Turning fragmented content into a durable organic system
Turning fragmented content into a system that compounded over time
The context
When I started doing what eventually became “rebuilding the organic growth engine,” organic growth already existed at Nulab.
There was:
blog content
some ranking pages
scattered SEO wins
multiple products competing for attention
content created by different teams at different times for different reasons
Traffic was growing, but unevenly. Some pages performed well, others ranked accidentally, and a lot of content existed without clear purpose or support.
What didn’t exist yet was a system:
no unified organic strategy
no consistent topic model
no clear prioritization by business value
no shared definition of what “good” organic content looked like
Growth was happening — just not deliberately.
How the work actually started
This didn’t begin as a greenfield rebuild or a big strategic reset.
It started as cleanup.
Early work focused on:
auditing existing content
identifying duplication, overlap, and decay
spotting high-performing pages that weren’t supported by surrounding content
clarifying which content belonged to which product
pruning or consolidating low-value pages
improving internal linking and navigation paths
Most of this work was incremental and iterative. There was no single launch moment — just steady improvement.
How a system emerged
Over time, patterns started to show up.
Content performed better when:
related pages were grouped around a shared user intent
articles addressed full questions instead of isolated keywords
strong pages were supported instead of left alone
internal linking made discovery easier
That’s when topic clusters and the Learn hub began to take shape — not all at once, but gradually.
As content was grouped and refined:
topic ownership became clearer (e.g. collaboration, project management, strategy)
editorial standards became more consistent
decisions about what to create next became repeatable
What eventually looked like an “engine” was really the result of repetition.
How SEO and content decisions worked together
SEO wasn’t a separate function handing requirements to content.
In practice, I was making the tradeoffs directly:
choosing topics based on search demand and product relevance
shaping content around real user questions
balancing educational depth with evaluation and conversion intent
adjusting based on performance over time, not launch metrics
SEO decisions were content decisions, and vice versa.
The structure that stabilized over time
As the work matured, a clearer structure emerged:
Blog for company updates, announcements, and brand storytelling
Learn hub for evergreen education, search discovery, and product activation
Solutions and examples for use cases, evaluation, and commercial intent
This structure clarified:
where content belonged
how users moved from discovery to understanding to product exploration
how content could support both acquisition and adoption
The Learn hub became the foundation — not because it was declared so, but because it consistently performed.
Decisions that shaped long-term performance
Several practical decisions had an outsized impact over time:
removing SEO-driven content from the blog to prevent dilution
avoiding a generic “Resources” section in favor of purpose-built hubs
prioritizing scalability and clarity over short-term traffic spikes
treating content as something that needed maintenance, not just launches
These weren’t flashy moves, but they made the system easier to grow, govern, and sustain.
How results accumulated
There was no moment where growth suddenly flipped on.
Performance improved because:
more content aligned to real intent
existing winners were supported instead of isolated
internal linking improved discoverability
content decay was addressed instead of ignored
Over time, those changes compounded.
Today, the Learn hub is the primary driver of Nulab’s non-branded organic discovery, reaching approximately:
~56K monthly organic visits
~9.6K ranking keywords
~3.4K top-3 keyword positions
~1.6K referring domains
Domain Rating: 74
The content has also surfaced consistently across AI-driven discovery experiences, including Google AI Overviews and large language models — a byproduct of structure, depth, and usefulness rather than optimization for those surfaces specifically.
Why this still matters
Search has changed a lot — especially with AI-driven interfaces — but this work reinforced something I still believe strongly:
Organic growth compounds when content is treated as a system, not a collection of pages.
This project wasn’t about predicting every shift. It was about building something sturdy enough to adapt as the rules changed.
What this shows about how I work
This case study reflects how I approach content strategy in practice:
I start with what exists, not with idealized frameworks
I improve systems through iteration, not one-time redesigns
I’m comfortable working in ambiguity before clarity exists
I focus on compounding improvements rather than launch moments
The “engine” wasn’t built in a sprint. It was built by paying attention, fixing what didn’t work, and repeating what did.