TL;DR A unified ecommerce dashboard pulls Shopify, GA4, Google Ads, and Meta Ads into one view using Fivetran → BigQuery → Looker Studio. It takes 2-3 weeks to build properly, or we can do it for you.
The Problem: Your Data Lives in 5 Different Platforms
You open Shopify to check revenue. Switch to GA4 for traffic. Jump to Google Ads for ROAS. Then Meta Ads Manager for creative performance. Maybe a spreadsheet to pull it all together.
By the time you’ve got the full picture, it’s Thursday. The decisions you needed to make on Monday are already stale.
This isn’t a minor inconvenience. It’s a structural problem. When data lives in silos, you can’t answer the questions that actually matter: Which channel drives profitable customers? What’s my true blended CAC? Where should I shift budget this week?
Why “Just Connect Everything to Looker Studio” Doesn’t Work
The naive approach is plugging each platform directly into Looker Studio via connectors. It looks easy. It isn’t.
Here’s what goes wrong:
- Different attribution models. GA4 uses data-driven attribution. Google Ads uses last-click on its own ads. Meta claims credit for everything within 7 days. You can’t just sum them.
- Mismatched time zones and currencies. Shopify might be in UTC, GA4 in your local timezone, ads platforms in the account timezone.
- Rate limits and connector fragility. Direct connectors hit API limits, break on schema changes, and slow down on large date ranges.
- No single customer ID. Without a join key, you can’t connect ad spend to actual purchase behaviour.
You need a data layer in between. That’s where the warehouse comes in.
The Architecture That Works
The proven stack for ecommerce analytics:
| Layer | Tool | Role |
|---|---|---|
| Extraction | Fivetran | Pulls data from all sources on schedule |
| Storage | BigQuery | Central warehouse, cheap and fast |
| Transformation | dbt or SQL views | Cleans, joins, and models the data |
| Visualisation | Looker Studio | Dashboards and reports |
This isn’t over-engineering. It’s the same architecture that 8-figure DTC brands use. The difference is you can set it up without a data team.
Step 1: Extract with Fivetran
Fivetran connects to Shopify, GA4, Google Ads, and Meta Ads out of the box. It handles schema changes, API versioning, and incremental syncs automatically.
Set up takes about 30 minutes per connector. Data lands in BigQuery in normalised tables — orders, line items, sessions, ad spend, campaigns.
Step 2: Store in BigQuery
BigQuery is Google’s serverless data warehouse. You pay per query and per storage byte. For most ecommerce brands doing under 50M in revenue, you’re looking at $20-100/month.
The key advantage: it handles billions of rows without blinking. No performance tuning needed.
Step 3: Transform with SQL
This is where the magic happens. You write SQL models that:
- Unify order revenue with ad spend by date and channel
- Apply a consistent attribution model across sources
- Calculate blended metrics (CAC, ROAS, contribution margin)
- Reconcile timezone and currency differences
A typical transformation layer has 10-15 models. It takes 1-2 weeks to build properly.
Step 4: Visualise in Looker Studio
Looker Studio connects natively to BigQuery. Dashboards are fast because they query pre-modelled tables, not raw data.
Your unified dashboard should show:
- Daily P&L view: Revenue, ad spend, contribution margin
- Channel performance: Spend, revenue, CAC, ROAS by channel
- Funnel: Sessions → Add to Cart → Checkout → Purchase by source
- Product performance: Top sellers, margin by SKU, inventory velocity
💡 This is what we do. We build unified ecommerce dashboards for DTC brands using exactly this stack — Fivetran, BigQuery, Looker Studio. Fully managed in our cloud, delivered in ~3 weeks. Book a 20-minute discovery call — no pitch, just scoping.
What to Watch Out For
Attribution double-counting. If you sum Google Ads conversions and Meta conversions, you’ll get more sales than Shopify shows. Use Shopify as your revenue source of truth and allocate credit using a consistent model.
GA4 data delays. GA4 data can take 24-48 hours to finalise. Don’t compare today’s GA4 numbers to today’s Shopify numbers.
Connector costs. Fivetran charges by monthly active rows (MARs). A typical Shopify store with 4 connectors runs $300-600/month on Fivetran. Factor this in.
Maintenance. APIs change. Schemas evolve. Someone needs to monitor pipelines and fix breakages. This isn’t a “set and forget” system — it needs ongoing care.
The ROI Calculation
A unified dashboard saves 5-10 hours per week in manual reporting. More importantly, it enables faster decisions.
If shifting ad budget one day earlier saves you even 2% of weekly spend on a $50K/month ad budget, that’s $4,000/month in savings. The entire infrastructure costs less than $1,000/month.
The math works for any brand spending over $10K/month on ads.
Build vs. Buy
You have three options:
| Approach | Timeline | Cost (monthly) | Maintenance |
|---|---|---|---|
| Build in-house | 6-12 weeks | $500-1,500 (tools) | You handle it |
| Hire a freelancer | 4-8 weeks | $500-1,500 + hourly | Fragile |
| Managed service (e.g. Chartica) | ~3 weeks | Retainer + tools | Fully managed |
The right choice depends on whether you have someone who can maintain SQL models and monitor data pipelines long-term. Most brands under 20 employees don’t.
Know someone drowning in spreadsheets? Share this guide with them.
If this sounds like more work than you want to take on, that’s what we do at Chartica. Book a 20-minute discovery call — we’ll scope it out, no pitch.