There is no keyword!

Let’s jump back in time: announcing the introduction of the Knowledge Graph in 2012, Google focused not only on the importance of this tool, but also on the meaning of the new approach to research, based no longer on “strings”, but on things.

The knowledge graph is based on the entities that Google knows – things, people, landmarks, celebrities, cities, sports teams, buildings, geographical features, films, celestial bodies, works of art and more – and allows you to instantly obtain information relevant to the request made by users. But, most of all, it was already defined as “a first fundamental step to build the next generation of research, which draws on the collective intelligence of the web and understands the world a little more as people do”

The direction taken (more than eight years ago!) with the Knowledge Graph indicates that even the Google Search system does not work by keywords and simple words, but on elements connected by relationships and entered in a context.

Entities in fact describe content using knowledge models known as graphs, which help machines to interpret the thought of man and at the same time allow us to find information more efficiently.

The confirmation comes a few years later, in 2015, when Google launches a specific algorithm update for Search, the famous RankBrain, based on machine learning to improve the way the system indexes and processes information to provide the user with useful content, managing to order billions of known pages and locate the most relevant ones for each search query.

We are now coming closer to the present day, with an increasingly advanced and sophisticated level of programs to recognize natural language – last but not least, Google BERT – and combine user requests and Web content. In the words of Nina Taniguchi, Consumer Insights Manager, Ads Marketing for Google, we must become able to “predict the needs of the customers” starting from “equal search terms that however generate different emotions”.

Her considerations reminds us that “marketing professionals invest time, money and resources in trying to decipher and predict the intentions of consumers”, an objective that does not differ from the SEO’s one. The key, to Google, is “to be able to understand that need that is the basis of these intentions”, extending this analysis to Research, as well, because “consumers type in the true sense of the word the terms corresponding to their needs within a search field”.

Basically, this means avoiding “universal” content, going beyond “the search terms related to the brand and the terms commonly associated with your category”, understanding “the underlying emotions of consumer actions” and in this way “to predict and respond to the needs that drive the research”.

As we know, there classically are three main types of need: informational searches, typical of the user who is documenting on a topic; transactional searches, characteristical of who is willing to carry out an action, like a purchase; and navigational searches, which represent the needs of those who are looking for something in a specific context.

Each of these research efforts is combined not only with different experiences, but also with different keywords: it is likely that those who want to make a purchase want to also know the price and technical characteristics of a particular product, which probably doesn’t really matter to those who are looking for information about its use. And, vice versa, those who are interested in knowing how to use an object at that specific time do not want to read its price.

Search engines are refining their ability to interpret user queries and respond by presenting the most appropriate search results, namely pages that have the content giving the right information to the right user. For some years now, Google, but also Bing, have been deploying increasingly advanced systems in the field of deep learning and natural language processing, such as the BERT language model launched by Google in late 2019 and now running for almost all queries in English, to help users achieve higher quality results than their questions.

And so, in a nutshell, before creating any content it is important to know and understand what the real intent behind the specific query that affects our business is, or we risk posting online pages that will not get the results we are hoping for and writing articles that do not fit.

Understanding what our target audience and potential customers want and what they need means being able to plan our strategy in advance, choosing more targeted keywords and writing more effective contents, given the fact that they are more useful.

Utility is a central word for our work, because it is related to the evaluation of the quality of our content: each of our pages must have a purpose, an added value for the user, and help him solve a problem, learn a skill, save time or money, increase knowledge on a topic, deepen a topic, get resources.

Thanks to artificial intelligence and predictive algorithms, the concept of “useful” has changed over time and today it does not mean only relevant, but relevant according to context and relationships.

How to automate and speed up the search intent research with SEOZoom

There are various techniques to be able to understand what the audience of a given query wants: the classic method is to directly look at the Google SERPs and check what kind of results are offered, or if there is a prevalence of transactional or informational content or if there rather is a “mixed” presence. It would then be necessary to analyze the individual pages more carefully to find out what kind of information they report, how the topics are covered and so on: in short, quite the brutal work!

Inside SEOZoom, the Italian SEO suite now debuting on international markets, there are some features specifically designed to simplify the detection of search intent and the creation of SEO-optimized content.

Since its start back in 2015, SEOZoom has been the first SEO tool in the world to offer a vision of the performance of a website no longer based on the placement of keywords such as trivial rank trackers do, but on the performance of every single web page of the site. And this is visible in the Content Overview, a tool that catalogues all the web pages of the site and groups them according to their performance on search engines, indicating useless or duplicate pages wasting Crawl Budget, cannibalization cases and so much more.

Thanks to its Search Intent Tool and URL Search Intent Tool we can find out precisely the search intentions of users behind every single keyword, useful both to create a new page already oriented to what you need in order to rank on Google, and to improve the performance of an already published content.

SEOZoom algorithms help us choose the key topic to develop (the main keyword), while its related topics indicate which correlated keywords we can use to enrich the content without ending “out of focus”, when we can merge keywords and themes into a single article and when users (and Google) expect individual and different articles.

And so, our keyword research has to follow a different modus operandi, as we do not just have to focus on searching for high-volume keywords, for a string, but to rather detect the best context.

To achieve this, we need to analyze the search process of users, from the birth of the need to the choice of the solution, and then deepen what Google has considered relevant and then shown in SERP, meaning to find out which are the answers that the search engine already considers the most useful ones, to understand what is the focus to stick to so to make our content effective and competitive.

Once we have all this information – and with SEOZoom it only takes a few minutes – we must then develop the content of the article keeping the focus central, remaining in the trace of what the search engine currently appreciates. This is the only way we can achieve a quality content, that tries to meet the specific needs of users and to do so in the way Google finds the most pleasing.

These concepts can also be applied to future scenarios, because it is conceivable that contexts will increase in the coming years, but not the basis of this activity. That is, the development of technological systems such as home automation objects, smartphone radars, voice assistants and voice search, will multiply the factors that can influence the choices of users and the channels with which people can come in contact with Google and a site, but not the “need” that drives them to seek.

That is, search intent will still be crucial and even keywords will still be important, always thinking in terms of clusters around what at that given time means “useful” to people and Google alike. In short, the real challenge that awaits us is to adapt the old keywords to new technological and above all environmental contexts, trying to evolve at the same time our tactics and strategies as well.