Managed analytics for growing Shopify brands
Numbers don't match. Profitability becomes guesswork.
Know which campaigns, products, and customers drive profitable growth using numbers your team can trust.
Built for $1–4M Shopify brands running paid ads.
01 - Build
Three steps,
one source of truth.
Ingestion
Source data synced into BigQuery daily. Schema changes are caught before a report breaks.
Modeling
Joins, revenue rules, and attribution defined once, then validated.
Reporting
Dashboards built from trusted business models, so reports use the same underlying definitions.
Managed end to end - monitoring, schema fixes, scheduled refreshes, and support whenever a number looks wrong.
02 - Model
Revenue verified
from source to dashboard.
Key transformations, totals, and reporting rules are checked so the final report follows the same agreed business definition.
One revenue total. Multiple attribution views.
Platform pixels report conversions independently. Each platform may claim credit for the same order, so their totals may exceed actual revenue. Shopify remains the revenue source of truth. UTM data is one attribution input with explicit fallback rules, so this split reconciles to the revenue above.
03 - Verify
Verified daily,
time saved monthly.
Reconciliation Variance
What's measured and the threshold that triggers a flag - not a customer result.
Where Reclaimed Time Comes From
Illustrative task categories - not a measured customer result.
These are example monitoring views showing what's tracked and alerted on, not results from a specific customer. Ask what this would track for you →
04 - Team
One team, start to finish.
Data engineering background, built specifically for ecommerce analytics on Shopify.
We scope, build, validate, and manage every system in-house.
Daily monitoring, source-schema fixes, direct support when a number looks wrong.
Python, BigQuery, and Data Studio. Hours cover US + EU teams.
05 - Example
See how one disputed metric gets diagnosed.
This is a sample audit trail showing how a disputed number is traced, explained, and documented into a clear next step.
Meta Ads Manager reported $91,540 in attributed revenue for the week of Mar 10. The BigQuery data warehouse attributed $79,140 to Meta using Shopify orders and first-party UTM data. The team did not know which figure to use.
The warehouse included $1,200 from orders that were later refunded or cancelled. Meta also used a different attribution window from the warehouse's one-day UTM rule, which had not been formally documented.
The audit identified that refunded and cancelled orders should be excluded and that the one-day UTM rule should be formalized as the warehouse's documented attribution standard: $79,140 − $1,200 = $77,940.
The discrepancy, likely causes, recommended calculation, and implementation requirements were documented. The underlying model and dashboard would be corrected separately as part of the implementation.
06 - Consult
Bring your worst number.
A paid, 60-minute session on one disputed metric. We identify the likely cause and scope what it would take to fix.
Book the $100 diagnosticNot sure yet? Start with a free 15-minute call.
✓Shopify is your commercial source of truth.
✓Meta and Google Ads both influence acquisition.
✓Reports get rebuilt, reconciled, or challenged manually today.
✓You need managed infrastructure, not another plug-and-play chart.
✗You just need a simple out-of-the-box dashboard connector.
✗Attribution and reconciliation aren't a live problem for you.
GODIN_DATA python · bigquery · data studio remote · us + eu Partners →