Context
Maison Liora is a French women's ready-to-wear boutique launched on Shopify in 2021. With around 180 SKUs and an average order value of 72 euros, the brand built its growth on social media and Meta advertising. In 2024, it generates about 85,000 euros in monthly revenue but struggles to deliver stable profit.
The founding team, two marketing co-founders, manages Meta Ads daily from Business Manager. They rely on Meta's reported ROAS and Shopify reports to make budget decisions. Indicators look healthy on paper: Meta ROAS at 2.8, conversion rate at 2.4%, rising AOV.
The challenge
Despite seemingly healthy numbers, cash flow is tight. Every month, Maison Liora spends more on ads without seeing net profit grow. The team suspects an attribution problem but cannot quantify it.
The diagnosis reveals three major blind spots. First, Meta's reported ROAS includes assisted conversions and ignores product returns (14% return rate in fashion). Second, gross margin varies widely across collections: some best-sellers have 68% margin, others just 22%. Finally, scaled campaigns push low-margin products, eroding global margin invisibly.
The Fullmetrix solution
Maison Liora connects its Shopify store and Meta ad accounts to Fullmetrix in 15 minutes. COGS are imported from Shopify then manually corrected for the top 30 products. Transaction, shipping and return fees are configured.
Within a week, the team has a POAS dashboard per campaign revealing the economic truth of every euro invested. Three seemingly profitable campaigns (ROAS 3.2) are actually loss-making (POAS 0.7) because they push low-margin products with high returns.
The team switches from ROAS to POAS as its north star and creates custom audiences synced to Meta from RFM segments: high-value repeat buyers, premium cart abandoners, VIP-based lookalikes.