What is user intent, and why does it matter?
User intent refers to the underlying goal or purpose behind a user's online actions. It's the "why" behind the click, what someone is actually trying to accomplish when interacting with digital content.
Understanding user intent goes beyond contextual keyword matching. It's about comprehending their motivations, expectations, and the specific outcome they want when interacting online.
Consider these scenarios:
A runner browsing articles about how to minimize injuries may be in the market for high-end running shoes.
Someone typing "how to fix a leaking faucet" is looking for step-by-step instructions.
A query for "Italian restaurants near me" indicates someone ready to make an immediate dining decision.
In each case, the intent shapes the entire experience they expect to find. When marketing efforts align with these specific intents, conversion rates naturally improve because you're providing exactly what users are looking for at precisely the right moment.
The four types of user intent you need to know
Most user intent falls into four main categories that marketers should understand:
Informational intent
Users with informational intent want to learn something. They're looking for answers, tutorials, guides, or other educational content. These queries often begin with words like "how," "why," or "what." Examples: "what’s the weather in my hometown," "how to make sourdough bread," "history of jazz music."
Navigational intent
With navigational intent, users are trying to reach a specific website or page. They already know where they want to go and are using search as a shortcut to get there. Examples: "Instagram," "Amazon customer service," or "Gmail."
Commercial intent
Users with commercial intent are researching products or services but aren't quite ready to buy. They're comparing options, reading reviews, and gathering information to make a purchasing decision soon. Examples: "best washing machines 2025," "iPhone vs. Android comparison," "affordable CRM software reviews."
Transactional intent
Transactional intent signals that a user is ready to complete an action, typically making a purchase, signing up for a service, or completing a form. These users have moved beyond research and are ready to convert. Examples: "buy Canon EOS R5 camera," "subscribe to Netflix," "book hotel in Miami."
Understanding these intent categories is crucial because they represent different stages in the customer journey, and each requires a different approach to content, messaging, and calls-to-action.
Read also: Large Language Models Meet Digital Advertising: Inside IntentGPT's Approach
How IntentGPT targets commercial and transactional intent
While these four intent categories provide a useful framework, IntentGPT identifies commercial and transactional intent signals within the content people are actually browsing. Unlike contextual methods that rely on broad keyword matching, IntentGPT analyzes the content users are actively consuming to determine their readiness to purchase, regardless of how they came to the URL.
By examining contextual signals that indicate buying consideration or immediate purchase intent, IntentGPT helps brands target the highest-value opportunities. This focus on commercial and transactional intent is particularly valuable for performance marketers looking to maximize engagement.
The challenge: Why user intent is hard to get right
Before we started building IntentGPT, our team spent months analyzing the limitations of existing approaches to understanding user intent. What we found were significant gaps:
Keyword analysis is too simplistic.
The same keywords can signal completely different types of intent depending on context. A search for "coffee machine" could indicate research, comparison shopping, troubleshooting, or immediate purchase intent. Without understanding the surrounding context, keywords alone provide limited insight into what a user actually wants to accomplish.
Traditional analytics tell you what users did, but rarely why they did it.
We can see that someone spent three minutes on a page, but not know what they were hoping to accomplish there. This action-focused, rather than motivation-focused, approach creates blind spots in understanding the customer journey.
Demographics don't determine intent.
Two people with identical demographic profiles can have completely different goals when visiting the same website. Assuming intent based on who someone is rather than what they're trying to do leads to misaligned messaging. This demographic-based approach often results in marketing that feels irrelevant to users whose actual goals don't match their predicted interests.
Manual content analysis doesn't scale.
Some marketing teams attempt to manually categorize content by intent, but this approach quickly becomes unmanageable across thousands of webpages and constantly changing user behaviors. The sheer volume of digital content makes manual analysis impractical for all but the smallest campaigns.
What became clear was that performance marketers needed a more sophisticated approach to understanding the why behind digital interactions.
How we approached user intent identification with IntentGPT
When building IntentGPT, we wanted to solve the core problem with traditional intent identification methods. Most existing approaches rely on basic keywords or broad categorization, but they miss the nuance of human behavior.
Our team developed contextual AI that analyzes digital content at the URL level. The technology examines the entire webpage—the content itself, how it's structured, and patterns of user engagement—to identify signals that reveal genuine user intent.
We identified five areas where better intent understanding could make the most difference. First, contextual understanding. Large language models help identify what matters on a webpage by analyzing both explicit statements and implicit meaning, separating signal from noise in complex content environments.
Second, intent interpretation. Seeing beyond surface interactions to understand the "why" behind user behavior is vital to recognizing patterns in browsing behavior that signal specific intent and basing marketing decisions on actual user motivation rather than assumptions.
We also focused on workflow improvement, reducing the manual effort required to understand content contexts, automatically identifying key elements that influence user intent, and allowing performance teams to focus on strategy rather than content analysis.
Scalability was another critical concern. Making intent understanding practical at scale, connecting data collection directly to actionable decision points, and providing clear prioritization for content engagement opportunities.
IntentGPT is a step change compared to existing solutions, both the ones RTB House employs and those employed across the internet. If you think about existing solutions, certain best-in-class approaches to understanding whether users will really engage on a publisher page are based on matching sets of words or keyword analysis. Or perhaps understanding basic sentiment analysis by looking at whether those keywords reflect certain emotional states, such as happiness or nervousness. However, that's a very outdated approach. [IntentGPT] reads and understands a page in its entirety, deriving deep meaning from the content, the same way a human would. We can implement this technology at scale and do it in 49 languages across the internet in near real time to ensure that we can drive as much scale and as much performance as possible.
Real-world applications we've seen with intent-based marketing
Our extensive testing has shown that when we integrate IntentGPT in campaigns using our Deep Learning-powered retargeting algorithms, they see engagement rates increase by an average of 44%. This performance improvement comes from IntentGPT's ability to understand what users are actually reading and consuming, rather than just matching keywords.
By focusing on the actual content users engage with rather than proxies like search terms, IntentGPT identifies genuine interest and purchase intent signals that keyword-based approaches simply miss. This more accurate targeting means ads appear in contexts that truly match user readiness to engage or purchase, rather than making assumptions based on demographic profiles.
Final Thoughts
As we’ve seen firsthand while developing IntentGPT, the shift toward intent-based marketing represents a fundamental evolution in how we connect with audiences online. The marketers who are seeing the strongest results are those who recognize that understanding what users actually want is far more valuable than simply knowing who they are demographically.
By focusing on user intent, performance marketers can create more relevant, helpful experiences that naturally drive better business outcomes.
If you're working on performance marketing campaigns and want to explore how contextual AI could help you better understand user intent, we’d be happy to share more about our approach with IntentGPT. Visit the IntentGPT page to learn more about how we're helping marketing teams gain a sharper focus on what matters to their audiences.
