Case study · Healthcare · Google Ads and Meta

It was their best channel by every number they had. Every number they had stopped too early.

Who this is about

A healthcare business spending up to $250,000 a month across search and social. Someone registers, gets screened for eligibility, and then either reaches clinical care or doesn’t. The patient who reaches care is the one the business earns from, so that’s what the account is judged on. We took it over in October 2025, with most of the budget sitting in social.

Their reporting said social was the best channel. It wasn’t.

By every number the client had, social was working. Registrations were cheap and there were plenty of them. What nobody could see was what happened to those registrations afterwards, because the back-end tracking stopped at the sign-up. So the channel producing the most registrations looked like the best channel in the account, and the conversion everyone was judged on wasn’t the one that mattered.

The easy version of this job is to report against that number and hit the target. We told them their best channel was the problem instead, and asked to move the budget before anyone could prove it. If we were wrong we’d have cut their largest source of volume on a judgement call, and it would have been ours to answer for.

Reporting against a number we didn’t believe would have been safer. It wouldn’t have been right.

What made the difference

It took someone with skin in the game

None of this happens unless someone treats down-funnel revenue as their own problem. Reporting on platform data is fast and it always looks fine. Gluing back-end data to platform data by hand takes twice as long and tells you things you’d rather not know. The tracking fix wasn’t a marketing task at all, so it meant twice the meetings with their data team and months of chasing owners outside marketing who had their own roadmaps. An agency logs that as a client dependency and reports the blocker every week. We took it as ours.

We made the call before the data could prove it

We pulled what the back end did hold, filled the gaps with stand-in measures and with what accounts like this one usually do, and argued for a radical change in the budget split. It took a lot of pushing. When the reporting was rebuilt months later it confirmed the call. Search was converting into eligible patients at a rate social never came close to.

We taught search to bid on a proxy for a real patient

Once the tracking held we sent Google a down-funnel signal, a proxy for a registration that would turn out to be eligible. The algorithm stopped optimising for people who fill in a form and started optimising for people who qualify. It also got choosier, which is why cost per click climbed to $13.24. Click to qualified registration went from 0.93% to 4.64%.

Every platform will tell you it’s working. Only the back end can tell you which one is right.

Did the volume hold?

It doubled. 235 engaged patients in October 2025, 498 in July 2026. January against July is the cleanest read: 497 patients on around $250,000 of spend, then 498 on around $150,000. Same output, $100,000 less.

Questions we get asked

Client name withheld at the client’s request. All figures are actual reported results for the periods shown. Cost per engaged patient and cost per qualified registration are paid media spend divided by total volume from all sources.

Case study · EdTech · Meta · Performance creative

Nobody can pick the winning ad in advance, so we stopped trying to.

Isn’t 704 ad tests just spray and pray?

It’s the fair objection, and volume on its own would have produced nothing.

Most accounts fail in one of two directions. The first is over-investing in a handful of ads. Weeks of concepting, rounds of internal review, four beautiful launches. It wastes the opportunity, because nobody can identify the winning concept in advance. Not the strategist, not the designer, not the client. Limiting yourself to a few attempts is an expensive way to be confidently wrong.

The second is shipping everything to see what sticks. That produces the occasional winner and no knowledge, because without a stated hypothesis you can’t tell whether an ad won on the angle, the format, the offer or the timing. So you can’t do it again on purpose.

The split matters here. Of the 704 tests, 461 were new concepts or angles and 243 were variants of something already working. Two thirds of the output was a genuinely different idea rather than a resize or a colour change.

Nobody knows in advance what will win. That’s the argument for volume, and it’s also why every test needs a reason attached.

Who this is about

An EdTech business where the conversion that matters is an enrollment. Paid social hadn’t been used at any real scale before, so there was no library of proven ads, no history of which messaging worked, and nothing to build on.

What made this work

The design team sat under the performance team

One of the key factors behind volume, data driven hypotheses and fast testing is having the design under the performance team. Not a brand team operating on completely different KPIs. Not a creative agency that cares more about how the work looks than how it performs.

You need them working desk by desk, one Slack message apart. From the moment a data insight lands to the brief being written should take hours, not days or weeks.

At 27 ads a week, a week between reading the data and acting on it costs you a full round of tests.

Two senior people held the account every week, no rotation and no juniors. A performance lead who reads the whole funnel from CTR down to CAC and knows how the algorithm spends. A designer who turns the idea into something that does its job and builds statics and motion at test speed. Both answering to the same numbers, which in a lot of accounts is the whole difference.

Every hypothesis started from data

No spaghetti at the wall. Every concept started from something observed. Reddit threads and other external discussion where people talk about the decision in their own words. The client’s customer survey. The questionnaire applicants fill in when they apply, which is a large pile of first party language about what people want and what is stopping them.

Each of those sources produced several hypotheses rather than one, and we tested most of them. That is where a lot of the volume comes from. It is not 704 random ideas, it is a much smaller set of real signals worked through properly.

Every test got a postmortem

Results were read back against the hypothesis, not just against CPA. That’s the step that turns 704 launches into an accumulating body of knowledge instead of 704 separate events, and it’s the one almost everyone skips.

Questions we get asked

Client name withheld at the client’s request. All figures are actual reported results for the periods shown.