When we began building the marketing system for Iberosattel, my premium saddle brand, our first impulse was to chase scale. The logic seemed simple: find the keywords with the highest search volume, bid on them, and drive as much traffic as possible. We looked at terms like “baroque saddle” and “premium horse saddle,” and the numbers were enticing. They promised a wide net for potential customers, so we did what most do—we built a campaign around reach.
The results were immediate, but not in the way we’d hoped. We got clicks, but they felt hollow. Traffic was unfocused, engagement was low, and the cost of a meaningful interaction was unsustainable. Our system was generating noise, not signal—a classic case of being busy without being effective.
That’s when we made a pivotal shift: we stopped optimizing for clicks and started optimizing for understanding. We decided to deliberately ignore 90% of the available keyword volume and focus on the 10% that truly mattered.
The Observation: Finding Signal in the Noise
Instead of focusing on broad, high-volume terms, I started digging into the search query reports—the raw language people were actually typing into Google. Hidden beneath the generic searches were phrases that were diagnostic, specific, and deeply problem-aware.
Queries like:
- “saddle for horse with very short back”
- “treeless saddle for wide shoulders”
- “which saddle fits a horse with a high wither”
These weren’t just product searches; they were cries for help. Each one told the story of a specific, often frustrating, problem: a rider struggling to find a solution, a horse in discomfort. This was where the real intent lived. On a macro level, the data backs this up: research consistently shows that long-tail keywords have a click-through rate 3% to 5% higher than generic searches. They perform better because they are conversations, not just queries.
This concept is often visualized as the „long tail“ of search. Imagine a graph where a high curve on the left represents broad „Head Terms“ (e.g., „saddle“), which quickly drops off into a long, low line extending to the right, representing specific „Long-Tail Keywords“ (e.g., „saddle for horse with short back and wide shoulders“).
We were seeing the digital equivalent of a customer walking into a store and saying, “My horse has this specific problem, can you help me?” The high-volume keywords were like someone yelling “Saddles!” in a crowded street. One is a dialogue; the other is just noise.
This shift from chasing volume to decoding language became the first foundational layer of our marketing system. It wasn’t just about finding better keywords; it was about understanding our customers‘ world so deeply that we could meet them exactly where they were.
The Framework: From Keywords to Problem-Solution Pairs
This discovery led us to a new framework. We abandoned the traditional model of keyword buckets and replaced it with a system of “Problem-Solution Pairs.”
Here’s how it worked:
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Identify the Problem: We documented every high-intent, long-tail query not as a keyword, but as a specific customer problem. “Horse with short back” became a problem category.
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Map the Solution: For each problem, we mapped the specific Iberosattel product or feature that solved it. The Amazona Dressage saddle, for instance, is ideal for that short-backed horse.
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Align the Message: We rewrote the ad copy to speak directly to the problem. Instead of a generic headline like “Premium Baroque Saddles,” we wrote, “Finally, a Saddle for Horses with Short Backs.”
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Construct the Path: We designed the landing page to be the final piece of the solution, showing only the relevant saddle and explaining exactly how it solved that specific anatomical challenge.
This approach transformed our results. We were tapping into a powerful principle: personalized CTAs convert 202% better than default versions. Our ads and landing pages stopped being generic marketing and became the direct answer to a question the user had just asked. This wasn’t just an advertising tactic; it was a real-time test of our product-market fit, query by query—and one of our first successful running experiments in mapping customer intent to system design.
The Insight: High Intent Always Beats High Reach
The biggest lesson from this initial phase wasn’t about Google Ads—it was about system design. We learned that a scalable system isn’t built on the widest possible foundation, but on the deepest one. By focusing on high-intent queries, we weren’t just getting better leads; we were gathering a rich dataset of our customers‘ most pressing needs.
This principle—that high intent always beats high reach—became a core tenet in building our marketing system. It informed everything that came later, from our content strategy and editorial calendar to how we trained our sales team. We learned that the goal isn’t to reach everyone. The goal is to be profoundly useful to the right people—the ones who are already looking for the solution you provide.
While many companies spend fortunes on broad awareness campaigns hoping to capture attention, we learned it’s far more efficient to listen for the whispers of high-intent users and build a system that gives them exactly what they need, the moment they need it. The foundation of a great performance marketing system isn’t technology or budget; it’s a deep, granular understanding of human intent.
Frequently Asked Questions
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What exactly is search intent?
Search intent is the underlying goal a user has when they type a query into a search engine—the „why“ behind the search. It’s typically categorized into four main types: informational (looking for information), navigational (looking for a specific website), transactional (looking to buy something), and commercial investigation (comparing products before a purchase). Our focus was on queries that showed a clear mix of informational and transactional intent. -
Why are long-tail keywords so important for a new system?
Long-tail keywords are longer, more specific search phrases. While they have lower individual search volume, they are less competitive and tend to convert better. For a new system like ours, their primary value was as a source of data. They provided unfiltered insight into customer pain points—insight that was far more valuable than a high volume of generic traffic that taught us nothing. -
Does this mean I should ignore high-volume keywords completely?
Not necessarily, but they should be approached with a different strategy and at a different stage. To build a performance foundation, long-tail keywords provide the clearest signal of user need. Once you use that signal to build and refine your messaging and conversion paths, you can begin targeting broader terms, confident you have a system that can effectively handle the less-qualified traffic. -
How does this concept of „intent“ apply outside of paid ads?
This is the key insight: understanding user intent is platform-agnostic. The „Problem-Solution Pair“ framework we developed from search ads became the blueprint for our entire content strategy. We began writing blog posts, creating videos, and designing website sections that directly addressed the problems we discovered, effectively designing conversion paths that began with a customer’s first question.
