Scaling spend wasn't scaling new customers. We restructured the Meta account for incremental new customer acquisition and rebuilt the offer architecture to improve profit per order. The economics flipped.

The Problem

Bored Brows came to us after their Australian launch exposed a structural crack in their economics.

New Zealand: Profitable at $49 nCAC

Australia: Losing $44 per first order at $129 nCAC

They couldn't afford to scale Australia. But they also couldn't afford to kill it.

When we ran the full P&L and unit economics breakdown, we found the real issue wasn't geography - it was the entire economic model:

• CM2 per order: $92.90

• Gross Profit per Visitor: $1.41

• Blended nCAC: $49 (October spiking to $65)

• AOV: $120

• Units per Transaction: 1.57

• Contribution Profit on first purchase: $45

The business looked healthy on paper. Strong gross margins. Healthy conversion rates. Acceptable blended CAC.

But the cash flow told a different story:

• 85% of revenue from a single hero SKU

• Too many customers buying 2-3 units upfront

• 5-6 month depletion cycle before repurchase

• Weak cross-sell adoption post-purchase

• Cash locked in slow-moving inventory

The Diagnosis

We ran our analysis on Aug-Oct 2025 (pre-sale period, to avoid inflated holiday data).

Three structural issues:

1. Ad Account Structure Was Burning Money

• Traffic campaigns driving low-quality visitors

• No audience exclusions—Meta was retargeting customers who'd purchased in the last 30 days

• Blended campaigns mixing new and returning customers

• No marginal CAC modeling

• No suppression beyond 180 days

Result: Paying acquisition costs for people who would've bought anyway.

2. Pricing & Offer Architecture Optimised for AOV, Not Profit or Inventory

• Bundle discount was overly aggressive

• Complementary product under 9% revenue share

• No quantity break incentives

• Upsells pushing the same hero SKU instead of moving excess inventory

Result: High AOV, but low profit capture and slow inventory turns.

3. Retention System Treated All Customers the Same

• Generic post-purchase flow

• No segmentation by purchase quantity

• Subscription pushed too early in lifecycle

• No consumption-based replenishment timing

Result: Long depletion cycles, weak repurchase rates, low LTV velocity.

The Strategy

We didn't increase ad spend. We re-engineered the system.

Phase 1: Account Structure Optimisation

Goal: Stop paying to acquire customers who were already coming back.

• Paused non-sales objective campaigns

• Reduced controlling audience signals to let Meta optimize

• Implemented 180-day purchase exclusions

• Restructured retargeting to focus on hot audiences only (IC + ATC 7 days)

• Let cold prospecting campaigns handle warm nurture

• Partnered with their ad agency to fill top-of-funnel and bottom-of-funnel creative gaps

Results:

nCAC dropped from $49 → $36.60 (-25%)

Phase 2: Pricing & Offer Architecture

Goal: Capture more gross profit per session and accelerate inventory turns.

• Tested hero SKU base price increase

• Tested 2 quantity break bundle variations and set best performer as default selection

• Rebuilt upsell offers to push lower-priced excess stock (avoiding discounts on fast-moving, high-margin SKUs)

Results:

• Units per Transaction: 1.57 → 1.90

• AOV: $120 → $138

• Gross Profit per Order: $93 → $103

Phase 3: Retention System Rebuild

Goal: Compress the repurchase cycle and improve consumption velocity.

Post-Purchase Flow:

Split by new vs. returning customers. Added educational content and habit-building nudges to drive faster consumption.

Replenishment Flows:

Segmented by purchase quantity. Timed replenishment emails based on product depletion (not arbitrary 30/60/90-day triggers).

Signup Flow:

Delayed discount offers. Led with education, social proof, and pain points to build urgency without training discount dependency.

Results:

• Email-attributed revenue: +161%

• Returning customer rate: +210%

The Results

The unlock: Contribution profit grew 302% - faster than revenue (+250%) and much faster than ad spend (+160%).

Scaling became structurally safer.

What This Means

Most agencies would've told them to "spend more on ads."

We showed them the real constraint was how they were capturing value - not how much traffic they were buying.

By re-engineering pricing, ad structure, and retention in parallel, we didn't just grow revenue. We accelerated cash recovery, compressed payback, and freed up working capital to scale further.

The result?

They went from capital-constrained to confidently scaling at 2.79 POAS.