Case Studies

Measurement Clarity
in Practice.

Real results from real brands. Names withheld by client request — results verified by our measurement infrastructure.

DTC Apparel Brand

$45M Annual Revenue

Challenge

Attribution Chaos Across 5 Channels

28% Capital Efficiency Gain in 90 Days

The Problem

A $45M DTC apparel brand was running $6M/year across Google, Meta, Pinterest, TikTok, and email. Each platform reported different attribution for the same sales, making budget decisions impossible to defend. Their MER was declining quarter-over-quarter with no clear explanation.

Our Solution

We deployed a full Measurement Architecture in 10 weeks: server-side tagging, BigQuery pipeline, channel normalization, and an MTA model that reconciled platform data against actual revenue. We identified that Meta was over-reporting conversions by 52% and Google by 38%.

Results

+28%
Capital efficiency improvement
$1.7M
Annual budget reallocation identified
52%
Meta over-reporting discovered
10wk
Full deployment timeline

PE-Backed Home Goods

$78M Annual Revenue

Challenge

Investor Reporting Credibility

$6M Budget Reallocation Identified

The Problem

A PE-backed home goods brand needed investor-grade reporting for their quarterly board meetings. Their CMO was presenting platform-reported ROAS numbers that the PE firm's data team was increasingly skeptical of. The brand had no way to reconcile their MER against channel-level attribution.

Our Solution

We built a board-ready reporting infrastructure on top of their existing BigQuery environment, added incrementality testing for their top three channels, and created a blended ROAS dashboard that reconciled platform data against Shopify revenue and offline conversions.

Results

$6M
Budget reallocation identified
Board
Investor-grade reporting delivered
3x
Reporting confidence improvement
Q1
First board presentation with clean data

9-Figure Retail Brand

$120M Annual Revenue

Challenge

Meta Over-Investment Discovery

40% Meta Spend Reallocation

The Problem

A $120M retail brand was allocating 60% of their $15M digital budget to Meta based on platform-reported ROAS of 4.2x. Their in-house team suspected the numbers were inflated but had no methodology to prove it or quantify the over-investment.

Our Solution

We ran a 12-week incrementality testing program using geo-holdout methodology across their top 10 markets. The results showed Meta's true incremental ROAS was 1.8x — less than half of what the platform reported. We built a reallocation model that shifted $6M toward Google Shopping and Retail Media.

Results

40%
Meta budget reallocation
1.8x
True incremental ROAS (vs. 4.2x reported)
$6M
Shifted to higher-incrementality channels
35%
Overall capital efficiency improvement

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