The Problem With Standard Keyword Research
The typical keyword research process looks like this: you open a tool, you enter a seed keyword, you look at the results sorted by search volume, you pick the keywords with the most volume that aren't too competitive, and you build content around those keywords.
This process produces the same keyword list as every other brand in your category using the same tool. It prioritizes volume over intent. It treats keywords as traffic opportunities rather than as windows into human psychology. And it generates a content strategy that's fundamentally defensive — you're chasing the same queries as everyone else, competing on who can rank best for content that looks similar to all the other ranking content.
The keyword research framework I've used to build organic strategies for clients across Apple Music, Häagen-Dazs, and dozens of other brands is different. Here's how it works.
The Intent Proximity Model
Every keyword exists on a spectrum from "pure awareness" to "purchase ready." The distance between the searcher's current state and the point of purchase is what I call intent proximity.
A search for "what is SEO" has low intent proximity — the person is in pure learning mode, far from any purchasing decision. A search for "SEO agency pricing for mid-market e-commerce" has high intent proximity — this person has a problem, knows the solution category, and is evaluating options.
Standard keyword tools sort by volume. The intent proximity model sorts by commercial potential — the probability that someone searching this query is within the conversion funnel for your business.
The implication: keywords with lower search volume but higher intent proximity are often more valuable than high-volume keywords with low intent proximity, particularly for service businesses and B2B.
Building an Intent Proximity Map
The process:
Step 1: Start with the buying journey, not the keyword tool.
Before opening a keyword tool, map your ideal customer's buying journey. What do they experience before they have the problem you solve? What do they search for when they first realize they have the problem? What do they search when they start evaluating solutions? What do they search when they're choosing between specific options?
This produces a rough map of the journey — from awareness to consideration to decision — in the specific terms your actual customers use.
Step 2: Generate keyword lists for each journey stage.
Now use keyword tools to expand and validate each stage. Ahrefs, SEMrush, Google Search Console, and Google's own autocomplete/People Also Ask features will all surface relevant variations. The goal is a comprehensive list organized by journey stage, not sorted by volume.
Step 3: Score for intent proximity.
For each keyword cluster, assign a rough intent proximity score (1-10) based on how close someone searching this query is to the conversion event you're optimizing for. High scores for keywords that indicate active evaluation ("pricing," "reviews," "vs competitor," "for [specific use case]"). Low scores for keywords that indicate early awareness or education.
Step 4: Identify the white space.
Overlay your keyword list on your existing content. Where are the gaps? Which high-intent-proximity keyword clusters have no content covering them? Where is your content addressing low-intent queries when high-intent versions of the same topic are being searched?
The white space — high intent proximity, no existing coverage — is your content priority queue.
The Categories I Always Target First
Problem-aware queries: The searcher knows they have a problem and is looking for solutions. Often question-based ("how do I fix [problem]") or symptom-based ("why is my [metric] declining"). These are high-trust-building opportunities because you're reaching someone at the moment of maximum receptivity for your expertise.
Solution-comparing queries: The searcher has identified the solution category and is comparing options. "[Category] vs [category]," "best [solution] for [specific context]," "alternatives to [competitor]." These are high-conversion queries that most content strategies underserve because they require specificity about competitors.
Buying-decision queries: The searcher is ready to choose and is doing final research. "[Brand] pricing," "[brand] reviews," "[brand] vs [specific competitor]." These have often lower volume but the highest conversion rates.
Long-tail commercial queries: Highly specific queries that precisely describe a problem, use case, or context. "SEO strategy for B2B SaaS with product-led growth motion" has a fraction of the volume of "SEO strategy" but a much higher probability that any visitor is a relevant prospect.
The Categories I Deprioritize
Pure informational head terms without commercial adjacency: "What is digital marketing" has enormous volume and almost no business value for a digital marketing agency — anyone searching that is in pure learning mode, far from a purchase decision, and well-served by dozens of better resources.
Keywords where we can't credibly produce the best content: Keyword research should be honest about whether the content you'd create for a given query would genuinely be the best available resource. If not, the traffic won't convert and the ranking won't be sustainable.
Keywords with declining intent over time: Some queries trend toward informational intent as categories mature. The people searching them now are different from the people who searched them three years ago. Intent isn't static.
From Keyword Map to Content Strategy
The keyword map is the input. The content strategy is the output.
For each high-intent-proximity cluster with a coverage gap, the content strategy specifies:
- The primary keyword and supporting cluster
- The intent it serves and the journey stage it addresses
- The specific, differentiated angle that makes our content genuinely better than existing results
- The internal linking relationship to other content in the intent map
- The CTA that moves the reader toward the next stage of the buying journey
- Standard keyword research produces commodity strategy — the same list as every competitor using the same tools
- Intent proximity is the missing dimension: how close is someone searching this query to the conversion event you're optimizing for?
- Build the buying journey map first, then use tools to expand and validate — don't start with tools and retrofit a journey later
- High intent proximity + low competition = highest ROI content — often lower volume than head terms but dramatically higher conversion potential
- White space analysis: where do high-intent-proximity queries have no coverage in your existing content?
- Each content piece should have a specified CTA that moves the reader toward the next stage — keyword mapping and conversion architecture are the same job
This is not a content calendar. A content calendar tells you what to publish when. This tells you what to publish, why, and how each piece fits into a strategy for capturing and converting the specific buying journey of your target customer.