What Marketers Still Get Wrong About Intent Signals (And How To Fix It)

Most of modern marketing rests on the belief that people express what they want through their behavior. Teams assume that actions taken across platforms reveal not only what someone cares about, but where they stand in a decision-making process. This assumption has been repeated so often that it has become doctrine. Yet a closer look at how audiences behave shows that the interpretation of intent signals is frequently misguided. People signal interest, hesitation, comparison, distraction, and curiosity in ways that are far more nuanced than dashboards suggest. The industry’s language around intent often implies a level of precision that does not exist, and as a result teams make confident decisions based on signals that were never meant to serve as definitive evidence.

Part of the issue lies in the way organizations have come to rely on neat linear models to explain behavior. Funnels, stages, and lifecycle charts simplify what is actually a fluid, non-linear process. People rarely move from one stage to another in a predictable sequence. Their behavior reflects personal circumstances, external conditions, internal debate, information gaps, and the unavoidable influence of timing. Understanding intent correctly requires accepting that human behavior is more layered and inconsistent than most marketing frameworks allow.

Before exploring the misunderstandings that shape the industry’s interpretation of intent, it is necessary to define what intent signals actually are. Many teams engage with the term regularly but operate with unclear or incomplete definitions. Establishing clarity provides the foundation for interpreting signals with more accuracy and nuance.

Understanding What Intent Signals Actually Represent

Intent signals are behavioral indicators that reflect some degree of interest, exploration, or motivation related to a product, service, or category. These signals range from subtle to explicit. Some appear early, long before a person has begun evaluating vendors, while others indicate active comparison or late-stage readiness. Because these signals reflect different motivations, treating them as interchangeable creates significant misinterpretation.

There are two primary categories of intent signals.

The first category is first-party intent, which consists of signals observed directly within a brand’s environment. These include repeated visits to key pages, increased time spent reviewing features, returning to pricing information, downloading materials, initiating trials, or interacting with product-specific content. These behaviors are often more reliable because they occur within a context controlled and understood by the brand.

The second category is third-party intent, which reflects activity observed outside the brand’s ecosystem. Platforms such as 6sense, Bombora, Demandbase, G2 Buyer Intent, ZoomInfo Intent, Apollo, Clearbit, Slintel, and similar providers aggregate research patterns, topic consumption, category-level interest, and company-wide engagement across external properties. These signals reveal broader themes within organizations and can help identify emerging interest long before direct engagement occurs. However, they often reflect exploration or planning rather than immediate buying intent.

Both categories offer value, but neither provides complete clarity on its own. Intent signals must be interpreted with an understanding of context, motivations, and the limitations of what behavioral indicators can reveal. Without that context, teams often draw conclusions that are more confident than they should be.

With this foundation established, it becomes easier to explore the most common misunderstandings that shape how marketers interpret intent signals today.

Why Marketers Mistake Activity for Intent

Many marketing teams treat activity as a sign of intent because it feels tangible. Traffic increases, clicks rise, engagement appears healthy, and the assumption is that demand must be growing. These indicators create a sense of progress that is easy to celebrate, especially when organizations rely heavily on surface-level numbers to demonstrate performance.

The challenge is that activity often reflects curiosity or convenience rather than intent. A person can visit a website without having any desire to pursue a purchase. Someone may click an ad because the creative caught their eye in a fleeting moment of interest, not because they are evaluating the product. A spike in traffic may come from audiences who have no commercial interest in the category at all. Activity alone cannot distinguish between casual browsing and meaningful exploration.

Activity becomes informative only when viewed in the context of sequence and depth. A single visit to a homepage provides little insight. Several visits to specific product pages, combined with a return to pricing after an interval, reflects a more deliberate pattern. Marketers who rely on activity without analyzing context often misinterpret surface-level actions as genuine intent and then struggle to understand why conversion rates do not reflect the volume of engagement.

Interpreting intent correctly requires treating activity as the opening of a behavioral narrative rather than evidence of interest. When brands shift from counting actions to understanding patterns, they develop a more realistic view of their audience’s motivations.

Why Interest Cannot Be Interpreted Without Considering Friction

Engagement numbers are comforting because they suggest that people are paying attention. Likes, comments, views, and page visits offer visible signs that the brand is resonating. However, interest does not automatically translate into intent, especially when the path forward introduces friction.

Friction is one of the strongest determinants of whether interest can evolve into meaningful action. A person may be interested in a product, yet abandon the journey when confronted with a confusing interface, unclear value positioning, long loading times, unexpected requirements, or inconsistent messaging. These moments introduce doubt or resistance, which leads people to pause or leave, regardless of how curious they may have been.

Many organizations misinterpret the absence of progression as a lack of desire, when the actual cause often lies in the experience. This misunderstanding leads teams to alter messaging or increase budget instead of addressing issues within the journey. When friction remains unaddressed, no amount of interest will reliably convert.

Evaluating friction as part of intent interpretation allows marketers to understand where people hesitate and why. This approach creates a clearer picture of what is genuinely driving behavior.

How Curiosity Gets Misread as Consideration

Curiosity is a valuable stage in the discovery process, but it does not always mean that someone is evaluating a purchase. People often engage with content because they find the subject matter interesting, enjoyable, or informative. Long-form articles, how-to videos, guides, and topic-based commentary naturally attract large audiences who may have no intention of purchasing anything in the category.

Teams often misinterpret these signals because curiosity and early-stage consideration can appear similar when looking only at time spent and repeat visits. Both involve exploration, but the underlying motivation is different. Curiosity is driven by an interest in learning, while consideration is driven by the desire to solve a problem or fulfill a need.

Identifying this distinction requires examining what people do after consuming content. Someone who reads multiple articles but never explores product-specific materials is likely acting out of curiosity. Someone who moves from content into comparison pages, detailed descriptions, or pricing exhibits a different form of engagement.

Accurate interpretation depends on the ability to recognize the difference between interest in a topic and interest in a solution.

Why Signals Differ by Platform and Should Not Be Treated Equally

User behavior is shaped significantly by the platform they are on. A click on TikTok reflects a different motivation than a click on LinkedIn. A view on YouTube often signals intentional consumption, while a view on Instagram Stories may reflect rapid browsing. A comment on Reddit tends to involve analysis or skepticism, whereas a comment on Facebook may reflect impulse.

Treating all platform signals as interchangeable leads to misguided conclusions. Teams may assume that high engagement on a low-intent platform represents meaningful interest, or they may overlook strong signals on platforms that produce less volume but higher relevance. This creates distorted expectations and leads to misaligned strategies.

Creating a platform-based intent hierarchy helps correct this. Platforms designed for problem-solving, such as Google Search or industry review sites, naturally produce more direct intent signals. Platforms focused on entertainment or inspiration often reflect earlier stages of exploration. When marketers apply different weights to signals based on where they originate, their interpretations become more accurate.

How Funnel Thinking Fails to Capture Real Behavior

Funnel models simplify user behavior in ways that do not reflect real decision-making. The idea that people move from awareness to interest to consideration to decision in a linear progression has shaped marketing strategy for decades. Yet audiences rarely behave in such a structured way.

People revisit earlier stages frequently as they encounter new information or reconsider their needs. They may pause their exploration for reasons that have nothing to do with the brand. They may compare options early, then step away, then return later with fresh questions. Linear models create expectations that do not match actual behavior and lead to strategies that feel mismatched to how people think and act.

A more realistic view treats intent as something that fluctuates. Marketers who observe how behavior changes over time gain a clearer understanding of where audiences stand. This perspective reveals the complex rhythm of exploration, doubt, enthusiasm, and hesitation that traditional funnels fail to capture.

How Third-Party Intent Engines Get Misinterpreted

Third-party intent platforms have become central tools in B2B marketing. Providers such as 6sense, Bombora, Demandbase, G2, ZoomInfo Intent, and others help organizations identify which companies might be researching certain topics or categories. This data is often positioned as a means of detecting emerging demand.

Many teams misunderstand these signals by assuming they indicate immediate buying readiness. In reality, third-party data reflects broader patterns across organizations. Someone within the company may be reading industry articles or reviewing best practices. A team may be documenting relevant technologies for future planning. Topic interest does not necessarily translate into near-term purchasing.

Third-party intent becomes meaningful when combined with first-party behavior. When an account shows increased topic research and also begins visiting the brand’s own product or pricing materials, the alignment strengthens the case for outreach. When third-party signals appear without supporting first-party evidence, they represent early exploration rather than active evaluation.

Understanding the limits of third-party data prevents teams from acting prematurely or expecting outcomes that the signals cannot support.

Why Silence Does Not Always Mean Disinterest

Silence tends to create anxiety in marketing because engagement is one of the most visible indicators available. When people stop opening emails, stop returning to the website, or stop clicking ads, the common assumption is that interest has faded. Teams often respond by increasing contact frequency or altering targeting, hoping to recapture attention.

Silence can reflect many factors unrelated to intent. People may be overwhelmed with other responsibilities. They may be conducting private research. They may be waiting for internal approvals or budget clarity. They may even be more interested than before but hesitant to engage publicly.

A more useful approach interprets silence as a neutral signal that requires patience. Instead of pushing harder, brands can offer materials that encourage quiet exploration. Comparison tools, detailed guides, and transparent product information help maintain relevance without creating pressure. When audiences are ready to reengage, they often do so with more clarity.

How Last-Click Behavior Creates False Attribution

Many digital metrics still prioritize the last action taken before a conversion. This creates the illusion that the final touchpoint played the most significant role in shaping the decision. Although the last click often reflects the moment of action, it rarely reflects the moment of conviction.

People usually convert through whichever method is most convenient. A direct visit may follow weeks of quiet research. A branded search may come after a conversation with a colleague. A click on a retargeting ad may reflect timing rather than persuasion.

Focusing excessively on last-click signals leads teams to overvalue easily measurable actions and undervalue the deeper interactions that shaped the decision. This misalignment often distorts budget allocation and weakens the strategic foundation of the marketing program. Intent analysis requires an appreciation of the full sequence of behavior, not just the final step.

Why Signal Strength Matters More Than Signal Volume

Marketing organizations often celebrate large volumes of signals, equating more activity with more interest. A high number of views, clicks, or interactions may look impressive, but these numbers do not always reflect meaningful intent. Large volumes of weak signals often contribute little to decision-making.

In contrast, strong signals are specific and deliberate. Individuals who return to the same pages repeatedly, revisit pricing after a gap in time, or engage with content that addresses key decision factors reveal more about their motivations. These patterns may appear in smaller numbers but hold greater predictive value.

Teams that evaluate signal strength rather than signal volume develop a more realistic understanding of readiness. This approach reduces wasted spend and leads to more accurate targeting.

Why Negative Intent Signals Provide Critical Insight

Negative signals are often overlooked because they disrupt optimistic narratives. However, they provide valuable information about where the experience does not meet expectations. Reduced time on key pages, repeated abandonment at the same step, switching from branded to generic searches, and reviewing cancellation policies all reveal friction or uncertainty.

Understanding these patterns helps identify where audiences hesitate and why. Addressing these issues strengthens the experience and reduces future abandonment. Negative signals highlight areas that require attention, offering clarity that can improve both messaging and product design.

Why Intent Should Be Viewed as a Dynamic Pattern

Intent is not static. It changes with circumstances, information, timing, and personal context. People may appear fully engaged one moment and then disengage for a period. They may take long pauses during evaluation or revisit earlier research after gaining new information. Treating intent as a fixed profile prevents marketers from recognizing these natural fluctuations.

Viewing intent as a dynamic pattern encourages teams to analyze how behavior shifts over time. This approach creates a more accurate understanding of where audiences stand and helps align communication more closely with their actual state.

How Brands Can Interpret Intent Signals More Accurately

Improving how intent signals are interpreted requires moving away from rigid frameworks and toward more contextual thinking. Signals should be understood within the environment in which they occur. First-party and third-party data should complement each other rather than serve as standalone indicators. Strategies should adapt to the fluid nature of decision-making rather than force people through predefined stages.

Three principles guide this approach.

First, context matters more than scale. The meaning of a signal changes with timing, sequence, and platform. Second, the combination of first-party and third-party signals offers a more complete picture than relying on either alone. Third, recognizing the non-linear nature of decision-making helps reduce misinterpretation and improves communication alignment.

A Final Thought

Intent signals help reveal the behaviors that influence decisions, but they do not speak with the clarity that many dashboards suggest. Teams who interpret these signals as definitive answers often misunderstand what the audience is expressing. Those who treat them as clues develop a deeper understanding of what people need, what they are unsure about, and how they move through the decision-making process.

The value of intent signals lies not in their volume, but in the insight they offer when interpreted correctly. When brands take the time to understand context, reduce friction, observe patterns, and analyze strength rather than scale, they gain a more accurate picture of behavior. This clarity shapes stronger communication, more relevant creative, and more efficient allocation of spend.

Marketing will always involve a degree of uncertainty, but teams who understand intent with nuance will be better positioned to make decisions that align with real human behavior.