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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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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Why the search intent is more complex to classify

Analysing and modeling the user search intent goes beyond the simplistic categorisation of informational, navigationals, transactional and commercial. Users are quite complex and the purpose of their search can’t be easily inserted into the four boxes of categorisation that is common in most search platforms, articles and commentaries.

The search intent is usually conceived as the purpose behind a search. Keyword search data on third party software tools, Google Search Console and Google Keyword planner are a goldmine for data-centric marketers. The user intent should focus on the purpose, motivations and reasoning behind a search. There are user stories, prompts and triggers around the public and private keyword data. Reducing use intent to general actions that suit us as marketers deprives us from gaining deeper insights as to why users are searching in the first instance.

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