Every DTC brand past a certain size ends up with the same ritual. Monday morning someone opens four tabs, exports four CSVs, pastes them into a master spreadsheet, and sends a summary to the team. By Tuesday the numbers are stale. By Friday nobody trusts them.
The Operations Problem for DTC Operators
Your numbers live in at least four places. Shopify holds orders and revenue. The 3PL portal holds on-hand inventory and in-transit stock. Meta Ads and Google Ads hold spend and ROAS. Klaviyo holds email revenue attribution.
None of these systems agree on the number. Shopify says you sold 420 units. The 3PL says they shipped 388. The ad platform says it drove 210 orders. The founder asks what yesterday's contribution margin was and nobody answers cleanly.
So the team builds a spreadsheet. And the spreadsheet starts drifting the day it is built.
The System We Build
The goal is one dashboard, refreshed daily, that every person on the team looks at and agrees on.
Data layer. We pull every source into a single data store. Usually Supabase, sometimes BigQuery if volume is high. Shopify orders, refunds, products. 3PL inventory and shipment logs. Ad platforms via direct API. Klaviyo events. Everything lands in raw tables first, then gets transformed into clean tables the dashboard reads from.
Inventory truth. On-hand by SKU by warehouse, refreshed every few hours. In-transit by PO, with expected arrival dates. Sell-through rate by SKU over a rolling window. Reorder trigger when days of supply falls below a threshold.
Revenue and margin. Gross revenue from Shopify. Discounts, refunds, shipping collected, taxes. COGS per SKU pulled from a product master table. Shipping cost from the 3PL. Payment processing fees from Shopify Payments or Stripe. The dashboard shows contribution margin per order, per SKU, and per day, not just top-line revenue.
Ads and attribution. Spend by platform. ROAS on platform numbers, then ROAS blended with Shopify orders so you see the gap. MER (media efficiency ratio) as the headline number. First-order versus repeat-order split so you know whether ads are buying new customers or subsidizing returning ones.
Dashboard layer. Looker Studio, Retool, Hex, or a Supabase-powered internal tool depending on what your team uses. The dashboard reads from clean tables, not raw platform APIs, so it loads fast and does not break when a platform changes its schema.
What Changes After
The Monday spreadsheet ritual goes away. The team stops arguing about which number is correct. Decisions get made faster because yesterday's data is ready by 9am.
Inventory planning gets tighter. You stop over-ordering slow movers and under-ordering fast ones.
The ads team makes better decisions because they see blended performance, not platform performance.
The founder stops asking for updates. That is the real tell.
Common Objections
"Triple Whale already does this." Triple Whale is good at ads and attribution. It is weak on the inventory and 3PL side, and does not let you define COGS or contribution margin with the nuance most brands need. We often build on top of it, not around it.
"We do not have the data volume to justify a warehouse." A Supabase project costs very little to run and handles most DTC brands without complaint.
"Our team will not use a new dashboard." They will if it replaces work they already do. The dashboard wins the moment it kills the spreadsheet.
When This Makes Sense
Worth building once you are running ads on more than one platform and managing more than a handful of SKUs. Below that, a clean Shopify dashboard and a tight Google Sheet will carry you.
It does not make sense if your data inputs are still messy. Fix the inputs before building the dashboard.