The $4.76M brand whose ad platform was overcounting by 66%.
A $4.76M e-commerce brand shipping 42,000+ orders a year was spending $176K annually on Meta ads, with no way to tell if that spend drove sales or just claimed credit for revenue that would have happened anyway.
Six-figure decisions on data with a conflict of interest.
Revenue was growing but profitability wasn’t keeping pace, and the founder had three questions no one could answer. Is Meta actually driving our sales, or just taking credit? Where is revenue really coming from? Why do most customers buy once and never come back?
This came after building the brand’s full ops infrastructure: AI-powered customer service, automated reporting, warehouse KPIs, and cross-country compliance. The attribution system was the layer that made the numbers honest.
- One source of truth — Shopify order data, Meta Ads metrics, and email platform data connected into a single pipeline
- Platform-agnostic — actual purchase behavior cross-referenced against ad exposure, email engagement, and organic traffic
- Not a dashboard redesign — a new data pipeline that bypassed every platform’s self-serving reporting
66%
conversion overcounting by Meta. Attribution-window inflation claimed credit for purchases driven by email, organic search, and direct traffic. Six figures misallocated.
48%
of revenue came from organic and direct channels. Nearly half the business was growing itself while the ad budget pretended otherwise.
$1M+
in untapped revenue sitting in the existing customer base: repeat buyers, lapsed customers, and high-LTV segments no campaign was reaching.
The founder went from budget decisions on gut feel and platform vanity metrics to a picture she could trust. Budget moved away from overcounted channels, campaigns launched into the $1M+ segments, and email went from afterthought to primary revenue channel. Better data, better decisions, and better decisions compound.
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