Many Web3, crypto, fintech, and other complex-industry teams struggle with fragmented content systems. Topics are disconnected from the customer journey, search intent is misaligned, internal links are an afterthought, and content decisions often depend more on assumptions than a clear prioritization process.
This week, I revised SEO fundamentals and started building a repeatable strategy process. The goal was to start building a system that can support clearer content decisions before anything gets written.
Why This Matters
A common pattern I see across content-heavy industries is that teams accumulate blogs, landing pages, documentation, support content, educational resources, and product pages over time without a clear framework connecting them.
As a result, content can rank without converting, educate without supporting adoption, or answer individual questions without building topical authority around the broader subject.
For teams trying to improve AI and traditional search visibility, user education, onboarding, or conversions, publishing more content is rarely the first answer.
The bigger issue is often that they do not have a clear way to diagnose:
- Which topics are missing
- Which pages are competing with each other
- Which assets fail to support the user journey
- Which opportunities are most likely to improve content ROI
Without that system, content investment becomes harder to measure and easier to waste.
This week made me think more clearly about the gap between content production and content architecture.
What I Built and Learned
I started by revising topics like entity SEO, topical authority, topic clusters, semantic relationships, internal linking architecture, keyword clustering, search intent classification, SERP analysis, content opportunity sizing, and funnel-stage mapping.
The more useful part was applying them. I spent most of my time building and testing pieces of a repeatable content strategy process.
SEO Topical Map
The first piece was a topical map template. Rather than organizing content around isolated keywords, I mapped core topical entities, closely related entities, search intent, funnel stage, internal linking opportunities, and potential content ideas.
One of the biggest shifts in my thinking came from entity SEO.
Historically, I thought about topic planning primarily in terms of keywords and topic clusters. This week pushed me to think more about semantic relationships and entity coverage. Search engines and AI systems evaluate how well content covers related concepts within a topic, not just whether a target phrase appears on the page.
Building this semantic content plan helped me think less about individual keywords and more about whether a content ecosystem covers the entities and relationships needed to demonstrate depth on a subject.
To build it, I started with the pillar page on modern SEO strategy, the main entity I want my site to be associated with. From there, I mapped six service pages as secondary entities, each connected to a specific area of my work. The idea is for each service page to act as a hub, with informational blog posts as spokes covering semantically close entities and related questions.
For now, I selected only three to five spokes per hub as an exercise. The goal was not to build a complete content plan yet, but to practice thinking about content architecture in terms of entity relationships, topical depth, and authority building.
Competitor Gap Analysis
This week, I also built a competitor content gap sheet (pictured below) and a simple prioritization framework. The aim was to identify content opportunities and create a process for evaluating them. I looked at factors such as ranking potential, business value, and effort required to determine which topics deserved attention first.
This is particularly relevant for content audits and roadmap work. Many teams already have more ideas than they can realistically execute. The challenge is deciding which opportunities are most valuable and why.
Since my own site is still early, I chose some ideas from my topical map rather than relying only on competitor gaps. That felt more aligned with building topical authority from the ground up.
SERP Analysis
I also created a new SERP analysis template (my old one was a Doc, not a Sheet) and filled it out for the first post I plan to write. I used the spreadsheet to compare ranking pages, page types, intent, word count, People Also Ask results, and related searches. From there, I wrote down some content recommendations.
Before moving into outlining, I added two additional steps to my process.
First, I looked at keyword clustering. The SERP analysis was based on the primary keyword for the post, but I also wanted to understand which related queries shared the same or very similar intent.
It’s not a new concept that one page should usually target a shared intent cluster rather than one isolated keyword. What felt different was applying it more deliberately as part of the strategy process.
Second, I reviewed semantic entities and relationships again.
The SERP analysis already gave me a strong starting point. If certain pages are ranking on page one, they are likely covering enough of the relevant entities, subtopics, and semantic relationships for search engines to understand them as useful results. That is what I captured in the “Most Common Subtopics” section of the table.
But I also wanted a second-pass check, so I used Gemini. It suggested that “maintenance and re-optimization” and “optimizing for AI Overviews and semantic search” were also important concepts, even though only a couple of ranking pages mentioned them explicitly. It also surfaced a few entities that were missing from my spreadsheet:
This made me think more carefully about SERP analysis, something I have been doing in different forms since I started working in SEO in 2020.
The old advice was usually: analyze every page ranking in the top 10 and create something better. That still holds up, but it’s not enough to add more words, include more headings, or cover the same subtopics in a cleaner format.
A stronger page needs to satisfy the shared intent behind the query cluster, cover the entities and relationships search systems expect to see around the topic, add something meaningfully useful that competing pages do not, and fit clearly within the site’s broader content architecture.
In practice, that means SERP analysis has to connect page-level quality with system-level questions like what this page should own, what it should not compete with, what it should link to, and how it helps search engines and AI systems understand the site’s authority on the broader subject.
Customer Journey and Funnel Stages Map
Finally, I created an initial customer journey and funnel map. It is still basic, but it helped me think more deliberately about how content supports business outcomes rather than existing as a collection of standalone assets.
Individually, none of these is a new idea. What felt valuable was combining them into a more structured process.
To complete all these mini-projects, I used tools like Semrush, Ahrefs, SEOTesting’s Sitemap URL Extractor, Keywords Everywhere, Detailed SEO Extension, MST SERP Counter, Nightwatch, ChatGPT, and Gemini to gather data, compare pages, and build parts of the workflow.
What I’m Taking Forward
I am starting to turn my SEO knowledge into a more complete, repeatable content working system. This week helped me connect the theory I revised, especially around entity SEO, keyword clustering, and prioritization, with practical templates I can actually use in future client work or in a full-time role.
That matters because the teams I want to work with rarely need someone to simply “do SEO” in a vague sense.
- A Web3 infrastructure team may need help connecting documentation, educational content, and product pages so developers can understand the use case and move closer to adoption.
- A crypto or fintech company may need to identify which pages build trust, which ones create confusion, and which content opportunities are most likely to support conversions.
The templates I built this week are the first building blocks for the whole system. They may need more testing, refinement, and real-world application, but that’s the direction I want to keep building toward: a plug-and-play content strategy system that can adapt to different teams, different content libraries, and different business goals.
