Data and Evidence-driven SEO
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This document discusses using data and evidence-driven approaches with Python and machine learning for SEO. It covers hunting for evidence to leverage technological advances, scaling SEO success from keywords to image searches, turning negative reviews into positive insights through sentiment analysis, and how to get started with Python to analyze SEO data. The agenda also includes a section on turning review trash into gold with machine translation and sentiment style transfer.
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Data and Evidence-driven SEO
- 1. Data and evidence-driven SEO – Why and How to get started with Python and Machine Learning for SEO
- 2. AGENDA – A rocket ship is born 🚀 – Scaling success – From keywords to picture searches – Turning reviews trash into gold – How to get started –
- 3. 1. Hunt for evidence Leverage technology advances Follow Prof Richard Feyman @ProfFeyman
- 5. Maintain and increase the current search traffic Increase the search traffic that results in sales What kind of content works best More content ideas
- 6. SEL DATA BLENDING ARTICLE https://selnd.com/2EoPtdf – FROM DIGITAL OCEAN’S 2013 SEO AUDIT –
- 7. EXCEL
- 13. IN 2013 TODAY
- 15. Conversions by type of keywords
- 16. Most searchers were looking for help not to buy hosting
- 17. Write content around keywords that convert
- 21. MS LABS Redmond, WA APRIL 17-28, 2000
- 28. From keywords to image searches –
- 36. Missing Alt Text Attributes – Computer Vision
- 40. Turning Review Trash into Gold –
- 45. Sentiment style transfer (negative to positive or viceversa)
- 48. How to get started
- 51. How to use Python to Analyze SEO Data https://bit.ly/2UzUk0u
- 55. SUMMARY – A rocket ship is born 🚀 – Scaling success – From keywords to picture searches – Turning reviews trash into gold – How to get started –
- 56. ABOUT RANKENSE – Free Realtime SEO Monitor – Ongoing monitoring with no active crawls – Receive alerts about critical SEO issues – Apply quick, temporary fixes in Cloudflare – Create developer tickets for permanent solutions Apply for Your Invitation www.ranksense.com
Editor's Notes
- You can’t get there by just using excel.
- Here is the data you’ll need: Google Search Console: Linking page, Last crawled Google Analytics: ga:date, ga:newUsers Common columns (for the merge function): left_on: Last crawled, right_on: ga:date You can plot traffic and links over time. Optionally, you can add a calculated domain column to the Search Console data table. This will allow you to plot linking domains by traffic.
- https://towardsdatascience.com/style-transfer-styling-images-with-convolutional-neural-networks-7d215b58f461
- https://nlp.stanford.edu/pubs/li2018transfer.pdf https://github.com/lijuncen/Sentiment-and-Style-Transfer
- 1. Start with a simple task you need to do each day or week in Excel. For example, finding underperforming content by high volume keywords with no clicks in GSC 2. Break it down in steps: a) collecting GSC data manually b) analyzing it in excel c) visualizing your analysis in Data Studio or your favorite BI tool 3.Read my guide to #python for SEO data analysis and start from the resources if you are new to Python 4. Piece together your solution by stitching together code snippets from my guide, stack overflow, and examples in the standard documentation. Also, feel free to ask in stack overflow or by commenting on Github issues related to your problem.
- Questions?