Paid Acquisition Results — Meta, Google, TikTok & LinkedIn

5.8x ROAS · 77% CPI reduction · $380K pipeline in 75 days · 2.75x spend scaled · $172K revenue attributed · 31% SQL rate · 5.8x ROAS · 77% CPI reduction · $380K pipeline in 75 days · 2.75x spend scaled · $172K revenue attributed · 31% SQL rate ·

documented results, 2023–2025

Case studies

documented results.

01 — DTC Beauty

5.8x

ROAS

from 1.4x · 90 days

Rebuilt creative around UGC and collapsed six loose audiences into three intent-based segments. CPA dropped 72% as spend scaled 2.75x in 90 days.

ROAS

5.8x

was 1.4x

CPA

$18

was $64 · ↓72%

Monthly spend

$22K

scaled from $8K

Revenue, 90 days

$127K

attributed

ROAS — 12 weeks

CPA decline

we'd burned through two agencies before this. nothing came close to what they built in the first 45 days — the creative testing alone changed how we think about paid.

— Founder, DTC apparel brand

02 — Consumer Wellness

70%

CPA reduction

1.9x → 4.3x ROAS · 60 days

Introduced a 15-variant-per-week UGC testing system and rebuilt the Shopping feed with clean attribution. CPA dropped 70% — then spend scaled from $15K to $40K/mo.

ROAS

4.3x

was 1.9x

Monthly spend

$40K

scaled from $15K

Revenue, 60 days

$172K

attributed

CPA reduction

70%

week 1 → week 8

ROAS — 8 weeks

CPA — 8 weeks

the creative testing framework they built is something we still use a year later. they didn't just run ads — they taught us a system.

— CMO, health & supplement brand

03 — Consumer App

$1.40

Cost per install

from $6.20 · ↓77% · 45 days

Built the full paid stack from scratch — offer hierarchy, TikTok-native creative, and audience layering. Hit lowest-ever CPI in week 3, doubled budget in week 6 with efficiency holding. ROAS climbed from 2.1x to 6.2x over the run.

ROAS

6.2x

was 2.1x

Cost per install

$1.40

was $6.20 · ↓77%

Monthly spend

$18K

scaled from $5K

Revenue, 45 days

$111K

attributed

Cost per install — 6 weeks (from $6.20 to $1.40)

week 3 we hit the lowest CPI we've ever seen. week 6 we doubled spend and it held. that's when you know the system works.

— Head of Growth, mobile subscription app

01 — B2B SaaS

$380K

Pipeline generated

from $0 · 75 days

Built a LinkedIn + Meta funnel from zero — job-title targeting layered with firmographic signals, stage-matched creative, and HubSpot-synced nurture. $380K in qualified pipeline in 75 days.

Pipeline generated

$380K

in 75 days

Cost per lead

$42

was $210 · ↓80%

Demo qualified rate

38%

was 11%

Monthly spend

$14K

scaled from $3K

Attributed pipeline — 10 weeks (spend vs. revenue)

for the first time, paid was generating pipeline we could actually close — not just form fills. the quality of lead changed everything.

— VP Marketing, mid-market SaaS

02 — B2B Fintech

31%

SQL rate

from 8% · CPL down 64% · 8 weeks

Rebuilt targeting around CFO-specific firmographic signals and rewrote every creative for that buyer's ROI lens. Same $20K budget — SQL rate went from 8% to 31%.

CPL

$162

→

$58

↓ 64%

SQL rate

→

31%

nearly 4x lift

Closed revenue

$240K

attributed in 90d

Monthly spend

$20K

same budget, 3x return

SQL rate — 8 weeks

CPL decline — 8 weeks

same budget, completely different lead quality. the CFO-focused creative work they did changed how our entire sales team approached demos.

— Head of Growth, fintech platform

The methodology

the creative system other agencies don't build — and why it compounds.

most agencies run ads. we engineer creative systems. every piece of creative has a written hypothesis, a test structure, and a scaling plan before it launches. the result: a compounding advantage that gets harder to beat the longer we run.

Hypothesis-driven creative

every ad starts with a written hypothesis: target persona, psychological trigger, success metric. example: "wellness buyers aged 35–50 respond to authority positioning (doctor endorsement) → success = sub-$30 CPA." we test with purpose, not guesses.

Intent-signal audience layering

we build audiences around behaviors and intent signals, not demographics. this means ads hit people when they're already considering a purchase — not just matching a profile. example: retarget cart abandoners who viewed pricing 2x in 7 days with an urgency-based offer.

Offer engineering before scale

the ad is rarely the problem — the offer is. we test offer structures (discount vs. free shipping vs. bundle), risk reversals (money-back guarantee positioning), and urgency mechanics before scaling spend. a 2x better offer beats a 2x bigger budget every time.

High-velocity testing cadence

we run 12–20 new creative variants per week across most accounts. faster test cycles = find winners sooner, kill losers before they drain budget, scale with data instead of guesswork. most agencies test 3–5 variants per month. we do that in 2 days.

let's see what you're leaving on the table.