Our work

Six accounts, and the thinking behind them

Numbers on their own prove very little — anyone can publish a screenshot. What follows is the reasoning each account was run on, so you can judge whether the thinking would survive contact with yours.

Brands we have worked with

SnowUni Team MarketingEvolvo TechnologiesAZTALQuantum QuestSnowUni Team MarketingEvolvo TechnologiesAZTALQuantum Quest

D2C skincare

Glowrish Pharma

A skincare brand taken from launch to profitable acquisition inside 45 days.

4.6×
Return on ad spend: $31.4K
Revenue: 1,418
Orders

The objective

Launch a skincare brand into a direct-to-consumer market with no existing audience, no purchase history for the platforms to learn from, and no proven creative.

A launch account is the hardest version of this work. There is no data to optimise against, so the first weeks either build a usable signal or spend the budget producing noise. The brief was to reach profitable acquisition inside the launch window rather than after it.

The approach

The first decision was what the account would optimise toward, before any budget moved. With no purchase volume, optimising for purchases from day one starves the algorithm — the structure has to earn its way down the funnel as data accumulates. I set that sequence and the thresholds for moving between stages.

Tracking came before spend. Pixel and server-side events verified, deduplication confirmed, and platform-reported purchases reconciled against the store before the account was allowed to scale. On a new store this is where most launches quietly lose their first month, because every decision afterwards rests on numbers nobody checked.

On architecture: consolidated prospecting so conversion data concentrated rather than fragmenting across audience ideas, a separate testing lane so new creative could be judged without disturbing what worked, and retargeting kept deliberately small while the audience pool was still forming.

Rather than briefing individual ads, I defined the angles the brand would be tested on — the reasons someone buys and the objections that stop them — so results could be read by angle, and a fatiguing ad could be told apart from a failing message.

Dermatology, local service

Maryam Derma Clinic

Consultation enquiries for a clinic, judged on people who arrived rather than forms filled.

$4.52
Cost per lead: 420+
Leads: 38%
Booking rate

The objective

Generate consultation enquiries for a dermatology clinic at a sustainable cost, and — the harder half — make sure those enquiries became people who actually arrived.

A local clinic has finite capacity and a finite catchment. Volume alone is worthless: a cheap lead that never books occupies the same front-desk time as a good one and returns nothing. The objective was defined from the outset as booked consultations.

The approach

The first decision was the success metric. Cost per lead is what a local service account drifts toward, because it is visible in the platform on day one. I set the account to be read on cost per lead and booking rate together, since optimising the first in isolation reliably degrades the second — the cheapest enquiries are usually the least committed.

That required tracking past the form. Lead events, booking events and the path between them had to be captured so the booking rate was observed rather than estimated at the front desk. Without that link, any claim about lead quality is a feeling.

A local account has a hard geographic ceiling, so the constraint is frequency, not reach. I structured it around a tight catchment with budget concentrated rather than spread, and put the qualification into the ad and the form itself — the fastest way to raise booking rate is usually to let the wrong person disqualify themselves before they become a lead.

Campaigns were then supervised against both numbers, with messaging adjusted on what the booking data said rather than what the lead cost said.

B2B FinTech

Payalma

Five markets and four channels brought under one definition of a lead.

5
Countries: $28
Cost per lead: 41%
Qualified lead rate

The objective

Build a lead engine for a B2B financial product across five countries and several channels at once, for a product with a long consideration cycle and a buyer no single ad can reach.

Two problems sit underneath that. B2B lead volume is misleading — a form fill from someone with no authority costs the same as one from a decision-maker. And once Google, Meta, TikTok and organic search run together, every channel claims the same conversions, so budget flows to whichever platform reports most aggressively.

The approach

The central piece of work was unified attribution. I specified a single reporting view across Google, Meta, TikTok and SEO, so performance was read in one place against one definition of a lead rather than in four dashboards that each overstate their own contribution. Every decision downstream — budget, market, what got cut — depended on that view existing and being trusted.

Alongside it, the qualification standard. Qualified lead rate only means something if what counts as qualified is fixed in advance and carried through the tracking into reporting, not judged after the fact. Setting that definition is what turned the account from a volume exercise into a pipeline one.

Five markets do not behave alike: cost, competition, regulation and buying behaviour all differ. I set the account up so each market could be judged and funded on its own performance rather than averaged into a number that hides the weak ones, while keeping the structure consolidated enough to learn from.

Execution and budget allocation across channels were then supervised against that single view.

Dubai real estate

Dar Developers

Investor leads in a category where a cheap lead is evidence of a problem.

86
High-net-worth investor leads: $94
Cost per qualified lead

The objective

Generate investor enquiries for Dubai property from a high-net-worth audience — one of the most competitive and most fraudulent lead categories in paid media, saturated with brokers buying the same clicks.

The objective was never lead volume. It was a defined count of genuine investor enquiries a sales team could work, at a cost the deal size justified.

The approach

The first decision was to accept a high cost per lead deliberately. In this category a cheap lead is evidence of a problem, not a win. Setting the target against deal value rather than category benchmarks is what allowed the account to stop chasing volume that would never convert.

Qualification then had to be enforced in two places. In acquisition, through how the offer was framed and who it was built to repel as much as attract. And in the pipeline, through a CRM structure where lead stages were defined and recorded, so a qualified investor lead was a recorded state rather than a sales opinion.

That pipeline is what makes the cost per qualified lead a real number rather than an assertion.

The account was supervised against it, with budget and targeting decisions made on what reached the qualified stage, not on what reached the form.

B2B SaaS

B2B trading SaaS (MT4/MT5)

Monthly spend tripled without the efficiency collapsing on the way up.

3×
Monthly spend: 18% → 31%
Landing page conversion rate

The objective

Scale acquisition for a B2B SaaS product sold to trading platform operators, from a monthly Meta spend in the mid five figures to three times that.

This is a different problem from launching. At $60K a month the account already works, which means every structural weakness is being paid for quietly and will be paid for loudly at $180K. Spend of that size also exposes the destination page: at low volume a mediocre conversion rate is a rounding error, and at triple the spend it is the largest cost in the account.

The approach

The first judgement was what had to be fixed before the budget moved rather than after. Scaling multiplies whatever is already there, faults included, so structure, measurement and the destination page were addressed first. That ordering is the whole discipline of scaling, and getting it wrong is why most accounts become less efficient as they grow.

The landing page was treated as part of the media buy, not as a separate marketing asset. Conversion rate on the destination is a direct multiplier on every dollar in the account — at this spend level, improving it is cheaper than buying the equivalent volume. I set the direction for that work and the standard it was measured against.

A campaign set this size fragments easily, with budget spread so thin nothing accumulates enough signal to optimise on. I set the architecture so conversion data stayed concentrated as spend rose, and defined how budget was allowed to move — incrementally, with settling time — rather than in the jumps that reset learning and cost a month.

The set was then supervised in flight through the scaling period, with decisions about what to expand, hold and cut made against efficiency at each new spend level.

Financial services

Regulated forex broker, Cyprus

A funnel producing volume at the top and thinning out badly by the stage that mattered.

21% → 37%
Qualified lead ratio: 6% → 10%
Lead-to-deposit rate

The objective

Improve acquisition quality for a regulated broker in Asian markets, where the account was generating leads but too few were qualified and too few of those funded an account.

The brief was not growth. It was a quality problem, and in this category that pattern is expensive in two directions at once: wasted media on the way in, and wasted sales capacity on the way through.

The approach

The first decision was which number the account would be optimised against. An account judged on leads will reliably produce more leads and fewer deposits, because the two are in tension — the easiest leads to acquire are the least committed. Moving to qualified-lead ratio and lead-to-deposit rate is the change that makes every later decision point the right way.

That required measuring the funnel end to end, through lead, qualification and deposit, so acquisition decisions could be made on where someone ended up rather than where they entered. Until that link exists, lead quality is an argument between media and sales rather than an observation.

Improving qualified ratio is largely a filtering problem: what the ad promises, who the messaging attracts and repels, and what the funnel asks before someone becomes a lead. I set the direction for that filtering and supervised how it was applied across markets.

The same discipline carried through to the deposit stage, where the question stops being whether someone is interested and becomes whether the path in front of them is clear enough to finish.

No dashboard screenshots on this page. Several of these accounts have since closed or are under NDA, and publishing an export we cannot stand behind would be worth less than the reasoning above.

Also worked with

Engagements where we have not published figures, either because the work is ongoing or because the client has not approved the numbers.

SnowPaid social management
Uni Team MarketingPerformance marketing partnership
Evolvo TechnologiesMedia buying and campaign management
AZTALDigital marketing support
Quantum QuestCampaign strategy and management

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