TL;DR Shopify’s default analytics are fine for day one. But they miss cohort analysis, blended CAC, true LTV, and multi-touch attribution — the metrics that actually drive growth. Book a 20-min call to build Shopify reporting that scales.
What Shopify analytics does well
Credit where it’s due. Shopify’s built-in dashboard handles the basics:
- Total sales and orders
- Online store sessions
- Conversion rate (session to purchase)
- Average order value
- Top products and referral sources
For a brand doing under £50K/month with one acquisition channel, this is probably enough. You can see what’s selling, what your conversion rate is, and where traffic comes from.
Where Shopify analytics falls short
Once you grow past the basics, the gaps become painful:
No real customer lifetime value (LTV)
Shopify shows you “returning customer rate” and average order value. It doesn’t show you true cohort-based LTV — how much revenue a customer acquired in January generates over 3, 6, 12 months.
This matters because a customer worth £30 on first purchase might be worth £180 over a year. If you don’t know that, you’ll underspend on acquisition and lose to competitors who do.
No blended customer acquisition cost (CAC)
Shopify doesn’t know what you spent on advertising. It can’t tell you whether your overall CAC is sustainable. You’re left manually pulling ad spend from Google, Meta, TikTok, and dividing by new customers.
No multi-touch attribution
Shopify credits the last click. If a customer saw a Meta ad, clicked a Google ad a week later, then purchased via direct — Shopify credits direct. That’s misleading.
No cohort analysis by default
When did your best customers buy for the first time? What did they buy? Which channel brought them? Shopify’s reports don’t support this level of analysis natively.
Limited segmentation
You can’t easily answer: “What’s the AOV for customers acquired via Instagram who bought in the last 90 days and have purchased more than once?” That requires data warehouse-level querying.
The metrics Shopify merchants actually need
| Metric | Why it matters | Where to get it |
|---|---|---|
| Cohort LTV (3/6/12 month) | Understand true customer value | BigQuery + Shopify data |
| Blended CAC | Know your real acquisition cost | Ad platforms + Shopify in warehouse |
| LTV:CAC ratio | Are you growing profitably? | Calculated from above |
| Contribution margin per order | True profitability after COGS and shipping | Shopify + cost data |
| Repeat purchase rate by cohort | Are you building loyalty? | BigQuery + Shopify data |
| Time between purchases | When to trigger retention campaigns | BigQuery + Shopify data |
| Revenue by acquisition channel (attributed) | Where to invest more | Multi-source attribution |
| Product affinity | What do people buy together? | BigQuery + Shopify data |
How to get beyond the default
Level 1: Shopify + Google Sheets (quick but fragile)
Export order data. Pull ad spend manually. Calculate metrics in a spreadsheet.
Pros: Free. Immediate. Cons: Breaks constantly. Doesn’t scale. Manual work every week. No automation.
Level 2: Shopify + third-party app (moderate)
Tools like Triple Whale, Lifetimely, or Polar Analytics add LTV, attribution, and cohort views.
Pros: Quick setup. No technical skills needed. Cons: Another subscription (£100-500/month). Limited customisation. You’re locked into their definitions and UI. Data stays siloed.
Level 3: Shopify + data warehouse (robust)
Export Shopify data to BigQuery. Combine with ad platform data, GA4, email platform data. Build custom reports in Looker Studio.
Pros: Complete flexibility. Own your data. Custom metrics. Scales infinitely. Cons: Requires setup. Needs maintenance (or a managed service).
This is the approach we recommend and build at Chartica. It takes about three weeks to set up and costs less than most third-party app subscriptions once running.
Building a proper Shopify analytics stack
Here’s the technical setup:
Data sources → Pipeline → Warehouse → Dashboard
- Shopify → Fivetran → BigQuery (orders, customers, products, refunds)
- Google Ads → Fivetran → BigQuery (spend, clicks, conversions)
- Meta Ads → Fivetran → BigQuery (spend, reach, conversions)
- Klaviyo → Fivetran → BigQuery (email revenue, flows, segments)
- GA4 → Native export → BigQuery (sessions, behaviour, attribution)
💡 This is what we do. We build the full Shopify analytics stack — pipeline, warehouse, LTV models, and dashboards — on a managed monthly retainer. Book a 20-minute discovery call — no pitch, just scoping.
Dashboards every Shopify brand needs
1. Daily trading dashboard
What happened today? Revenue, orders, AOV, sessions, conversion rate. Compare to yesterday and same day last week. This is your morning check-in.
2. Acquisition performance
Spend by channel. ROAS by channel. New customers by channel. Blended CAC. LTV:CAC ratio. Updated daily or weekly.
3. Cohort and retention
Monthly cohorts showing revenue over time. Repeat purchase rates. Time between purchases. This tells you whether your brand is building loyalty or buying one-time customers.
4. Product performance
Revenue by product/collection. Margin by product. Sell-through rate. Inventory days remaining. Which products drive repeat purchases?
5. Customer segments
New vs returning revenue split. High-value customer behaviour. At-risk customers (previously active, now dormant). VIP segment growth.
What this looks like in practice
A Shopify brand doing £200K/month came to us with Shopify’s default dashboard and Triple Whale. They couldn’t answer: “Which products, acquired through which channels, produce the highest 6-month LTV?”
We connected Shopify, Meta, Google, and Klaviyo to BigQuery. Built cohort models. Answered the question in a dashboard.
The answer changed their entire product strategy. Their hero product had the worst LTV. A secondary product line — one they were about to discontinue — produced 3x the repeat revenue.
That’s what happens when you go beyond the default.
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.