Intent over volume: a practical bet for the AI search era

Your content ranks. Traffic is up 40% year-over-year. But pipeline hasn’t moved.

Here’s the question: What if those 10,000 monthly visitors are optimizing for the wrong outcome?

Something fundamental is changing in how content strategy works. Most marketers are still operating with outdated assumptions. The metrics they’re celebrating might be selecting against their best prospects.

This article explores an emerging shift from optimizing for keyword volume to serving specific buyer intent. We’ll look at why AI search might be accelerating this.

We’re not presenting established rules. We’re sharing our strategic thinking and what we’re seeing as we make this bet on intent-first content.

The old playbook worked (past tense)

The traditional approach was straightforward:

Find keywords with search volume. Create content targeting those terms. Measure rankings, traffic, impressions. The assumption: more visibility equals more business.

This approach worked for years. It wasn’t wrong. It was right for its time. Companies built entire marketing strategies around this playbook and saw real results. Rankings mattered because they determined who showed up when buyers searched.

But the mechanism that made it work is changing.

What we mean by “intent” (with precision)

Before we can talk about intent-first content, let’s define what “intent” actually means. Not as a buzzword, but with operational precision.

Intent is the buyer’s job-to-be-done at a specific moment that a page solves completely.

More practically: A page is intent-aligned if a qualified reader can accomplish their task without visiting another page.

Three intent classes we’re targeting:

Problem diagnosis intent

The reader suspects they have a problem but needs language to articulate it. Example query: “Why isn’t my content converting even though traffic is up?”

Vendor evaluation intent

The reader is comparing approaches or partners and needs criteria to decide. Example query: “How do I know if my agency thinks strategically or just executes tactics?”

Implementation detail intent

The reader has decided on an approach and needs specific execution guidance. Example query: “How do I structure a content brief for intent-first content?”

This isn’t everything. It’s the lens we’re using to make strategic bets about what content to create.

How AI search works differently

Traditional search rewarded surface relevance at scale. Conversational systems increasingly reward problem completion.

Our tests suggest pages that solve a single evaluator task end-to-end are more likely to be cited than pages that broadly “cover a topic.”

Here’s the mechanism:

Traditional search (PageRank-era) matched keywords in queries to keywords on pages, then ranked by authority signals like backlinks and domain age.

AI search tools (Perplexity, Google’s AI Overviews, ChatGPT with search enabled) actively search multiple sources and synthesize answers tailored to your specific question. They understand semantic meaning and context, then surface content that comprehensively solves the specific problem.

Semantic understanding

LLMs don’t match words. They understand concepts. A query like “how do I know if my agency thinks strategically” maps to content about agency evaluation frameworks, not pages that happen to contain those exact words.

Real-time synthesis

When you ask these tools a question, they search across sources and pick the ones that best answer your specific intent. They’re not just matching keywords. They’re evaluating which content solves your problem.

Citation-worthy signals

AI systems seem to care about:

• Comprehensive coverage (does this fully answer the question?)
• Clear problem-solving (can the reader take action?)
• Authoritative voice (does the source demonstrate expertise?)
• Depth over breadth (does it go deep on one problem versus shallow on many?)

What we’re seeing:

• Users increasingly start with AI assistants, not traditional search
• Zero-click searches continue rising as Google answers questions in SERP features
• Conversational queries are longer and more specific than typed keywords
• AI citations appear to favor depth and expertise over keyword optimization

The data isn’t fully in yet. These are patterns we’re seeing, not proven doctrine.

Running the numbers (hypothetically)

Here’s what the math looks like:

Note: These are illustrative numbers to show the mechanism, not actual case study data. Even with conservative assumptions (Scenario C), the intent play still outperforms on client acquisition.

Why this math matters:

Intent-aligned content serves people looking for what you offer. High-volume content serves everyone, and most of them aren’t ready, qualified, or interested.

The conversion rate difference isn’t magical. It’s mechanical. When someone searches “why isn’t my content converting,” they have a specific, acute problem. When someone searches “content marketing strategy,” they might be a student writing a paper, a competitor researching your approach, a junior marketer learning basics, someone vaguely curious, or (rarely) a qualified buyer.

You’re paying the same cost to attract all these visitors, but only one type converts.

The strategic trade-off:

Choosing Scenario B means accepting that your traffic dashboard looks “worse.” Your CEO might ask why traffic is down. Your marketing team might feel like they’re failing because they can’t hit volume targets.

But your pipeline tells a different story.

Our strategic bet (with testable predictions)

Here are our specific, testable hypotheses:

Prediction on conversion quality:

Pages written for vendor-evaluation intent will generate a higher opportunity rate per 100 sessions than pages written for definition intent within the same product line.

Prediction on return traffic:

Long-dwell, low-volume pages will attract a higher percentage of branded return traffic within 30 days compared to high-traffic, low-dwell pages.

Prediction on AI citations:

Pages that fully answer a conversational query are more likely to be cited by AI systems (ChatGPT, Perplexity, Claude) than listicles targeting head terms.

Prediction on deal velocity:

Prospects who engage with intent-aligned content will close 30-40% faster than prospects who enter through high-volume keyword content.

Where volume still wins:

This isn’t an either-or. Some situations still favor the volume play:

• New-category awareness where you must create demand from scratch
• Low-consideration offers where frequency beats nuance
• Brand plays where share of voice itself is the KPI

If you’re launching a new product category, you need volume to establish the concept. If you’re selling a $19/month tool with a 30-second decision, volume likely wins. But for complex B2B services where buyers research deeply before committing, intent alignment matters more.

What we’re sacrificing:

Keyword opportunities that “should” be easy wins. Traffic numbers that look impressive in reports. Rankings for terms our competitors chase. The ability to say “we drove 10,000 visitors this month.”

Why this connects to our integration philosophy:

Just as we believe all specialists should work from shared discovery materials (not filtered summaries), we believe all content should serve a unified strategic goal (not departmental metrics).

Keyword-chasing is the content equivalent of siloed specialists: each piece optimizes for its own metric (rankings, traffic) without asking whether it serves the business outcome (qualified pipeline).

Intent-first content is the integrated approach: every piece exists to solve a specific problem for a specific buyer at a specific moment in their journey.

We’re making a calculated bet and we’ll know the answer by measuring conversion quality, return traffic patterns, AI citation frequency, and deal velocity.

What this means for content creation

This shift doesn’t just change what you write. It changes how you start.

The process shift:

What changes in practice:

This means we start with buyer problems, not keyword research. We ask “who is this for?” before “what keywords can we target?”

It requires learning to accept that intent-first content may never rank number one for high-volume terms. That’s fine if it ranks number one for the query our ideal buyer asks.

This pushes us to optimize for what makes content citation-worthy: depth, expertise, and clear problem-solving.

We’re shifting to measure business outcomes (conversion quality, assisted deals, time-to-close) instead of just traffic metrics.

Example from this very article:

We’re not targeting “content marketing strategy” (5,400 searches/month) or “SEO best practices” (2,900 searches/month). Those keywords serve a different intent. They’re for practitioners learning tactics.

Instead, we’re serving readers who are questioning whether the traditional playbook still works. That’s a much smaller audience, but they’re thinking about content strategy at a deeper level.

If this article gets 200 readers who all spend 8 minutes engaging with it, that’s more valuable than 5,000 readers who bounce after the first paragraph looking for a quick tip.

The dashboard problem

What if the SEO dashboard you check every Monday is optimizing you away from your best prospects?

Traditional SEO metrics (rankings, traffic, impressions) were designed for an era when more visibility always meant more opportunity. In 2025, those metrics increasingly measure the wrong thing.

When you celebrate moving from position 5 to position 3 for “content marketing strategy,” you’re celebrating more students finding your content for their papers, more competitors researching your approach, more junior marketers learning basics, and more vaguely curious browsers.

What you’re not celebrating is more qualified buyers who are ready to have a conversation.

Here’s what’s happening:

Your content team might be hitting their KPIs while selecting against qualified pipeline.

This doesn’t mean keyword research is worthless. The metrics we inherited from 2015 SEO are becoming less predictive of business outcomes. Rankings still matter, but ranking for what matters more.

Objection: “This is just traffic versus pipeline talk.”

Fair pushback. There’s a key distinction though. We’re not saying ignore traffic. We’re saying optimize for intent first, and let traffic follow (or not). The traditional approach optimizes for traffic first and hopes intent shows up.

Objection: “This only works if you already have traffic, brand, and resources.”

Counter-intuitive truth: This approach might work better for companies without massive traffic.

Why? You’re not competing in saturated keyword spaces. You’re creating content for specific problems that larger competitors ignore because the “search volume isn’t there.”

You’re fishing in less crowded waters.

A practical path forward: see it, do it, scale it

Don’t take our word for it. Here’s how you can validate this hypothesis yourself.

This isn’t theory. It’s a testable approach any reader can run.

1. See it: the 5-minute micro-experiment

Don’t trust us. Trust your own eyes. Here’s a three-step test anyone can run to observe the pattern we’re betting on:

Step 1: Ask a conversational query

Open ChatGPT, Perplexity, or Claude and ask a question a buyer would ask. Not a keyword. A problem. For example:

• “How do I know if my content marketing agency thinks strategically or just executes tactics?”
• “Why is my website traffic up but qualified leads are down?”
• “What questions should I ask to evaluate whether an agency integrates specialists or just coordinates them?”

Step 2: Note what gets cited

Look at which sources the AI cites and why they seem citation-worthy. Are they comprehensive (fully answer the question)? Do they demonstrate expertise (not just generic advice)? Do they solve a specific problem (not broadly cover a topic)? Are they written for an evaluator’s intent (not just education)?

Step 3: Compare to traditional SERP winners

Now Google the “head term” version of that query (e.g., “content marketing agency”). Compare the AI-cited sources to the pages ranking in positions one through five in Google.

What you’ll likely find: The AI citations are often different from the traditional SERP winners. AI favors depth and problem-solving. Traditional search still favors authority signals and keyword optimization.

What this means:

We’re not claiming causality. Just offering a clean way to observe the pattern. Run this test with queries relevant to your business. What you discover might inform your content strategy bets.

2. Do it: your first intent-based bet

This micro-experiment isn’t just validation. It’s the start of a different approach.

If you wanted to test this hypothesis with content, here’s where we’d start:

One intent-based piece this week:

Instead of writing “What is [topic]” (high volume, educational intent), write “How to know if [specific problem you solve]” (lower volume, diagnostic or evaluation intent).

Identify your highest-intent topics:

Mine your sales call recordings for the specific questions qualified prospects ask right before they decide. Those questions are your highest-intent topics, even if they have zero search volume in keyword tools.

Measure differently:

Track which content pieces appear in the “Recent Activity” section of closed deals in your CRM. That’s your signal, not traffic volume.

This isn’t a complete methodology. It’s an exploratory direction for anyone curious about testing this alongside us.

3. Scale it: where this bet leads

If you’re wondering where this leads strategically, here’s what we’re seeing:

The long game:

AI search continues to improve semantic understanding. Conversational queries become the dominant mode of search. Traditional keyword metrics become less predictive of business outcomes.

Content quality (depth, expertise, helpfulness) matters more than volume. Early movers in intent-first strategies build citation authority that compounds.

What success looks like:

Smaller traffic numbers, higher conversion rates.

Content that gets cited by AI systems builds brand authority at scale. Even when AI answers the question directly (zero-click), being cited as the source establishes credibility with qualified buyers. Some systems like AI Overviews and Perplexity still drive traffic through citations. Others provide pure brand value.

Prospects arrive already educated on your approach. Sales cycles shorten because content pre-qualified the buyer.

Important caveat:

Or maybe traditional SEO adapts and remains dominant. We don’t know the future. We’re making a calculated bet that conversational search rewards intent-alignment over keyword optimization.

Practice what we preach

We’re not targeting “content marketing strategy” or “SEO best practices” because those keywords serve a different intent. We’re serving you. Someone thinking about whether old content playbooks still work.

If this resonated, that’s intent-first content at work. If you bounced after reading the title, we filtered correctly.

Here’s our test: We’re betting on 150 deeply engaged readers over 2,000 browsers.

What’s one high-volume keyword you’d be willing to sacrifice if it meant attracting more qualified buyers instead? What specific buyer problem could you solve for your best-fit prospects?

We’re genuinely curious what trade-offs others are considering. Leave a comment or reach out. We’d like to hear what you discover.