Client Overview

Tulip is on a mission to reinvent the funeral-arrangement experience. They offer one simple cremation package that can be arranged online or over the phone, providing families with affordable and more convenient alternatives to traditional funeral homes.

The Challenge

Before we started working with the team, Tulip already had an active account for more than a year, but couldn’t scale due to the high cost per lead and CAC. They were also planning to expand to new locations and needed a partner that could support their rapid growth.

Strategy

We couldn’t track the end purchases with pixel-based conversion, so we had to rely on back-end data in order to optimize the campaigns. With different pricing strategies for each location, keyword, and both at-need and pre-need demand, we had to keep a tight account structure in order to connect the dots.

Smart bidding was all the hype as always, but by testing different bidding strategies we found that some keyword categories are more suitable for manual CPC, while others for Target CPA. We ended up with a hybrid strategy between manual and automated bidding, occasionally re-testing our approach.

Even with a good structure in place, our insights were limited because we were getting only a fraction of purchases on the platform. To get the real ROI from our campaigns we implemented and automated the process of offline conversion tracking (OCT) in Google Ads. This allowed us to be faster with re-allocating the spending based on performance and to maximize efficiency. OCT also allowed us to get purchase insights on demographic and ad level, as well as to do proper price sensitivity testing in each location.

Our biggest impact however came from our creative team and the ongoing messaging optimizations. On one side we were constantly battling and outsmarting our competitors for the most competitive “cremation” category, while on the other, we opened up new efficient volume by re-positioning ourselves for keywords like “mortuary” or “funeral”.

Results

With ever-rising competition, we still managed to keep our dominance in the California market, while successfully expanding to new locations, and are proud to say that we brought peace of mind in difficult times to thousands of families in the US.

Lastly, the focus of this case study and how we achieved the biggest impact is through our ad optimization process. Below are a few examples of our creative team at play.

For the funeral keyword category, we positioned the ads to filter out misguided users while still keeping high relevance. Our hypothesis was that by positioning Tulip as a funeral home, we were paying for clicks from people that were already sold on the classic funeral home experience. With the challenger ads, we made sure to clearly communicate our “cremation” offer, state why it’s the better option, and still mention “funeral” in order to stay relevant to the user’s searches. For example, on the left is a preview of the “old” ads that were running when we took over the account. And, on the right, is the ad positioning approach that helped us achieve 40% lower cost per lead and CAC, without sacrificing volume.

For the mortuary keyword category, we tripled the purchase volume while getting 35% lower CPQ and 3x ROI!

Here, we employed the psychology of the Cognitive Bias called “Framing Effect”, which hypothesizes that people react to a particular choice in different ways depending on how it is presented, as a loss or as a gain. We took the previously winning ad headline that said “Cut the Mortuary Expenses” and reframed it to say “Mortuary Costs Included”. It was communicating the same value, but its framing made a huge difference.

For the cremation-related terms, at one point we took a significant hit on the CTR and Lead volume from a new competitor that offered free cremation with body donation. Even though users were price sensitive, we managed to get back on track by focusing on our dignified cremation process and high-touch level of service. We also doubled down on the transparency of our pricing as most of our competitors had hidden fees.

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.