In the world of SEO (Search Engine Optimization), there are countless aspects that you need to consider. In addition to technical requirements, keywords still play a major role.
These come in different forms, e.g. long-term keywords or short-term keywords. One topic that is hotly debated in this context is the so-called LSI keywords.
But what does LSI actually mean? How did LSI keywords work and does the approach associated with them still make sense today? We’ll give you the answers!
LSI keywords: a simple definition
LSI stands for Latent Semantic Indexing. The term comes from mathematics and is actually a method for indexing static databases. The aim is to identify certain main components within the documents in a large amount of data.
Basically, it is about finding texts that have the highest possible relevance for the noun, even if this does not necessarily appear in it. So the system works via semantic relationships between terms.
What does such a mathematical principle have to do with SEO? A strategy for creating content was derived from the LSI, which involves researching keywords that are as related as possible, so-called LSI keywords, to the main keyword and placing them in the text.
Let’s just take a look at the example “shoe”:
If you follow the LSI approach, then my text about “shoes” should definitely include the terms sandals, hiking shoes, blisters, sneakers, and much more included.
From this comprehensive semantic coverage, Google expects the highest possible classification in terms of content relevance. So the thought is the more semantically linked terms, the better the text and the higher the ranking.
The problem with LSI keywords: a misunderstanding of SEO
The processing and permanent integration of LSI keywords suggest that you can simply incorporate certain terms and thus tap into good rankings. As if it were a method of specifically incorporating certain keywords into a text.
We are miles away from these times in SEO. The density of certain keywords and the use of stimulus words only play a role in a few places (metadata, headlines…).
Rather, Google considers content to be good if it matches the search intent of the target group. Google wants to offer its users the best possible results for the respective search query.
The search engine also uses semantic analysis to do this. However not like a checklist that rewards the naming of certain key terms. Rather, Google can create links in this way and classify content as relevant if the actual keyword is not even mentioned.
It often happens that thematically well-positioned pages become search terms ranks that are not used at all or only extremely rarely or in a modified form on the site. Because just because a certain term does not appear does not mean that the content has nothing to do with it.
Furthermore, there is no confirmation whatsoever that Google works according to an LSI principle at all. Accordingly, we have to say clearly that the differentiation from LSI keywords as a measure for content creation does not actually make sense.
That the mere integration of keywords is the wrong approach also shows the search intention alone. Because just because terms are semantically related does not mean that they also match the same search intention.
An example of this: The main keyword “walking ‘ is semantically related to the topic ‘sneakers’. Ultimately, the topics “buy hiking shoes” is also related to this.
While the first two terms may have a need for knowledge (information about preparations for hiking ) or linked to a specific place (plan of a specific hike), the last two are clearly actions, in this case, a purchase.
If I were to bundle all these LSI keywords on one page, I would create content that can’t work. Because part of the target group will expect a shop and the other part will probably expect a guide.
No matter how: Part of the target group will jump off the page disappointed and thus send Google the negative signal that the content does not fit. The rankings fall and the text loses out to the more relevant content of the competition.
What LSI Keywords Can Do for Your Content
Our conclusion of not considering LSI keywords as a detached category may sound like a harsh judgment. Nevertheless, the approach of researching related keywords is not pointless. In fact, it is absolutely necessary, but in a different way than working with LSI keywords suggests.
Every good SEO sticks to Creating a keyword analysis for related terms. However, this is not done with the intention of accommodating as many keywords as possible. Rather, it is about penetrating a topic and understanding which subtopics and needs of the target group are associated with it.
The aim of SEO content is to offer holistic content on a topic, which illuminates all relevant points and thus provides comprehensive information on the subject. This ensures that the target group always finds the right information.
Let’s go back to our “shoes” example for a better understanding. Imagine you are a manufacturer of sneakers and would like to provide detailed information about coughing in your guide.
Logical common sense dictates that you discuss different forms of shoes as well as various activities with shoes. You will also not be able to avoid putting on shoes in the context of walking.
That means: If you pursue the claim to provide comprehensive information, you will integrate the LSI keywords anyway. After all, they are related to the main keyword.
In this case, the integration does not take place because you absolutely want to use certain keywords. Rather, it naturally results from the fact that you want to provide the most comprehensive content possible.
So using related keywords isn’t bad or wrong. You won’t be able to help but use a lot of them. Creating content based on the goal of using certain keywords is a fundamentally wrong approach, which is based on the term LSI -Keywords stick and simply reproduce a wrong understanding of SEO.
How to Find LSI Keywords: Research Tools
The topic of LSI has become so entrenched in the SEO context that there are now even LSI keyword generator tools like LSI Graph. Whether you want to use these is up to you. In the end, they don’t do anything differently than any keyword tool: They provide a list of keywords and related keywords.
The exact assessment and handling of it is always the responsibility of the person working with it. So you don’t need a special LSI tool, but can continue to work with well-known keyword tools such as:
- Keyword Planner
- Sistrix Keyword Tool
- Google Suggest…
But always remember: When working with such keyword lists, it is not about compressing as many of them as possible into one text. Rather, you need to understand what the search intention is behind it and how you can make the best possible offer to the target group.
What you can learn from the term LSI keywords for SEO
The problem with the approach, attached to the term LSI keywords, illustrates very well what SEO actually means nowadays. It’s not about forming a quantitative set of keywords into a text.
Rather, it’s about understanding.
What the target group expresses for a need by using a keyword and how we can best meet this need with our content.
This is mainly possible if your content provides comprehensive information and is at the same time pleasant to consume.
Any principle that in any form applies a mathematical equation to determine the frequency of terms has its limits and must not be used indiscriminately as a basis for website content.
Of course, such principles can be helpful in identifying subtopics and adapting your own vocabulary to that of the target group. However, it should never become an end in itself to incorporate certain keywords with a certain frequency.
In this respect, the LSI keywords approach makes sense for SEO, because it provides insight into Subtopics and an understanding of what SEO should not be created.
Actually, it is not necessary to maintain this principle as an independent term. Because related keywords are part of the routine of every experienced SEO. Accordingly, it is not necessary to pay special attention to the topic of LSI keywords again.