Scaling a template library into a demand-validated content system
How search demand, competitive patterns, and capacity constraints shaped what we built — and what we didn’t
The context
Over time, templates became one of the most valuable content surfaces in the product.
They sat at the intersection of:
product usage
onboarding and activation
organic discovery
conversion
Templates were consistently among the highest-converting pages on the site outside of core product pages. That made them high-leverage — but also high-risk. Poorly chosen or poorly structured templates don’t just underperform; they quietly add maintenance cost and dilute the product experience.
What started as individual template pages gradually needed to function as a system.
Phase 1: defining what a “good” template page needs
Early on, I noticed that template pages across the industry followed fairly consistent patterns — and when those patterns were missing, pages felt incomplete or untrustworthy.
To understand baseline expectations, I analyzed competitor diagram landing pages and documented:
common page elements (CTAs, examples, videos, benefits, integrations)
which features appeared on most pages vs some vs few
how CTA language aligned to user intent
One clear insight was that intent mattered more than polish. Pages that asked users to do the thing (“Make a flowchart”) performed better than pages that jumped too quickly to product or trial language.
The goal at this stage wasn’t differentiation — it was avoiding silent failure. If a template page didn’t meet baseline expectations, it wouldn’t matter how good the product was.
This work established a repeatable page pattern teams could rely on when creating or updating template pages.
Phase 2: validating which templates were worth building at all
As the template library expanded, new ideas came from everywhere: user requests, internal suggestions, and product intuition.
Many ideas sounded reasonable. Some even sounded great. But templates are long-lived assets — expensive to design well and costly to maintain — and my concern was that we were at risk of building based on anecdote or instinct alone.
The core question became:
Are we building templates people are actually looking for — or templates we think they should want?
I was asked to evaluate both existing and proposed template ideas through a search lens and help narrow a large, unfocused list into a clear set of priorities.
How I approached prioritization
Rather than treating this as keyword research for its own sake, I treated it as a comparative decision problem.
For each template idea, I:
identified relevant search queries through competitive analysis and manual discovery
evaluated keyword difficulty, local volume, and global volume
normalized the data so ideas could be compared side by side
categorized each idea by overall search potential (High, High–Global, Mid, Low)
Search demand became a proxy for demonstrated user need — not traffic forecasting.
This allowed the team to discuss template ideas with evidence instead of opinion.
Key decisions and tradeoffs
The most important work here was interpretive, not mechanical.
I made deliberate choices to:
treat discoverability as part of usefulness, not a bonus
weigh global demand differently depending on whether a template was free or premium
deprioritize ideas that sounded useful but showed little evidence of demand
make tradeoffs explicit instead of forcing every idea into the “top” category
Saying no clearly was as valuable as saying yes.
What this enabled
Using this framework, we narrowed a long list of ideas into a focused set of templates worth pursuing.
The outcome wasn’t just a “top 30” list — it was shared clarity around:
which templates justified investment
which were better suited for niche use cases
which ideas were better left unbuilt
how demand should factor into future decisions
Search signal became a neutral input into product and content prioritization, rather than something applied after the fact.
Pressure-testing scalability
As part of this work, I explored whether template evaluation and publishing could become programmatic — regularly validating demand and shipping new templates over time.
The opportunity was real. Templates consistently converted well and supported activation.
What became clear, though, was that a sustainable program required consistent design and development support. Given competing priorities across teams, that level of resourcing wasn’t available at the time.
Rather than forcing a cadence that couldn’t be maintained, the focus stayed on making better one-time decisions — prioritizing quality, clarity, and longevity over volume.
That assessment was as important as the analysis itself.
Why this still matters
Templates are a classic example of content that fails quietly.
You can build excellent templates that no one ever finds — or you can give users what they’re already looking for, and do it better than alternatives.
This work reinforced principles I still rely on:
validate demand before investing deeply
design for baseline expectations before differentiation
treat discoverability as part of usefulness
respect capacity as a real constraint
What this shows about how I work
This case study reflects how I approach product-adjacent content systems:
I use external signal to ground internal decisions
I’m comfortable narrowing scope instead of expanding it
I design repeatable frameworks, not one-off answers
I value restraint as much as ambition
Templates didn’t become a content system overnight. But by defining expectations, validating demand, and respecting constraints, they became something the team could scale without waste.