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.
Well, you have a few options.
👀
Option four - highly not recommended. But why?
Here are three reasons: 1. Search intent corresponds with the conversion marketing funnel, so knowing the intent split in the industry you are targeting can help you assess and forecast content performance
2. Knowing the query is the first task in content optimisation as it can allow you to better position your content via optimisations in relation to your goals
3. Understanding intent will enable you to write pages that fulfill it and move the user from one intent category to another via internal linking
According to research, 75% of all web-mediated search queries satisfy single search intent, so working with a rule-based approach will likely be sufficient for a lot of the research you are doing.
What should you consider when doing intent-based keyword research for international markets? 1. The complexity of language and how this affects Search Intent
- Same words in different languages mean different things
- Idioms exist
- Homonyms and homographs exist
Simply translating the keywords is not enough.
2. The complexity of user and query locations and how this Intent
- Locations affects user expectations for the same query
- Cultural differences affect search
- Explicit location is not the same as the locale
- A single keyword can have multiple different intents
5 Tips on How to Incorporate Search Intent into the Process of Keyword Research Internationalisation
1. Use Regex strategically.
2. Build a behaviour dataset.
Use Google Analytics to analyse user behaviour and site interactions from this location.
Use Google Search Console data to identify mismatches in intent
Use Google Trends data to identify discrepancies in search behaviour in different regions
3. Consult an SEO localisation expert
They can help you highlight things you might have missed in your research
What should you consider if you are adapting Search Intent for different industries?
Here is an example from transactional intent
5 Considerations for Industry Adaptation of Search Intent Labels for keyword research
Adapt, revise, and be thorough, if you want to nail intent on a budget at scale. 🚀
Here is a link to the blog post, that goes through the process:
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.