I'm interested in alternative approaches, but as or more insightful, to causal impact testing in #seo - whilst ci testing is great when you are a larger business with bigger data capabilities, it's harder to get insights as a freelancer with smaller client base. Any advice?
Another thing I forgot to say is an approach that can also account for multiple implementations over several weeks - perhaps this makes it almost impossible?
Alternatively, I'd be interested how I can use CI testing with smaller ecom websites, when making template wide changes and therefore minimal options for control it seems. Do people use a smaller cluster for comparison? Better market matching?
Anyone know if it's possible to add more keywords to a project in @keywordinsights if you need to add more after already running a clustering project in there?
Currently learning data science and keen to use real life data in SEO. Interested to know if anyone can point me in the direction of practical examples using probability or k-means clustering in SEO or digital marketing.
Fun causal impact fact - I worked with a client with similar - lots of links BTF. Same but different.
We tested the impact of reducing the number to 12, 24 & 32 vs original (~70 or so), and most positive uplift was 32 which saw ~8-10% uplift for the linked to pages.
You may have seen how I created a python script using #chatGPT for my #SEO work. I've put together a follow-up that turned that script into a #Streamlit application by making small adjustments to that original script:
https://t.co/VnG4cQUeLE
I've completed the second part of this blog by showing you how I took that python script built with chatGPT and put it into a Streamlit app.
Would love to know what you think!
https://t.co/VnG4cQUMBc
Like most, I recently dived head first into the #chatGPT discussions and started using it to create simple python scripts to help my day-to-day in #seo.
I've put together a start-to-finish explanation of how I did it with little Python experience.
https://t.co/p4LyfuBmTo
The second part of the 2-part series is now done - I explain how I took the python script I created and turned it into a Streamlit app. Have a read:
https://t.co/VnG4cQUMBc
Like most, I recently dived head first into the #chatGPT discussions and started using it to create simple python scripts to help my day-to-day in #seo.
I've put together a start-to-finish explanation of how I did it with little Python experience.
https://t.co/p4LyfuBmTo
A great article on how you can transition from Excel-first to python-first.
There's a lot to learn, but if you get used to the python functions that are direct alternatives to Excel, you'll be winning
https://t.co/4Vx1qJL2W5
Just input keywords & search volume csv, enter brand names/products to exclude in spellcheck, set your similarity score and out pops a table that lists any close match KWs you can potentially remove, misspellings or special characters:
https://t.co/l4scFgWUBl
Have delved into making smaller scripts (with a lot of help from chatGPT) that save me just that little bit of time here and there.
One that I created recently for #SEO is a keyword research QA tool to help me find errors/close match KWs 1/