A handful of lessons I learned (and I am still learning) while trying to apply #DataScience to #SEO. Some of them are not so obvious either.
This is a thread 🧵
This is a thread 🧵
1. Communication is hard and you will get mad a lot of times. Non-technical people have no clue what you are talking about and you have to educate them.
Easier said than done, but I think that you should stay strong and keep trying.
Easier said than done, but I think that you should stay strong and keep trying.
2. Data quality is all. In SEO it's way harder as you are working with estimates and you don't even know the original data distribution.
That is why I am very careful when using Machine Learning models for SEO.
That is why I am very careful when using Machine Learning models for SEO.
3. SEO is challenging, now imagine explaining bits of #DataScience and SEO together to non-technical people.
Mastering the art of communication is a must, while keeping ethics into account.
Mastering the art of communication is a must, while keeping ethics into account.
4. Visualization is your way to go for most things. People don't understand the impact of your work?
Plot it and show them your progress visually.
Plot it and show them your progress visually.
5. You don't need Deep Learning to solve simple tasks. Actually, you don't even need it for most SEO tasks either, barring few exceptions.
Basic data analysis and plotting are way more useful than you think.
Basic data analysis and plotting are way more useful than you think.
6. Mentally prepare yourself for hearing some like "OK, now can you do it in Excel?" when you are working in Python/R or whatever programming language.
7. You have to be very careful when you define KPIs and metrics. A blog will have a different business model compared to an e-commerce, implying different success metrics as well.
8. Google APIs are your best friend. Imho GSC API alone is a goldmine of information and if you have a medium-sized website you already have enough material to create crazy stuff.
9. It takes much more time to combine 2 disciplines together. Consistency is key and the results will be much higher if you think about the long term.
10. The king of data is SQL. This one is mandatory and should be improved over time.
I am trying to learn BigQuery as of now and guess what? It's a Google product
I am trying to learn BigQuery as of now and guess what? It's a Google product
11. Most times you'll be frustrated because you can't use the cool and shiny scripts that you created.
It's normal, you have to think about value first, to avoid being disappointed.
It's normal, you have to think about value first, to avoid being disappointed.
12. Most data sources for SEO coming from tools can be considered "clean" compared to what you see in other industries.
Either way, data cleaning > all the rest
Either way, data cleaning > all the rest
13. #NLP is a great friend for #SEO. You have so many options to find cool insights with small scripts.
14. There is a lot of hype around data, it will eventually fade away.
That is why I've put point 5 in, because you really need to master the basics first!
That is why I've put point 5 in, because you really need to master the basics first!
15. If you want to go all in consider studying Statistics or Data Science. Be ware, your focus should be on creating value first, your client/employer/whoever pays you doesn't care at all!
My other post containing some personal considerations about SEO:
https://twitter.com/GiordMarco96/status/1486348945123123203
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