🔥Tutorials on how to do everything, for those who need them will be published on YouTube very, very soon. Subscribe to know when. youtube.com/channel/UCkhA-… 🔥
Access the deck to get the links to all of these sheet templates, and make a copy to start using the @dataforseo, @OpenAI GPT-3, and @googlecloud NLP API code straight away for entity analysis, sentiment analysis & more
Remember to modify the scripts to include your own API key
There are so many use cases for these scripts, it would be crazy not to use them.
Here are just a few... 🤫
@brightonseo as per usual is an absolutely incredible experience, thank you to everyone, who made it special, and massive, massive thanks to the #BrightonSEO for being the most amazing human beings ever.
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Below, I list the main sections of the dashboard + link a tutorial 🧵
In the Coverage Overview Section, you can get an overview of:
how many pages are crawled, as well as the distribution of pages per section
how many pages have coverage status as per the Inspection API, including the distribution of pages per status
+ more (!)
In the Mobile Usability Section, you can:
Get an Overview of whether your pages are mobile-friendly or not (distribution)
Get an overview of the main types of errors encountered on the site, and how many pages trigger them
Filter URLs based on Error type, Indexability,and Status
I just completed my talk on Intent-based Keyword Research - the methods, the complexities for international keyword research, and how to adapt per industry at #IntSS
Here is a 🧵 below of the main insights I shared, a blog post, and my slides ⬇️
Search intent is everywhere these days.
@semrush released a Search intent categorization feature, which provides each keyword with a label, letting you know what supposed intent the user typing this has.
But how about if you don't have access to their tool?
Or what would you do if you are building a bigger data set with multiple data sources, then you’d need a rule-based system for classifying intent or build a custom classification model for intent.
Automatically Generate Your Meta Descriptions Using Python And BERT
If you want a quick and dirty way to programmatically meta descriptions at scale using Python, this is the tutorial for you.
Meta descriptions are used as part of the site’s metadata, as well as shown in SERPs to provide search engine users with a brief summary of a page.
They impact search rankings indirectly. By being visible in the SERPs, meta descriptions can impact the click-through ratio (CTR).
If your site has thousands or even millions of pages, hand-crafting description meta tags probably isn't feasible. In this case, you could automatically generate description meta tags based on each page's content.
Recently, during a webinar I heard an absolutely magnificent piece of insight, which inspired me to create the resource I am sharing today:
‘There is no such thing as keyword cannibalization, only search intent cannibalization’
Bernard Huang, Co-Founder of Clearscope
I am presenting a keyword classifier Data Studio Dashboard, which utilizes the search intent categories we know and use:
* informational
* navigational
* transactional
* commercial
How to filter a column of broad match keywords, using a REGEX match filter formula to return question-type long-tail keywords. 🧵 #seo
Question-type keywords are long-tail keywords that contain the seed keyword in any order, plus a “question word” like “how,” “what,” “where,” “when,” or “why.”
Export a list of keyword recommendations from @semrush Magic Keyword Tool.
In order to fill the Questions column, we use a regex match formula in the filter criteria, referencing the list of keywords exported via the Keyword Magic Tool from SEMrush.