Industry: E-commerce, women’s intimates · Channels: Shopify, Amazon, paid social, CTV · Engagement: ~4 years
- $7.2M → $13.8M gross revenue over roughly four years
- $44M acquisition, reports went straight into due diligence
- $250K recovered from Amazon up front, then $30–50K every quarter
The problem
Glamorise had been selling bras for a century, first through Sears catalogs, then as a growing e-commerce brand across Shopify, Amazon, and paid social. A real business with real revenue, except no one in the executive suite could agree on what the numbers actually said.
Two executives could ask the same question and get different answers, with no way to trace why. Assumptions were buried across dozens of spreadsheets. The data analyst held a monopoly on truth, able to pull whatever numbers made any answer look right. And something as basic as “what was our ROAS last month” took over a month to compile by hand.
They’d ask for the same thing from two different people, and the numbers would not match. And no one could answer why.
The reporting chaos masked something worse: Glamorise was spending millions across Facebook, Google, Amazon, and CTV with no reliable way to know if any of it was working. Every platform claimed full credit for every sale.
The attribution trap
Facebook reported 100 orders. Google reported 100 orders. Klaviyo reported 100 orders. Add them up, and it looked like 700 total conversions, when the real number was closer to 100. Every channel was taking credit for the same customer.
This blind spot cost them. The team shifted 90% of their Google budget to Facebook, assuming Google was underperforming at 1x ROAS versus Facebook’s apparent 3x. Instead of a lift, they lost 30% of sales. Google had been the first touchpoint, how new customers found them. Without it, Facebook had nobody to retarget.
The process
Before building anything, the data was made real. The work started at the foundation: pulling every data source cleanly into one place. Shopify, ERPs, Facebook, Google, Amazon. Every report tied back to actual orders placed and actual money in the bank, not what the ad platforms claimed.
Server-side tracking was rebuilt from scratch. Campaign naming and UTMs were restructured so full user journeys could be stitched together across two years of visits, from first touch to purchase.
On Amazon, where financial reporting is deliberately opaque, every remittance, commission, and sales order was pulled and reconciled by hand. The numbers didn’t line up. Glamorise was owed money they didn’t know existed.
Amazon reconciliation
By pulling all financial report data from Vendor Central and tying it together, significant discrepancies surfaced between what Amazon reported and what Glamorise was actually owed. The result was a $250,000 recovery check and an automated quarterly reconciliation system that now recovers $30,000–$50,000 every quarter.
Reliable data revealed something fundamental: Glamorise was running two different businesses. New customer acquisition, where the goal is to break even, get women into the product, and bet on lifetime value. And returning customer revenue, where profit actually lives. Once those two were separated, every spending decision got simpler.
The transformation
From a data analyst’s monopoly to a system anyone could trust. The new reporting broke down every day, style, product, and dollar, traceable all the way back to raw platform exports. Every number verifiable. Leadership could check a single dashboard once a day and trust what it said.
On Amazon, keyword bids were automated daily against profitability targets. The team set ROAS or TACOS goals by style, campaign, or globally, and watched performance climb toward those targets over a few days without touching it.
When the data analyst eventually left, there was no scramble. The systems ran without them. That salary, plus what had been going to outside agencies, went into maintaining and expanding the infrastructure instead.
They were able to become a tech company without a single tech employee.
The results
Nearly doubled the business. Then sold it for $44 million.
- $7.2M → $13.8M gross revenue over roughly four years
- $44M acquisition price
- $250K recovered from Amazon, then $30–50K every quarter on autopilot
- ~40% of revenue from returning customers, the profit engine
- 10 people on the team at acquisition, no ad agency, no data team
This growth came through COVID, rising tariffs, and climbing acquisition costs. Headwinds that would have stalled a business still running on intuition and manual spreadsheets. Instead, Glamorise had compounding cohorts of returning customers stacking year over year, clear visibility into which channels to scale and which to cut, and infrastructure that made the whole operation legible to a buyer.
When the acquisition came, the reports went straight into due diligence. Everything had increased; everything was verifiable. The acquirer, a 200-person operation running five global brands at $200M a year, asked where the ad agency was. Where the data team was. There wasn’t one. Now they’re implementing the same systems across their entire portfolio.
It was the difference between pouring gas on a house fire or into an F1 car.