A familiar client with a new problem

A past client reached out. They were building something new: a small ad network that would show ads on Shopify’s Thank You page, the screen buyers see right after completing a purchase.

They already had the tech built, but just a few networking-attracted leads. Not enough advertisers. No system to find them.

That’s where I came in.

A fresh start

The client didn’t just need advertisers but the right kind of advertisers. If I had gone wide and generic, they would have ended up with time-wasters: brands that couldn’t convert on this kind of surface, weren’t aligned with Shopify’s rules, or didn’t value fast, buyer-qualified traffic.

So I focused on:

• Brands that already sell to consumers

• Offers that make sense right after a purchase

• Clear funnels, no friction, no surprises

• Categories that Shopify allows (no risky stuff)

Why I didn’t just search “ecommerce” or “DTC”

Those terms are too broad. I needed clues that the advertiser already understood buyer behavior and could work in a post-purchase context.

To find them, I searched for phrases like:

“Get cash back”

“Tap to install”

“Thank you gift”

“Your order is confirmed”

These kinds of ads showed up over and over in niches like wellness, beauty, finance, and tools for online shoppers. They were already geared toward action, you just had to catch them at the right moment.

Scoring that matters

I wrote a scoring script in Python to go through every ad. It checked for a few key things:

• Was the offer clear and relevant?

• Was it in a safe, approved category?

• Could someone act on it easily?

• Did the landing page make sense?

• Did it feel trustworthy?

I also added rules to filter out ads that wouldn’t work like anything from crypto, CBD, or shady-looking sweepstakes.

Most ads didn’t score well. That was fine. I was after the top 3–4% — the ads that looked clean, fit the format, and felt like something a buyer might actually click after finishing a purchase.

The first results

• Within my analysis, I captured over 50,000 active ads across all query runs

• Specially created Precision Ad Scoring Agent™ applied to 7,400+ advertiser entries

• Each ad was evaluated on 5 axes, with bonus/penalty logic enforced under deterministic thresholds. The goal was buyer-qualified intent mapping, not traffic farming.

• As “Ideal Fit” identified potential ~300 advertisers, representing the top ~4% of qualified brands

• Exclusion rate: ~47% scored 0–3 due to poor UX, category risk, or offer opacity

Wrapping up

This project started with nothing: no leads, no process, no system. I helped the client go from zero to a focused list of high-quality advertiser prospects, all matched to a very specific use case: ads shown right after a Shopify customer makes a purchase.

I didn’t work on the product itself. My job was to figure out who would actually want this kind of ad placement, find them, and get them ready for a conversation. That meant researching, filtering, and building a lead generation process they could keep using.

It wasn’t about volume. It was about finding the right fit and making sure the client didn’t waste time chasing leads that were never going to convert.

Simple, useful, and built to last.