How we gave a DTC brand back 14 hours a week of order reconciliation
A growing Shopify brand was reconciling orders, payouts, and 3PL data by hand every morning. We automated it in eight days and verified $48,000 in Year-1 savings.
- Verified Year-1 savings
- $48,000
- Hours saved / week
- 14 hrs
- Build time
- 8 days
- Reconciliation errors
- −96%
Note: this case study is anonymized at the client’s request. The numbers are real and verified against the agreed baseline; the brand name is withheld until we have permission to publish it.
The morning that ate two hours
Every weekday, an operations lead at a mid-seven-figure DTC brand opened four tabs: Shopify orders, the payment processor’s payouts, the 3PL’s fulfillment export, and a master spreadsheet. Her job was to make them agree. Which orders actually shipped? Which payouts matched which batch of orders? Where did the numbers drift, and why?
It took about two hours a day. On a bad day (a chargeback wave, a 3PL export with shifted columns, a promo that broke the SKU mapping) it took the whole morning. And because she was the only one who fully understood the spreadsheet, the work couldn’t really be handed off.
This is the quiet kind of cost. Nobody puts “manual reconciliation” on a budget line. It just sits inside a salary, invisible, until you measure it.
What it was actually costing
In the Savings Audit, we measured the four line items we always measure:
| Line item | Before |
|---|---|
| Labor | ~14 hrs/week × loaded rate, one senior ops person |
| Tools | A reconciliation add-on they barely used |
| Errors & rework | Mis-matched payouts caught late, two written-off discrepancies/quarter |
| Opportunity | Ops lead pulled off retention projects to babysit a spreadsheet |
The labor alone, measured honestly at a loaded rate rather than base salary, was the bulk of it. The agreed baseline came to roughly $48,000/year for this one process.
Why we scope narrow first. We could have proposed a sprawling “ops automation platform.” Instead we picked the single most repetitive, most costly, least-changing task. Narrow scope means a working system in days and a savings number that’s easy to verify, which is the whole point of the model.
What we built
We built a reconciliation pipeline that runs on the brand’s own accounts, no data left systems they didn’t already control:
- A scheduled pull of orders, payouts, and the 3PL export every morning before the team logs on.
- A matching engine that pairs orders to payouts and fulfillment records, tolerant of the messy real-world cases (partial refunds, split shipments, currency rounding) that broke the manual process.
- An exceptions queue: instead of reconciling everything by hand, the ops lead now reviews only the handful of records the system couldn’t match confidently, with the reason attached.
- A clean daily summary posted to Slack: matched totals, flagged exceptions, and any payout drift, ready before the first coffee.
The first version was live on real data in eight days. The team watched it run alongside the manual process for a week before trusting it, exactly as it should be.
How we proved the savings
After four weeks, we re-measured the same line items. The two hours a day became roughly ten minutes of reviewing the exceptions queue. Reconciliation errors that used to slip through dropped by 96%, because the machine doesn’t get bored at 8:45am. We didn’t estimate any of this, we counted it, against the baseline both sides had signed.
Projected Year-1 savings: $48,000, agreed before the build. Our fee was half of that, paid on delivery, and the verified result held the number, so no refund was due. The brand kept the larger share in year one and keeps roughly 90% of it every year after.
The reusable part
Order-payout-fulfillment reconciliation looks different at every store, but the shape is the same everywhere. That matching engine is now a pattern we can adapt for the next Shopify brand in a fraction of the time, which means a faster build and a cleaner estimate for them, and a sharper benchmark for us. Proof, a template, and a data point: the three things every See.ke project is supposed to leave behind.
If your team starts the day making two systems agree, that’s a candidate. Book a free Savings Audit and we’ll measure what it’s really costing you. You keep the number either way.