Resurrecting Content Ops from a Dead Stop
Originally published June 5, 2025 to The Builder’s Log – Log 002 on LinkedIn
Builder’s Log Entry 001: Resurrecting Content Ops from a Dead Stop
Headline: The Blueprint for Reviving Dead Content Ops (No Team, No Strategy, No Time)
Initiative: Content Ops Resurrection + Ecosystem Rebuild
Approx Start Date: Mid-February 2025
Starting State: No publishing. No team. No working strategy.
Quick Context
In the middle of Q1, the content engine stopped on the heels of layoffs. With the creation and publishing rhythm disrupted, there was no content going up and none going out. For the past few years, revenue has been generated by inbound, almost entirely. And half of that inbound pipeline came from organic content.
So, content ops being at a standstill was a big deal. Here’s a reconstruction log still in progress.
Phase 1: Sifting Through the Rubble
Just me and the illustrator. And every content initiative, across every channel, went cold.
I wasn’t even trying to restart yet. I was just trying to figure out what was salvageable. I sifted. That’s the best word for it. Not audited. Not optimized. Just sifted. Through dashboards, old calendars, failed drafts, loose ideas, abandoned Coda docs.
It wasn’t triage. It was archaeology.
This wasn’t about jumping into a new strategy. It was about figuring out if anything from the old one was still worth using. A few pieces were fine. But most of it didn’t fit anymore – not with the way buyers were behaving, not with the way our team was resourced, and not with where the market had moved.
Phase 2: The Paid Reckoning
Around that same time, I realized I had to figure out Paid. Not in theory. In full. The person overseeing our external paid team was gone. The campaigns were still running. And I had no idea what levers were being pulled.
So I got my hands in it. I didn’t know Google Ads. But I had AI. Maverick, my AI assistant, walked me through the bones – campaign structure, match types, where money leaks when nobody’s watching.
I started with structure, match types, cost signals, and CTR benchmarks. I reviewed 24 closed-lost calls to understand what buyers actually needed from us, then built a new strategy rooted in intent, not keywords.
I handed that strategy off to our external team. Nothing changed. So I took it back. And the minute I took over execution and started doing the work of iterating and testing, the cracks showed fast. And now they were my cracks. Maybe I should have let the paid team handle this pivot after all.
Sorilbran’s Ego: Nah, bump this! If anybody’s strategy is gonna fail, let it be mine!”
And for weeks, it looked like I was getting a lesson in failure. Fast and furious.
CPA went WAY UP. Impressions were WAY DOWN.
I iterated: rewrote copy, rerouted campaigns, tested landing page flows.
I watched the campaigns like a hawk, and looped in my AI assistant to help me decipher and refine testing. Whenever the spirit hit, I asked it:
“What should I be checking now?”
“Where’s the friction in this funnel?”
“How long before impressions catch up to relevance?”
“What am I missing here?”
AI became my strategist on call, offering diagnoses, sense-checks, second opinions.
I followed the traffic. Watched user behavior. Compared it to what we were hearing in sales calls. And for weeks, nothing happened. Nothing but work. Eventually, after a dozen cycles of refinement…
CPA dropped.
CTR lifted.
The machine – and I – started breathing again.
But let’s be clear: From my seat-of-my-pants training to actual improvement took
Three.
Full.
Months.
Full ones, fam.
No shortcuts. No silver bullets. Just strategy, systems, and iteration on top of iteration until the engine finally caught.
Phase 3: Rebuilding the Foundation – Clean, Modular, AI-Legible
Fixing Paid meant fixing where it led (and fixing the conversion rate in the process).
So I overhauled our landing pages:
Cleaned up code and rewrote sections and built new landing pages to match buyer intent. It was a heavy lift that needed a hack. So, I worked with AI to design modular blocks I could copy and reuse anywhere on our site. For speed and sanity and sleep.
Our CEO mentioned JSON. I learned it and injected structured data (JSON-LD) into intent-focused pages to improve machine readability. Worked a treat.
I also began building unified messaging into our pages and across every asset. This was about the time I realized: our case studies needed… something.
Case Studies
They showed up in the exploration path of every single sales-qualified lead and the structured updates were making them visible in AI environments. But I noticed when people landed on our case studies page, they didn’t stay long at all. Made sense – it was an archive of 27 case studies with pics and metrics that may or may not have been relevant to the intent of the individual buyers.
On the surface, it seemed like our case studies were okay – they weren’t underperforming. But they were certainly underutilized. Here’s what I mean:
Upon careful inspection, I discovered that each case study was playing up a particular angle of a campaign. Some were celebrating awareness, while hiding GTM wins that could anchor real positioning work.
Another was framed around brand lift… but quietly revealed that the campaign had driven tens of thousands of product page clicks. That’s not just awareness. That’s performance – five years before it became the primary ask in sales calls.
In prioritizing new case studies, we were trashing our track record – one of the things that could help differentiate us in a noisy market.
We didn’t need more proof. We needed better storytelling and a system for extracting value from what we already had. So I built a custom GPT specifically designed to extract value and identify the narratives of individual case studies.
I spent an entire day doing nothing but working with AI to refine the series of prompts (11 in total) that were platform agnostic – meaning I could drop the prompts into any AI environment and get the output I needed to get, formatted exactly the way I needed it formatted. That meant I had a way of building case studies that was both user-friendly AND machine readable (that’s the real flex).
That cut the time it took to build case studies pages by about 70%. Which makes it scalable for a team where the writer also doubles as the strategist and builder.
Go Modular or Home
I also realized that I needed a way to rinse and repeat system elements at will. I needed to find ways to spend less time engineering content systems and more time building repeatable frameworks – hacks – that would let me move faster.
So, I started thinking in terms of finding ways to build once and use forever. Modular. I worked with AI to build Blocks – small content blocks that Maverick or a trained GPT can spin up in seconds as needed.
Clean. Clear. Machine readable. Dynamic – for folks with short attention spans.
Phase 4: The Newsletter Rebuild (aka The Positioning Lab)
Once Paid was running clean and landing pages were in the works, I turned to Organic. But not the blog. Not thought leadership.
Newsletters.
Tactical Tuesdays (email) → mid-funnel pain point alignment
Tactical Influence (LinkedIn) → executive POV and positioning test
This wasn’t about “just getting content out.” I treated the newsletters as the strategic fulcrum for the entire new system. If I could get the positioning right there (as evidenced by open rates and engagement), I could build everything else off the same insights.
This was important, because my hunch that we should tailor every piece of content around educating our ICP was right in theory. I needed it to be effective in execution.
But here’s where it gets deeper:
I didn’t blast the whole list and hope it worked though. I laddered the rollout, segment by segment, week by week.
Here’s how that worked:
- Newsletter Subscribers Only
First sends went only to users who explicitly opted in to the newsletter. These were the warmest of warm – engaged, expecting content. Baseline open rate: ✅ above 30%. - Engaged Marketing Leads
Once I saw the messaging resonating, I added in users who had engaged with other Shelf content or opened marketing emails in the past 30 – 60 days. Open rate still held. Messaging still landed. - Event Leads
After that, I tested the same content with folks we’d met at conferences – people we’d hand-verified at booths or added to CRM via follow-up. Different list. Different temperature. Still solid opens. - Cold Leads
Next up: the quiet ones. People who hadn’t opened in over 60 days. Could the message still pull them in? Surprisingly, yes. Not as high. But not dead either. - Never Engaged
Final test group: folks who had never opened a Shelf email. Ever.
I gave them the same high-value message with no fluff and a CTA that made sense. Some opened. Some clicked. Enough to prove the system could scale.
Each subject line was a pressure test. Each click gave me signal. Each piece that resonated was fed back into ad copy, visuals, landing pages, and sales decks. As validation.
Did we get unsubscribes? Absolutely. But our open rates went UP. By 20%. Both with our Emails and our LinkedIn.
I didn’t roll cold leads into every send. Because obviously.
But I’m positioning our email strategy to move into ABM and full sales enablement once I’ve automated enough of our other processes to be able to give ABM the attention it deserves. We already have a rhythm, and that’s something. I can train that rhythm into a machine. And scale it.
So, newsletters became the pulse check for messaging, targeting, and resonance. If it worked there, it would work anywhere else.
I effectively built our content ecosystem on newsletter opens. Because why the heck not?
Phase 5: The Pre-Automation Phase
This is where we are now.
Not automating. Yet. Still refining. Everything is in progress – nothing’s completely done. But we’re finally on an upward trajectory. And now I have solutions, frameworks, and systems to revive dead content ops.
Now, my days are spent:
- Scaling Paid with intent, not volume
- Cleaning up lingering UX and CRO friction across the site
- Training AI to draft in the tone and structure I’ve already defined
- Designing backend automations that’ll connect all of this. But only once the system is stable
Automation is the reward for building a system that deserves to be scaled.
What I Learned
- You don’t fix broken ops with new content. You fix the infrastructure.
- You don’t automate misalignment. AI only amplifies what’s already working. Or not working.
- Every asset must earn its slot. Credibility. Clarity. Conversion. Or it doesn’t get built.
- Consistency comes before scale. You don’t need volume nearly as much as you need resonance.
- If your content isn’t visible to AI, it’s not really visible at all.
Final Thought
Now I’m turning once again to the mounds of unstructured data we have so that I can start thinking through our sales enablement strategy. That shouldn’t be a separate thing with a special title, by the way. It should be baked into every aspect of your content ecosystem.
Content without a purpose doesn’t scale. It just clogs the system.
