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One ad set spent $169 today at a 3.4% CTR. I killed it before lunch.
Another was at 20.9% CTR on $9 of spend — I left it alone.
That's the whole job. Know which one to kill and which one to leave running.
This week on one content creator account: 11 active ad sets, $393 spent today, average CTR across healthy sets sitting at 17–21%. Two paused, zero wasted
overnight.
Most brands don't have someone watching this daily. The algorithm doesn't pause bad ads — you have to.
If you're spending $2k+ per month on Meta and nobody is checking your ad sets every day, you're leaving money on the table.
DM me. I check accounts daily and tell you exactly what to cut and what to scale.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Everyone's talking about the Meta vs Shopify attribution gap.
There's a simpler mismatch nobody mentions.
#Meta's algorithm is constantly optimizing toward your best performing SKUs.
Your best performing SKUs are the ones selling fastest.
The ones selling fastest are the ones closest to sold out.
So Meta is spending your budget driving traffic to your most vulnerable inventory.
In real time. Right now.
Without knowing it.
And without you knowing it either.
A customer clicks the ad. Lands on the product. Hits a sold out button.
The ad did exactly what it was supposed to do.
The inventory couldn't back it up.
And Meta keeps optimizing toward that SKU because the click data looks good even while the conversion data quietly falls apart.
Most Brands discover this when they pull the weekly report and wonder why ROAS dropped.
By then the campaign has already spent days driving traffic to a product that couldn't convert.
The fix isn't a Meta setting.
It's knowing in real time which of your fast-moving SKUs are approaching zero before the algorithm spends another dollar sending traffic to a sold out page.
Have you ever pulled your Meta spend and realized your budget was optimizing toward inventory that was already gone?
hashtag#Shopify hashtag#DTC hashtag#EcommerceOperations hashtag#PaidMedia hashtag#InventoryManagement
Your website gets about five seconds.
A founder lands on it and decides to keep scrolling or close the tab before they read a word. Most sites lose them in the first one.
So I'm starting a series. Founders Teardown.
Every week I take one real founder's website and break down what's working, what's quietly costing you customers, what to fix this week, and how to turn random browsers into permanent clients.
No "make it pop." The actual decisions that win or lose the sale.
Want yours next? Drop your URL in the comments and I'll tear one down next week.
you are really excellent!
The Ultimate Bottleneck in B2B Digital Marketing: Solved.
If you run online marketing for high-value B2B industrial equipment, you know the struggle. Selling million-dollar machinery—like our high-power laser cutting systems here—is a completely different beast compared to B2C e-commerce.
In our industry, we constantly hit a massive roadblock when it comes to measuring true ROI. The core pain points are always the same:
Marathon Conversion Cycles: B2B decision-making isn't instantaneous. It takes months of technical evaluations, multiple stakeholder approvals, and offline negotiations.
The Tracking Black Hole: The buyer's journey inevitably jumps from online clicks (Google Ads, Meta, LinkedIn) to offline channels (WhatsApp, emails, factory visits, direct sales calls). Traditional pixel tracking completely breaks down the moment the conversation goes offline.
The Attribution Nightmare:Without end-to-end visibility, it is nearly impossible to accurately attribute a final Purchase Order back to the specific ad campaign, keyword, or creative that sparked the initial interest. We end up optimizing campaigns based on superficial top-of-funnel metrics (like CTR or initial CPL) rather than actual revenue and high-quality pipeline generation.
For a long time, this disconnect meant marketing teams were essentially flying blind when trying to scale budgets effectively.
But here is the good news:
After extensive testing across our global markets, I have finally cracked the code to this attribution gap.We have successfully implemented a solution that connects the dots from the very first ad impression to the final closed deal.
By building a robust closed-loop data architecture—integrating Offline Conversion Tracking (OCT), dynamic parameter passing (GCLID/FBCLID), and deep API syncs with our CRM—we can now feed actual, offline sales data directly back into our ad platforms' machine-learning algorithms.
The result?We are no longer guessing. We can track exactly which campaigns drive the highest-value POs, optimize our tROAS/tCPA bidding strategies based on real revenue, and drastically reduce the cost of high-quality leads.
It wasn't an easy fix, but after validating this closed-loop system in real-world combat across North America, Europe, and Asia, the impact on our global digital performance has been transformative.
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