Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Bringing the fun back to SEO with Python

761 views

Published on

In case you missed my presentation or you are interested in the resources which helped me to learn Python - here are all my slides.

Published in: Technology
  • Be the first to comment

Bringing the fun back to SEO with Python

  1. 1. SLIDESHARE.NET/BGOERLER BRINGING THE FUN BACK TO SEO WITH PYTHON BENJAMIN GÖRLER // AYIMA // @ayima
  2. 2. BRACE YOURSELVES INFORMATION OVERLOAD IS COMING!
  3. 3. 48000 50000 52000 54000 56000 58000 60000 62000 2000 2002 2004 2006 2008 2010 2012 2014 2016 Total number of pubs in the UK Source: BBC (2018)
  4. 4. 48000 50000 52000 54000 56000 58000 60000 62000 2000 2002 2004 2006 2008 2010 2012 2014 2016 Total number of pubs in the UK v9.0 v10.0 v11.0 v12.0 v14.0 v15.0 v16.0 Source: BBC (2018)
  5. 5. YOU SPEND 4 HOURS PER WEEK USING EXCEL
  6. 6. 16 HOURS PER MONTH
  7. 7. ~ 100 HOURS PER YEAR
  8. 8. WHAT IF I TOLD YOU YOU’RE WASTING YOUR TIME
  9. 9. Python To The Rescue What is Python? ● Easy to learn programming language ● Runs on any device Why Python? ● Steep, but quick learning curve ● Almost unlimited use cases ● Tons of resources provided by a massive online community ● You can use it without actually ‘speaking’ any Python ● It’s scalable! ● Cause it’s fun!
  10. 10. WHY SHOULD I BOTHER TO LEARN IT?
  11. 11. You already possess an essential skill! 1 x 8 + 1 = 9 12 x 8 + 2 = 98 123 x 8 + 3 = 987 1234 x 8 + 4 = 9876 12345 x 8 + 5 = 98765 123456 x 8 + 6 = 987654 1234567 x 8 + 7 = ??????? Spot the pattern
  12. 12. Problem Pattern Python Solving Problems with Python
  13. 13. Scenario #1 Site crawl of 5 Million URLS
  14. 14. https://www.screwfix.com/c/decorating/paint/c at850142#category=cat850142&brand=dulux Analysing URL parameters
  15. 15. & {word} = Pattern
  16. 16. The Excel Way #1 • Opening the crawl 10 mins • Filtering and cleaning the data 15 mins • Performing VLOOKUPs 10 mins • Analysing the data 30 mins = 1 hour in total
  17. 17. The Python Way
  18. 18. python paramsfinder.py -i crawl.csv -o output.csv The Python Way Evoke python Script name input argument output argument input file output file
  19. 19. python paramsfinder.py -i crawl.csv -o output.csv The Python Way Parameter Occurrences Example &brand= 11,655 https://www.screwfix.com/c/decorating/paint/cat8501 42#category=cat850142&brand=dulux_trade &colour= 24,182 https://www.screwfix.com/c/decorating/paint/cat8501 42#category=cat850142&colour=white &price= 144,693 https://www.screwfix.com/c/decorating/paint/cat8501 42#category=cat850142&colour=white&price=5.0&pric e_to=25.0
  20. 20. Digestible Data 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 Paint Parameters By Category brand colour price
  21. 21. Time Spent Using Python 1 2 3 4 Developing the script 20 mins Entering the command 10 secs Opening the crawl 5 secs Running the script 15 secs ~ 20 mins in total
  22. 22. HOW MANY FRIDAY PINTS COULD I HAVE HAD?
  23. 23. Let’s ask Google
  24. 24. Success Metric 10 Minutes =
  25. 25. Success
  26. 26. Scenario #2: Keyword Reporting
  27. 27. Raw Data Export From Third Party Tool Keyword Monthly Traffic Rank Ranking URL bags 9,500 1 https://www.asos.com/women/bags-purses/cat/?cid=8730 hoodies 3,100 1 https://www.asos.com/men/hoodies-sweatshirts/cat/?cid=5668 jumpsuit 13,500 5 https://www.asos.com/women/jumpsuits-playsuits/cat/?cid=7618
  28. 28. Categorise keywords automagically Dresses Jeans Jumper Shoes Accessories wedding guest dresses boyfriend jeans mens turtleneck loafers for men wallet maternity dress black skinny jeans half zip jumper flip flops belt party dress mom jeans jumper dress espadrille wedges hat burgundy grey ripped jeans cropped jumper sandals mens scarf
  29. 29. Pattern #1 https://www.asos.com /women -> Women /dresses -> Dresses
  30. 30. The Excel Way #2 • Filtering and cleaning the data 20 mins • Developing formulas 120 mins • Categorising the data 90 mins 4 hours in total
  31. 31. python analyse-kw.py -i keywords.csv -o analysis.csv The Python Way #2 1 2 3 4 Developing the script 60 mins Entering the command 10 secs Opening the keyword export 5 secs Running the script 5 secs
  32. 32. 72% 28% Monthly Traffic Women Men
  33. 33. Reporting Made Simple 4.3 2.5 3.5 4.55.4 3.9 7.5 2.8 8.2 2.0 18.0 5.0 This Month Last Month Last Year Avg. Rank Department Shoes Dresses Jeans Accessories Page 1 Page 2
  34. 34. Success
  35. 35. You Can Potentially Automate Everything With Python Facet Optimisation Reporting Keyword Research Internal Linking Analysis Redirect Hops Ad Campaigns
  36. 36. Today ● Set up the Python environment ● Learn about Command Line Interface (CLI) ● Run your first script! In a Week ● Support some workflows with existing scripts ● Tweak scripts for own scenarios In a Month ● Write custom scripts ● Query data from various sources ● Automate reporting Start Saving Time Today
  37. 37. Useful resources Prerequisites • Command Line (Windows) • Terminal (Mac) • Python Environment Using Python • Python tutorial • Online community • Ayima’s blog
  38. 38. Contact Benjamin Görler benjamin@ayima.com

×