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by Wayne Smith
A listicle structure presents information in a concise and topically structured format using headlines. Often they have or can have a table of contents and are numbered; There is a lot of flexibility in how they can be structured.
Merriam Webster simply defines a listicle as:
"an article consisting of a series of items presented as a list." It is commonly used in articles such as "The 10 best ... of 2023."
On-page content optimization for Content Creators:
- Each section or list element has text or content that explains the headline topic.
- Visitors can go straight to the topic of interest and read that section of the page, without needing to read the entire document.
- Content editors can edit individual sections, and typically list elements can be moved up or down on the list without interfering with the understanding.
- Unlike a book with chapters, listicles can be understood without the context of other chapters, and readers can read material in any order.
Listicle Topic Structure
The primary contextual design structure of a listicles for SEO purposes is a topical tree -- a topic is an entity for SEO.
The structure of a listicle can be looked at as a single page content silo.
Skyscraper Page to Semantic Topical Silo
An SEO Silo would be a group of pages related to a keyword with a hierarchical link structure to a central parent page. Silos provide easy navigation to all the sub-pages.
The Listicle Headlines are the related entities
The reason listicles do so well in search includes the gain of knowledge that is provided by the topical structure. When Google introduced BERT in 2019: Instead of using the predominance of a keyword, Google has been able to look for related entities on the page to determine relevancy.
- It is easy for the search engine to know what topics are explained
- What new information (gain of knowledge) or view point is being presented,
- It is presented in a helpful manner to visitors to the page.
Bert ties into the knowledge graph of AI -- making listicles evergreen content.
Entity-Based AI search engine optimization
Entity-based AI search or (Open AI, Google generative search, and voice search), systems use the entity (relationship model) data to find facts and documents.
Graphical web design - SEO design Considerations
The listicle topics should be treated as headlines larger than the text with the verbage on the listicle topic, and smaller than the page headline. All other elements can be designed as needed.
Page headline, topic headlines, and text should be visible when the page is loaded. Scroll effects still work as the effect begins at the point the element would become visible.
For supplemental content such as citations and quotes used within the subtopic (which can help people scanning through the content to find the information they want quickly):
- The text can be emphasized or the size competitive with the subtopics as long as it is styled as using an HTML element which is not a headline tag.
- The supplemental content aids in SEO when its content supports the topic.
- Supplemental content can be considered as needed or wanted; Needed content supports the topic, and wanted content such as advertising can be wrapped in an aside tag.
- The design using aside tags can be created using grids such that when the page is displayed on desktop aside goes into a side panel and on mobile it enters the content flow where it is placed.
Coding considerations used by web developers for listicles
As noted for SEO design Considerations the size relationships or predominance is focused on headline elements: page topic, listicle topic, and supporting text matter for SEO. Proper usage of headline tags are used for listicles, and the only headline tags used are for the listicle entities.
Headlines (H1, H2, H3, et al) for SEO
Entity-based AI search or (Open AI, Google generative search, and voice search), systems use the entity (relationship model) data to find facts and documents.
Content should not break keyword proximity rules.
Keyword Proximity, Evergreen Search Algorithm
If both keywords exist in the same inline element, (or a sibling inline element), they are near or close to each other. Proximity is almost a qualifier, not a factor; However, because relevancy is quantifiable and a factor ... proximity can be best called a co-factor for relevancy. However, there is also nuance in the implementation of the search engine side.
Tools such as Google's "Reading mode," can be used to insure all headline elements and entities are in the main block.
Minimum viable coding (HTML5 and CSS3) example:
Listicles and Table of Contents
For the table of contents to be listed in search, headlines must be used correctly. A graphic can be used but is not a requirement, a graphic is solely a site design style choice.
The headline tags were designed to allow a TOC to be automatically generated from a web page, but the headline tags are often misused as a style tag. When headline the tags are correctly used and linked: A TOC can become a snippet appearing in Google Search Results.
Content creator considerations ... topical (entity) relevance
Google has moved over to using entities in search, the evolution started before knowledge panels showed up on Google search results. Google's usage of entities can be clearly shown when Google applies natural language processing to determine search intent.
User Query Intent + SEO insights into entities
A query for "how to make pizza," could be interpreted fairly as asking for a recipe. With the Natural Language Processing algorithm using entities: Pizza is an entity under the entity of food and related to food is the word recipe, which is closely related to the concept (entity) of "how to make." A search for "how to make pizza," pulls up "pizza recipes," even when the term "how to make," does not exist on the page.
For Natural Language Processing algorithm purposes -- the listicle items are topics related to the page headline topic. The verbage in each list element includes words and entities that are related to the listed topic, and by extension relate to the headline of the page.
On topics where the author is very knowledgeable, they may be able to do a very good job in building a listicle. However, natural language processing is very good at spotting gaps. For competitive terms, research may be required to ensure there are no gaps. The topical research can require a lot of time. It is like old-school SEO for keyword research.
SEO: The evolution from Search Keywords to Search Entities
Nothing new under the sun. Old School SEO used keywords. Entities are like keywords but with a lot of information added. For document retrieval in scale, keywords can be indexed and each keyword index would have a record pointing to the resource or web page. The document retrieval system then looks for a record in the index that points to the same resource for all of the keywords. The natural order of the indexed data can be based on a page rank, (or using Search Entities Authority/Trust), which presorts the data. The smaller data set with all of the keywords can then be sorted for an exact, better match, and on expertise and experience.