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Data and Evidence-driven SEO
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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- 1. Data and evidence-driven SEO – Why andHow to get started with Python and Machine Learning for SEO
- 2. AGENDA – A rocket shipis born 🚀 – Scaling success – From keywords to picture searches – Turning reviews trash into gold – How to get started –
- 3. 1. Hunt forevidence Leverage technology advances Follow Prof Richard Feyman @ProfFeyman
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- 6. SEL DATA BLENDINGARTICLE https://selnd.com/2EoPtdf – FROM DIGITAL OCEAN’S 2013 SEO AUDIT –
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- 15. Conversions by typeof keywords
- 16. Most searchers were lookingfor help not to buy hosting
- 17. Write content around keywordsthat convert
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- 21. MS LABS Redmond, WA APRIL17-28, 2000
- 28. From keywords toimage searches –
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- 36. Missing Alt TextAttributes – Computer Vision
- 40. Turning Review Trashinto Gold –
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- 45. Sentiment style transfer (negativeto positive or viceversa)
- 48. How to getstarted
- 51. How to usePython to Analyze SEO Data https://bit.ly/2UzUk0u
- 55. SUMMARY – A rocket shipis born 🚀 – Scaling success – From keywords to picture searches – Turning reviews trash into gold – How to get started –
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Editor's Notes
- #8 You can’t get there by just using excel.
- #10 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.
- #45 https://towardsdatascience.com/style-transfer-styling-images-with-convolutional-neural-networks-7d215b58f461
- #46 https://nlp.stanford.edu/pubs/li2018transfer.pdf https://github.com/lijuncen/Sentiment-and-Style-Transfer
- #52 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.
- #58 Questions?