How Clean Origin Used Data to Double Regional Sales, Optimize 52,000 SKUs, and Exit Successfully
A conversation with Jeb Beckwith, former CFO of Clean Origin, on the business results of building a modern data infrastructure.
Clean Origin was a company on a mission — to change the diamond industry. Founded in 2017, they sold lab-grown diamonds that were chemically identical to mined stones, but produced ethically and sustainably. By the time they were acquired, lab-grown diamonds had shifted from a novelty to a force that reshaped the market.
But between the mission and the exit was a data problem that nearly held them back.
We sat down with Jeb Beckwith, Clean Origin’s former CFO, to talk about what that problem looked like, how they solved it, and what the business got out of it.
”We Were Drowning”
When Jeb joined Clean Origin in 2021, he brought decades of financial leadership experience, including running global financial operations at Royal Bank of Canada. He knew immediately what the company was up against.
“You’ve got an operation here with 52,000 SKUs,” Jeb recalls telling the CEO. “Each one of those is going to have a different gross margin. We need to have marketing associated with each one. We’ve got to get better about our data, our data integrity, our data manipulation.”
The challenge wasn’t just volume — it was complexity. Every diamond is unique: different cuts, clarity grades, carat sizes, and settings. The combinations created a product catalog where no two items were alike, and each needed its own margin profile.
On top of that, the data was everywhere. Sales flowed through an e-commerce platform. Marketing spend lived in separate tools. Accounting sat in its own system. Customer service data, product feeds from diamond vendors, geographic data, promotion tracking — over 20 data sources in total, none of them talking to each other.
“We were drowning,” Jeb says.
Building the Foundation
Cloud Data Consulting was brought in to do what Clean Origin couldn’t do alone: unify all of it.
Using Fivetran, we connected every data source — e-commerce, point-of-sale systems, accounting, loyalty programs, customer service platforms, ad spend, and a constellation of supplementary datasets — into a Snowflake data warehouse. A semantic layer built with dbt transformed the raw data into business-ready datasets. Looker provided self-service dashboards that the business team could use without writing SQL.
“Cloud Data Consulting was tremendous,” Jeb says. “We wouldn’t have been able to do it without you.”
The result: report generation time dropped by 85%. Insights that used to take days now took minutes. And the CFO’s team was running analytics and creating ad hoc reports every single day — without needing to call an engineer.
The Dallas Experiment: Proving Omnichannel with Data
Clean Origin started as an online-only retailer. The question of whether to open physical stores was a major strategic bet — the kind of decision that can make or break a growth-stage company.
Before the data warehouse, the team had only anecdotal evidence that omnichannel might work. After it was up and running, they had ZIP code-level sales data across the entire United States.
They opened their first store in Frisco, Texas — in the middle of COVID — as a deliberate experiment. The data would tell them whether it worked.
It did more than work.
“We were basically doubling the sales in that region that we otherwise would have had,” Jeb explains. At roughly 50% gross margin, the incremental revenue more than covered the cost of the store. And because the physical location served as a marketing venue itself — driving foot traffic and brand awareness — marketing costs in the region actually went down.
That single data-backed experiment gave Clean Origin the confidence to expand to five more locations: Tysons Corner outside DC, Woodfield Mall near Chicago, Eastgate Mall near Cincinnati, and two stores in the Houston area. Each expansion decision was grounded in the same geographic and financial analytics.
Knowing Your Margins at the SKU Level
For most retailers, margin analysis happens at the category level — maybe the product line level if they’re sophisticated. Clean Origin needed it at the individual SKU level, across 52,000 products.
The data infrastructure made that possible. The team could look at gross sales before discounts and returns, net sales after returns, gross profit, and gross margin for each individual sale. They could slice it by geography, customer type, channel, and time period.
“The data that we were able to pull into Looker was something we used every single day,” Jeb says. “It enabled us to look at trends and to do pricing and repricing every single day.”
That granularity changed how Clean Origin spent its marketing dollars — the company’s largest cost after the diamonds themselves. Instead of broad campaigns, they could target spend against the products with the highest gross margins. Instead of guessing which markets to invest in, they had data down to the ZIP code.
Understanding the Customer Journey
The data revealed something the team hadn’t fully appreciated: who was actually shopping, and how.
In the traditional diamond business, roughly 90% of purchases are made by him. But Clean Origin’s data showed that 85% of the browsing on their site was done by her. And in the high-end physical stores, a significant share of purchases were women buying for themselves — not the traditional bridal scenario at all.
This insight reshaped how Clean Origin thought about customer lifetime value. The initial bridal sale was just the beginning. If they could make that customer comfortable with the brand and the product, every subsequent purchase — an anniversary gift, a birthday gift, something for herself — came at almost zero acquisition cost.
“The customer lifetime value becomes critically important,” Jeb explains. “Marketing — the biggest cost of customer acquisition — is in that upfront marketing, getting a customer that doesn’t know us to come onto our platform. If we can get that customer into our database, comfortable with us, then the customer acquisition cost goes down to almost nothing.”
Built to Last, Not to Babysit
One concern companies often have about data infrastructure projects: will we be dependent on consultants forever?
Clean Origin’s experience says no.
“The maintenance on it was really low,” Jeb says. “The usefulness of it was very good. We could create our own queries really pretty easily.”
The engagement followed a natural arc — heavy lifting up front to build the infrastructure and get the mapping right across all those data sources, then a lighter maintenance phase once the system was running. When issues did come up — like a diamond vendor changing their data feed format — the Cloud Data team resolved them quickly.
“When that happened, you guys would be on it. We’d see it pretty quickly, flag it, and have it corrected within a very short amount of time.”
Jeb also noted the flexibility of the working relationship: direct phone access when needed, Jira for priority alignment, Slack for day-to-day communication, and costs he described as reasonable.
The Numbers
| Metric | Result |
|---|---|
| Data sources unified | 20+ |
| SKUs with individual margin tracking | 52,000 |
| Regional sales lift from first store | 2x |
| Report generation time reduction | 85% |
| Inventory turnover improvement | 32% |
| Analytics usage | Daily, self-service |
| Physical stores informed by data | 6 |
From Startup to Acquisition
Clean Origin was ultimately acquired — the kind of outcome every growth-stage company aims for. While the data infrastructure wasn’t the only factor, having clean, unified, well-documented analytics in place reduces due diligence friction and demonstrates operational maturity to acquirers.
For Jeb, the experience validated a broader thesis. He’s now building Cooper Beck, a consulting firm that helps small and medium-sized businesses get the same kind of operational clarity that Clean Origin achieved.
“Most entrepreneurs want to focus on their business,” Jeb says. “They don’t want to know about finance or HR until the end of the month — then they want to know how much money they made and where the cash is. They want to know the answers. They don’t necessarily want to get involved in the cooking.”
Is Your Data Holding You Back?
If Clean Origin’s story sounds familiar — fragmented systems, gut-feel decisions, marketing spend you can’t trace to results — Cloud Data Consulting can help.
We build modern data infrastructure that turns scattered data into daily decision-making tools. From data warehouse to self-service analytics, we handle the heavy lifting so you can focus on your business.
Contact Cloud Data Consulting →
Cloud Data Consulting specializes in data infrastructure for growing businesses. We work with Snowflake, dbt, Fivetran, Looker, and the modern data stack to deliver analytics that drive real business results.