Modern advertising has always revolved around securing consumer attention. Yet, merely capturing eyes on a screen no longer guarantees real outcomes. In our initial blog, From Attention to Intention: Redefining Performance with Intent Based Marketing, we introduced the idea that intent—not viewability metrics alone—truly drives conversions.
Let’s now dive deeper into intent based targeting and AI intention models as a strategic solution for advertisers looking to connect with high-intent audiences at precisely the right moment.
Attention metrics evolved because advertisers grew dissatisfied with superficial measurement. An ad might be “viewable,” but viewers often skip right past it if they lack genuine interest. Publishers sometimes exploit such metrics by overcrowding pages with ads, resulting in wasted budgets.
Conventional targeting often focuses on broad user traits or simple viewability. But these approaches overlook user intent, the critical indicator of someone’s motivation or readiness to act. If a consumer is in the research stage for a product or service, they’re more receptive to detailed information.
Conversely, a customer comparing prices might respond best to promotional offers. Recognizing these nuances reduces mid-funnel waste and ensures ad spend goes where it matters most.
Where attention is fleeting, intention signals a deeper commitment, whether that’s learning about a category or actively preparing to purchase.
By identifying high-intent users, you increase the likelihood of conversion and substantially improve performance metrics—all while respecting user privacy. This shift from mere visibility to meaning helps advertisers trim waste and realign marketing budgets with real outcomes.
Contextual advertising traditionally relied on keywords or broad topics, but it didn’t always capture a user’s mindset. Intent based targeting digs deeper, distinguishing casual curiosity from a genuine readiness to buy. Whether the user is checking detailed reviews, comparing costs, or seeking a product’s pros and cons, these actions highlight how far along they are in the buying journey.
What sets intent based marketing apart is its respect for user privacy. Rather than tracking personal data, you focus on the “why” behind a user’s content choice. For instance, if someone consistently reads about eco-friendly travel, an ad for renewable energy solutions hits closer to genuine customer intent than a random pop-up. By aligning messages with user motivation, advertisers see higher relevance, stronger engagement, and a more refined marketing approach overall.
Early contextual methods only scanned for keywords, missing the deeper meaning. AI intention models take it further by examining page structure, sentiment, and engagement signals in real time. Two similar-looking pages about electric cars may differ dramatically in tone—one might celebrate them; the other might criticize them. AI interprets these nuances, ensuring ads appear where the buying intent is highest.
Such AI models require vast amounts of labeled data to differentiate between purely informational content and content signaling purchasing decision readiness. Over time, the system refines its accuracy, quickly learning from which ad placements yield stronger results. This self-improving loop makes intent based targeting more flexible and immediate than any static keyword approach.
The mid-funnel often swallows large chunks of marketing budgets without delivering conversions. People might be curious but not quite ready to act. Intent based targeting, powered by AI intention models, helps filter out half-hearted browsers. Advertisers engage those who exhibit genuine interest—like searching for specific features or actively comparing reviews—rather than wasting impressions on visitors who remain on the fence.
With intent based marketing, brand messaging syncs up with the user’s stage in the buying journey, nudging them further along the funnel. In B2B marketing, for example, identifying a prospect who’s actively evaluating solutions can significantly improve lead generation efforts. You’re no longer yelling into the void; you’re helping potential clients finalize their choices.
Regulations such as GDPR and CCPA demand a privacy-first approach, complicating how advertisers gather data. Intent based marketing meets this challenge by relying on context signals within content rather than personal identifiers. Advertisers stay compliant while still honing in on high intent audiences who genuinely match the product or service being promoted.
Unlike retargeting that follows a user around the web, intent based targeting zeroes in on immediate signals within content.
Ads feel relevant rather than invasive, boosting user intent to click. Freed from heavy tracking scripts, brands can build trust by delivering more respectful, better-timed ads that align with the user’s actual interests.
Seedtag’s proprietary contextual AI Liz, interprets language, visuals, and engagement signals to determine real-time intent. Traditional approaches might label a page simply “travel” or “health,” but Liz detects whether the content indicates a user casually browsing or actively evaluating a product or service. The result is a more refined approach that resonates with potential customers on the verge of making a decision.
Many brands, from automotive to consumer goods, have cut costs and increased conversions by using Liz’s AI intention models. Instead of blanketing broad categories, ads appear in contexts that reflect actionable customer intent. This fosters an environment of higher relevance, fewer wasted impressions, and stronger ROI.
As machine learning grows more sophisticated, expect ongoing improvements in how systems interpret language and user signals. Advertisers will adapt in real time, recognizing subtle shifts in consumer interest. The upshot: a more agile marketing approach ready to pivot fast when trends or consumer preferences evolve.
The essence of intent based targeting is giving consumers ads they welcome at the moment they seek answers. With AI-driven insights, advertisers deliver messaging that stands out as helpful rather than intrusive. The result is a mutually beneficial ecosystem, where performance goals align naturally with user needs.
Gone are the days when superficial measures like viewability or broad-based targeting sufficed. Today’s advertisers must connect with real user intent, ensuring each impression lands in front of a high-intent audience ready to engage or convert. By integrating advanced AI intention models, the gap between vague browsing and concrete action narrows, helping brands cut through the noise.
Ready to meet your consumer right where they are with the power of intent based targeting with AI-driven precision? Learn how Seedtag’s proprietary AI, Liz, can transform your strategy