The brand was strong. The dashboard it was steering by wasn’t.
A leader in its category, with a 77-year heritage and a loyal base, was making spend decisions on agency numbers that didn’t match its own books. So the first job was getting the measurement honest. Then we tuned the store, the email flows and the ads against it, before anyone added a dollar more.
0.97% → 2.73%
Site conversion, same traffic
3× → 9 to 14×
Return on ad spend, same budget
+79%
Sessions, year over year
A luxury skincare company founded in 1948, sold today across North America, the UK, Germany and Asia. It has a proprietary ingredient with a real origin story, and a name built over decades on live TV shopping, where the founder herself became a beloved on-air personality. One of the leading brands in its category. By early 2025 it had the things most DTC companies spend millions trying to manufacture, a strong product and decades of genuine heritage, with a loyal base to show for it. The digital side just hadn’t been tuned to match yet.
- Standing
- A leader in its category
- Industry
- DTC premium skincare
- Revenue
- ~$20M
- Founded
- 1948 · three generations of family leadership
- Engagement
- 90-day intervention
“We need more ad spend.”
“The agency’s reported return didn’t reconcile with the books, and the store and the email flows had never been set up to convert the buyers already arriving.”
The diagnostic turned up a handful of misalignments, all connected. None of them was dramatic on its own. Together they held a strong brand below what its traffic could deliver. The store was converting paid traffic at 0.97%, roughly a third of the Shopify average. That’s headroom.
- Inflated, opaque reporting. The agency claimed 11.7× ROAS on Meta. Cross-referenced against Shopify, the numbers didn’t reconcile. One “winning” campaign had generated $918 in net sales against $600 in spend.
- Zero segmentation. 27,000 to 39,000 email subscribers and no demographic data at all. A 60-year-old loyalist and a 37-year-old first-timer got the same newsletter, the same offer, the same message.
- Friction in the store. ~100 SKUs with no hierarchy. 80%-off ads landing on generic pages. A popup firing on top of the promo. And a $99 free-shipping threshold that surprised buyers at checkout.
- Automation mostly switched off. Klaviyo was running as a basic email sender. Welcome, cross-sell, browse-abandonment, post-purchase, VIP and win-back flows were missing or disabled, so most of the machinery in the tool was just sitting there dead.
Rather than overhaul everything at once, we ran a focused 90-day intervention on the constraints with the most pull, and the rule was simple: tune what’s already there before adding spend. There’s no magic in any of it. Mostly it’s doing things in the right order.
- 01
Agency audit and transition
A forensic review flagged fraudulent like-purchasing that was suppressing organic reach, and campaigns optimized for clicks instead of purchases. Which is wild, when purchases are the only thing anybody’s paying for. Ad management came in-house, with structured coaching.
- 02
Klaviyo flow architecture
Rebuilt the automation strategy: welcome, indoctrination, abandoned cart and checkout, cross-sell, browse abandonment, post-purchase and VIP, plus a win-back moved to 90 days and written in the founder’s voice. Plus ongoing subject-line testing.
- 03
Segmentation and data
Captured birth-year data through an incentive campaign, so a 37-year-old prospect sees different creative than a 55-year-old loyalist. Klaviyo segments synced to Meta for exclusions and lookalikes.
- 04
Creative diversification
A handful of untested creatives with inconsistent attribution gave way to multiple hooks across multiple angles, clear naming conventions and UTM tracking.
- 05
Founder-led storytelling
Coached the third-generation leader on short-form storytelling. Three generations of one family is a narrative no competitor can copy, and it’s cheap to produce.
- 06
Ambassador program
An opt-in structure that turns the brand’s most passionate customers into nano-influencers through affiliate links. No upfront payment, segmented by age.
The first result was the truth about the numbers. The agency’s dashboard reported 11.7× on Meta. Reconciled against Shopify it was closer to 1.5×, and one “winning” campaign had made $918 on $600 of spend. So why did nobody catch it sooner? Because the agency was grading its own homework, and until the reconciliation nobody’d set that grade next to the actual sales. Budget, performance and growth plans had all been set against a figure that didn’t exist. Double the spend on that number’s say-so and every extra dollar’s screwed before it leaves the account. That finding is what changed the decisions.
Once the measurement was honest, the execution had something real to move against.
| Metric | Before | After | Change |
|---|---|---|---|
| Reported vs. real ROAS | 11.7× reported | ~1.5× reconciled | Truth |
| Site conversion rate | 0.97% | 2.73% | +181% |
| Meta ads ROAS | ~3× | 9 to 14× | +200% to +367% |
| Sessions, year over year | Baseline | +79% | +79% |
| Ad management | External agency | In-house with coaching | Full control |
| Audience segmentation | None | Age-cohort targeting | Activated |
| Klaviyo utilization | Basic ESP | Flows + segmentation | Expanded |
The conversion jump took the brand from well below the Shopify average into the range benchmarks call strong for established DTC beauty, which works out to roughly 2.8× more orders from the same ad spend. Paid media went from ~3× to 9 to 14× on the same budget. And all of it counts only because it’s measured against a baseline that finally reconciles. Stack a massive lift on top of a number that was never real and what you’ve got is nonsense with a chart attached.
“Before your guidance we were seeing maybe around 3× on every dollar of ad spend. After what you showed us, we started to see 9, 10, 11, 12, even 14×. It was an amazing experience.”
The first deliverable was the truth about the numbers. Before touching a single flow, I reconciled the agency’s reported massive gains against actual Shopify sales, and they didn’t hold up. You can’t tune what you’re mis-measuring. This case is anonymised at my request; the figures are real and independently reconciled.
This brand already had what most DTC companies spend millions trying to manufacture: a 77-year heritage, a genuine origin story, and a leader with the charisma to carry it. None of that needed changing. Leadership was making sound decisions on numbers that weren’t true, and every call about spend, growth and performance ran through a dashboard that didn’t reconcile with the bank. Why? Because a dashboard looks like measurement, and hardly anyone opens the hood on something that already looks like measurement. The first job was making the numbers real, because you can’t find the headroom until you can trust the map. Almost no one’s checked theirs.
How much of your ad spend is being judged against a number nobody’s reconciled?
The Next Version finds where the money is actually stuck (starting with whether your numbers are even real) and names each move, what it’s worth, and who runs it. You keep the roadmap either way.