Introducing Meaning Engineering in Marketing

Search marketing has traditionally viewed products and services through the lens of keywords. However, people rarely purchase products for the product itself. They buy them because of a problem they are trying to solve, an outcome they hope to achieve, or a broader set of circumstances influencing their decisions.

A product is often just one part of a much larger story. It exists within a network of causes, motivations, situations, behaviours, and desired outcomes. To truly understand the value of a product or service, brands must understand the wider context in which it exists and the role it plays in a customer’s journey. Doing so requires a more deliberate and holistic perspective.

This is where Meaning Engineering comes in.

Meaning Engineering is the process of understanding and representing the network of causes, contexts, functions, situations, and desired outcomes that give meaning to a product or service from the customer’s perspective.

Unlike traditional SEO, which often begins with keywords, Meaning Engineering starts with concepts and the relationships that define them. Keywords become downstream expressions of those concepts rather than the starting point.

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Moving from Lower-Order to Higher-Order Intent Understanding

Traditional search intent classifications, such as transactional, informational, navigational, and commercial, are limited in their ability to explain modern search behaviour. These classifications tend to generalise the likely actions users will take based on SERP signals, but they often fail to capture the deeper human goals, motivations, and desired outcomes that trigger a search in the first place.

For example, a search term such as “beach vacation packing list” would typically be classified as informational by platforms like Semrush and Ahrefs. These SEO tools analyse SERP indicators and infer that the user is seeking information. As a result, many marketers in the travel and accessories sector would cluster this keyword and create a blog post containing a downloadable packing checklist.

While this is a reasonable starting point, the informational classification does not go deep enough to explain the underlying motivations behind the search, particularly for users in specific locations and at specific points in time. A deeper understanding of intent can unlock a broader range of business opportunities and strategic insights.

Clearscope, an AI-powered content optimisation platform, has also explored this challenge. Rather than relying solely on traditional intent labels, Clearscope attempts to describe what users hope to find. Using the same example, “beach vacation packing list”, Clearscope describes the intent as:

“Searchers are looking for comprehensive lists that include all necessary items for a beach vacation, covering clothing, accessories, and gear.”

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Exploring a Usage-Centric Approach to Intent Understanding

User intent is highly complex and cannot be neatly categorised using simplified labels like transactional, navigational, commercial, or informational. This may sound like a broken record, as I have often reiterated that user intent is nuanced, dynamic, and contextual.

While reviewing papers that cited Amazon’s Folkscope Intention Knowledge Graph work on Semantic Scholar, I came across an interesting study from scholars at the University of Edinburgh titled “A Usage-Centric Take on Intent Understanding in E-Commerce.” Digging into this paper, I found that it introduces a different, deeper paradigm for understanding intent.

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Why User Intent Modelling Should Explore Both Concrete and Abstract Intents

User intent is inherently complex and is best modelled or represented in a nuanced and contextual manner. As I have stated in previous discussions, we cannot simply categorise user intent into informational, navigational, commercial, and transactional and expect to capture the multilayered and dynamic nature of consumer behaviour.

In the Folkscope project by the Amazon Science team, intentions were generated from co-buy transactional data across categories such as electronics and clothing. The Intention Knowledge Graph project by the same team acknowledges the challenge of understanding intentions in online platforms. The researchers also noted that many existing works on intention knowledge graphs often lack sufficient focus on connecting intentions, an aspect believed to be vital for modelling user behaviour and predicting future user actions.
Abstract intentions play a useful role as the conceptual umbrella under which concrete intentions are connected.

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Why Folk Psychology Provides Deeper Insights Into Search Intent

The search or user intent is a core aspect of most search marketing strategies and tactics. Leading platforms like Semrush and Ahrefs assign an intention class to keywords populated on the system. 

The above is a simple illustration in the work titled “The Category of the Mind: Folk Psychology of Belief, Desire, and Intention by  Yoshihisa Kashima

In the work he clearly stated that beliefs and desires do not primarily cause action but intention is believed to act as the causal link between belief-desire and action. In addition, within folk psychology humans are considered to utilise beliefs, desires and intentions to understand, predict and explain human action.

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How Amazon’s Commonsense AI Research on FolkScope and COSMO Validated My Thinking

As a search marketer, I came across the concept of commonsense reasoning in 2016 while reading articles on narrative intelligence. The curiosity to learn more about the topic and its key concepts led me to a thesis from Niket Tandon in 2019. His thesis is titled ‘Commonsense knowledge acquisition and applications.” 

This initial exposure to commonsense reasoning and an increasing passion for graph theory led to my first presentation at BrightonSEO on the topic “From knowledge graphs to commonsense knowledge graphs.” 

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How using a commonsense knowledge graph can enhance an understanding of the search journey

Human behaviour is complex and never linear. Users employ a variety of queries at each stage of their journey. Marketers have tried creating frameworks or adopting conventional ones to explain the user journey. Sometimes we are guilty of oversimplifying the journey for our own convenience and satisfaction.

A blog written on Moz got into search journey using the popular AIDA (Awareness, Interest, Desire, Action and Loyalty) model. And the author used the cough medication example to illustrate how users would always start their journey with a query like “how to stop coughing” and proceed to “cough medicine” “best  cough medicine for dry cough” and so on. 

This journey or framework makes logical sense. Marketers and non-marketers can simply understand how search by a user can progress through the different stages. But in reality, human search behaviour is never linear as our life experience, location and external triggers are different. Some users may start with a query at the 3 stages of this journey and flipflop between a few to before making a decision. 

Search Journey mapping using a before and after structure 

Interestingly, whilst looking at the different ways search journeys are mapped, I ran into a blog from SEO Clarity that expressed a before and after keywords to a target keyword. For example, the target keyword “bronze glass” is predicted to have such keywords like “bronze glass mirror, bronze glass vase, bronze glass table lamp” as the preceding search term and keywords such as “bronze mirror, bronze tinted glass, bronze glass panels” as some of the next terms users are more likely to search for. Whilst this paints a logical picture of how users are likely to search, it is very lexically structured that it might fail to some degree to mimic human reasoning. But it does make a case that humans are more likely to progress or evolve with how they search for information. 

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Rethinking keyword clustering through a semantic and cognitive lens 

Keyword clustering remains an important task for search marketers and is key to driving organic traffic, improving rankings and enhancing the authority of the website on a given topic.

There are a handful of practical and great tools that exist in clustering keywords such as Keywordsinsights.io, SurferSEO, Inlinks and the incumbent greats such as Semrush and Ahref. For clustering people also asked questions the likes of AlsoAsked and AnswerThePublic will come in handy. The former clusters by PAA questions from SERP while the latter uses Google Autosuggests and has also added PAA to its collection.

These and a few more that haven’t been mentioned are conventional tools used for keyword clustering and have yielded ranking benefits and an inspiring source of content generation opportunities for most search marketers. But, I have a problem with the approaches and logic utilised by these tools. They almost pigeonhole human search behaviour into some lexical and linear route which ignores how searchers think, reason and act. They are more focused on clustering based on word cooccurrence and cosine similarity of the lexical nature of words but can often lack the semantic and sequential depth akin to human cognition and behaviour. 

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Introducing the concept of semantic authority in search marketing

As an industry, the importance of providing in-depth coverage on a topic is essential for brand relevance and ranking. With Google’s helpful content updates, there have been cases of companies experiencing reduced rankings and traffic because they employed a spray-and-pray approach. That is employing a content marketing approach that involves creating articles for high search volume topics for the sake of generating website visits. A traffic-first approach that does not cater to the user’s needs and fails to build a solid depth or authority in a particular niche topic. With E-E-A-T (Experience, Expertise, Authoritativeness and Trustworthiness), there is no room for a blanket content marketing approach. Companies have to build an authority in a given topic that is closely related to their brand, product or service. But the journey does not need to stop there, more concepts need to be developed to help us better understand the intent of searchers and analyse how the available content is catering to these needs. For this, I am proposing the concept of ‘Semantic Authority’, an evolution of topical authority. This concept will be introduced below. 

What is Semantic Authority 

Semantic authority is the degree to which a website demonstrates a contextual, cognitive, and causal understanding of a domain by aligning with user intent, interpreting meaning accurately, and structuring information relationally and dynamically. To put this simply, semantic authority involves a more contextual and cognitive understanding of a topic or concept and developing the required content to cater to the user’s needs. Websites that have fully broken down the semantic relations of a concept or seed term are more likely to create contents that go a step above topical coverage. These brands and websites can be viewed to attain an authority in the meaning or semantic space. 

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Moving from topic clusters to semantic clusters

As an industry, topic cluster is being heralded as one of the greatest templates to setting up a website to rank favourably for target keywords by interlinking in a well knotted format. Hubspot has put together a great resource on topic clusters and provided an experiment carried out by their former team members that indicates that effectively interlinked pages had better placements on Google’s SERP. A quick note to add is that, internal linking is always a great SEO optimisation strategy and irrespective of topic clusters or not.

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