can help us identify the best times to test our model.
  38. 38. @verticalleap Time series data
  39. 39. mail: Seasonality patterns by product and category @verticalleap
  40. 40. mail: @verticalleap Is it worth the effort?
  41. 41. mail: Boosting one product range @verticalleap 30/01/2018 Opportunity 1st wave of improvements 2nd wave of improvements Traffic and revenue increased
  42. 42. mail: YoY performance gains @verticalleap -17% in revenue +6% in revenue
  43. 43. mail: 1. 2. 3. SEO has changed @verticalleap
  44. 44. mail: 1. Become a data scientist. 2. 3. SEO has changed @verticalleap
  45. 45. mail: 1. Become a data scientist. 2. Be great at data collection. 3. SEO has changed @verticalleap
  46. 46. mail: 1. Become a data scientist. 2. Be great at data collection. 3. Automate the analysis. SEO has changed @verticalleap
  47. 47. mail: Speed is our friend If it takes a month to realise an improved outcome, we need to identify a great opportunity in less than an hour. @verticalleap
  48. 48. mail: Thank you! gkarapalidis@vertical-leap.uk @verticalleap @verticalleap

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