How to Create a Sitemap Structure, using User Search Intent in Mind [ 🧵]

Just published a post on this topic with @OnCrawl 🚀 (ty, @RousseauxMagal1 for all your support 🙏)

Read a quick overview of it below ⬇️

oncrawl.com/technical-seo/…
Search intent has been the topic of academic research since 1993, where searching behaviors were defined by four things:

the goal of the search interaction;
the method of interaction;
the mode of retrieval; and
the type of resource interacted with during search.
Scientists analyzing queries in digital searches realized all four descriptors apply to digital searches, too.

The goals and type of resources shown are what really define search intent.
There are the top three levels of search intent:
➡️Informational
➡️Navigational
➡️ Transactional
However, they can be subdivided into categories, adding an additional two states – commercial and localized search intent.
Past research suggests that approximately 75% of queries can be classified into a single category of user intent (i.e. informational, navigational, or transactional) with a high degree of certainty.
Here are the three things to consider when planning a website’s structure for search intent:
1) Optimizing the site’s main pages, based on the type of search intent they serve
Pages on a website can be grouped into 3 groups, each of them corresponding with one main intent category.

💡 Resource Pages ➡️ Informational
🚀Company Pages (commonly referred to as branded) ➡️ Navigational
🎯 Product (&Service/ Solution Pages) ➡️ Transactional
Create a website that enables a high number of organic visits via high-quality resource-type content and moves users from informational to transactional search intent all without encouraging them to return back to perform additional searches.
2) Understanding the internal links needed to assist moving users from one search intent to another, without having to go back and perform an additional search (i.e. organising the site, based on the user journey)
Internal linking is important in the sense that it prevents visitors from ‘pogosticking’ or otherwise – going back to Google with their additional questions and it prevents Google from having to re-rank the millions of websites it has again, based on the new input.
In the post, I touch on three internal linking strategies:
💡Simple matching – KW to a target page
💡Topic relevance and content clustering
💡Search intent enrichment – linking pages on a similar topic that address different parts of the user journey
3) Being aware of the additional factors that can contribute to the site’s performance and the methods and tools you can use to identify pitfalls in the architecture
Here I touch upon:
💡 Importance of Tech SEO
💡 Site goal Definition
💡 Intent-based Content Optimisation
💡 Content Pruning
💡 UX
💡 Catering to User Personas
💡 Troubleshooting and Behavioural Analysis

Enjoy! 🚀

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More from @lazarinastoy

18 Nov
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.
Read 19 tweets
1 Sep
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.

-Google
Read 6 tweets
18 Aug
Supercharge Your Keyword Research Process By Incorporating Search Intent Classification

Use this SEARCH INTENT KEYWORD CLASSIFIER (Data Studio Dashboard)

datastudio.google.com/u/0/reporting/…
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
Read 6 tweets
24 May
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. Image
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.

=filter(‘Machine learning’!A:A,regexmatch(‘Machine learning’!A:A, “who|what|where|why|how|which”)) Image
Read 4 tweets

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