- What is Topical Content?
- Syntactic vs Semantic — Keywords in Topical SEO
- Topical Content vs Evergreen Content
Topical content marketing is a subfield of content marketing in which specific topics are written about in order to gain topical authority. The goal of topical marketing is to produce enough relevant content to organically impact search engine rankings.
Modern search engines favor depth and breadth of content over thin content only published to target specific keywords. Topical content goes against such practices and focuses on educating readers with a lasting impact.
Topical content is a type of content that pertains to a specific topic. The goal of topical content is to provide comprehensive in-depth coverage over a particular thematic.
The terms topical marketing and topical content were recently coined in the SEO world as search engine algorithms smartened up. Indeed, older indexing methods focused on literal keywords while today, search engines approach web pages as topic-centric pieces of content.
Humans never judge a blog post's quality by how often it used a specific focus keyword. Instead, what makes topical content marketing a better method is that it mimics the human understanding of text. Having a topical approach to your brand's content marketing effort means writing in-depth information in a digestible format.
Longer blog posts (> 2,000 words) generally outperform shorter articles (< 1,000 words). This is not a magic threshold to pass, but if we know that content depth matters, then we understand that you need some substance to reach it. Therefore, the average word count tends to grow.
Content editors deem a particular piece of content as topical when it linguistically leads them to believe so. A topic is not just a word we use for fun. A topic is a concrete idea that we can express mathematically as the sum of its related phrases.
Yet, topic modeling is very complex because humans can write the same things in very different ways. We may use the same words with different intents. And algorithms have a hard time deconstructing textual content in order to extract its meaning.
For example, men and women, or coffee and tea, are often found together in text, but do they belong to a same topic cluster? I guess it depends on the subject at hand.
Writing topical content, or at least with a topical approach, is actually simple. Users perform a search query. Search engines know that the keywords from the user often contextually come up with a set of other words. Search engines then present results that show depth and breadth of content from topically authoritative sources.
In computational linguistics, a topic is the sum of its most frequent words and the words that are most exclusive to it. If you analyze a Wikipedia article (i.e. a topic), it can be boiled down to how often specific words appear in the page.
However, English is full of very common words so we need a way to counterbalance the frequency of these words. An algorithm called Term Frequency – Inverse Document Frequency (tf–idf) lowers the importance given to words that appear in a lot of different topics, or documents. This is called the exclusivity of a word.
An interesting research paper from Harvard's Department of Statistics, from 2012, proposes a model to infer estimates of the differential use of words across topics as well as their frequency within topics.
Words appearing often within the same context or topic are called semantically-related words, or topic clusters. A topic can be defined as the entire collection of its semantically related terms.
Google processed gazillions of web pages by now and understands what words are often found near other words. These topic clusters, when there are billions of them, allow Google to know what is expected from a blog post in order to credit it as valuable.
For example, if you write a long blog post about
coffee beans and never use the words
robusta, your content may be seen as incomplete or thin. Before, the goal would have been to use the keyword
coffee beans as much as possible, but things have changed. Today, Google and other modern search engines want to detect semantically-related words to judge comprehensiveness. To put it simply, you must use words and phrases often found in other contents about the same topic.
An information is worthless if it cannot be found intuitively.
Writing is obviously a key part of any topical content marketing strategy. Yet, structuring your overall content around a particular topic is as important.
Strategizing your internal linking strategy as well as each article's outline both matter very much. Readers and botnets trying to understand your website will all enjoy a nicely outline article over a 10,000-word blob of text. It's really a matter of breaking down what is a large topic into smaller consumable chunks. Each chunk may be a sub-section in a blog post, or an entire new article.
Keywords are still a big part of topical SEO today. Less as the main driver of content, but more as an actual expression of topics.
In fact, semantically-related words are generally semantically-related keywords. The terms can be swapped. However, we want people to move away from the term keyword as it has a bad connotation. One of a literal words that gives meaning to text only when frequently found within a blog post. This definition is outdated but still followed by most people.
Naturally, a blog post about
topical content will include the keyword
topical content many times. But the article is incomplete until we also cover several other related terms such as
Pillar Pages, etc.
Keywords are still useful but not in their literal sense anymore. Topical SEO advanced to a more sophisticated natural language understanding. More conceptual, less syntactic.
Both concepts of topical and evergreen contents should not be opposed as most of the time they can go hand in hand.
Evergreen content is a type of content that is not time-sensitive and should remain correct over the course of several years. Evergreen blog posts are long-lived and would educate readers two years later as much as at the time of writing.
For example, a news article would be the opposite of evergreen content. Indeed, trending news are very short-lived and their accuracy is questionable after just few days.
Topical content tends to be evergreen for established topics. Knowledge and facts about most domains and topics is not changing often unless the topic pertains to a new discovery or unstable concept.
From spectacular credibility to long-lasting organic results, topical content is a no-brainer for any results-driven content strategist. The upsides of topical content marketing are manifold for digital marketers willing to put effort into it.
The main goal of topical content marketing is to increase a brand's topical authority. In other words, have the brand demonstrate expertise over a topic to generate trust from prospects. When purchasing decision comes, prospects tend to buy from the brands they trust and we all trust who we learnt from.
Every entrepreneur should focus on their brand's topical authority and credibility. If somebody needs information about your core topics (not products, topics):
- would they benefit from browsing your blog?
- have you put enough content that is pedagogical and educational enough to get and keep their attention?
- will they forget about you after reading one article, or
- will they remember your brand first?
Topical authority is often mentioned when topical SEO is discussed. Yet, increasing a blog's topical content authority is even more important for how customers and prospects perceive your brand.
Content strategists focus on cover all aspects of a topic. They may need to write hundreds of articles to cover a broad topic, or a single 10x or skyscraper blog post to cover a narrower subject matter.
The scope of content depth can be for a specific article but it is generally applied to an entire blog. Most topics cannot be covered in a single digestible blog post. They require a lot of different blog posts, each covering a specific facet with as many details and factual information as possible.
In-depth topical content is not just about writing a lot of words, or paraphrasing the same information in twelve different ways. Content depth is also offering answers in different ways. Some search queries want a quick answer while other prefer a guide. Some people enjoy reading blog posts, others prefer infographics. You do not have to be on all fronts, but make sure there is a little bit of every style! Just do not spread yourself too thin.
Internal links matter in order to glue pages together and allow users to keep on learning in an organized and structured way. Pages discussing similar topics statistically link up more to each other so it all makes perfect sense.
Most blog posts are publishing dozens of articles about similar concepts. We, ourselves, publish weekly blog posts discussing content marketing and the topical SEO approach. Yet, each article covers a specific thematic as deeply as possible. We use internal linking to allow people to expand their learning experience, if they want to.
Pillar pages are also called 10x articles, skyscraper blog posts, or topic hubs. They are a visitor's entry point to your entire library of blog posts for a topic they are trying to learn about.
The main problem with cover a topic to the tiniest details is that you end up with a large amount of sparse blog posts. Some published few years back, while others were published very recently. A user entering your blog may quickly get lost in the wealth of information that you hold.
The recent trend, loved and recommended by search engines themselves, is to offer pillar content that is a long, structured summary of everything you published about a particular topic. Such pages often include nested headings and many links to existing articles.
Google frequently enhances search results using additional metadata around your current search query. These additional results generally appear on a side panel or in an answer box at the top.
These relevant short pieces of information range from answers to question, famous people bios, listings of events or places, maps, and a lot more. In order to source such data without a lot of accuracy, Google built its own Knowledge Graph.
In summary, a knowledge graph is a graph database made up of nodes (vertices) and relationships (edges). Nodes are topics, keywords, organizations, people, events, or other nouns. Relationships tend to be verbs and actions such as "is the capital of", "is the child of", "owns", "invested in", etc.
At Google scale, there are hundreds of billions of nodes and relationships in such graph. These come from external data sources (Wikipedia, governmental institutions, open datasets) but also Google's own scraping of information on the Internet (trusted and authoritative blogs, structured data, etc).