Shopify Analytics: The KPIs That Actually Matter

Shopify gives you 40+ reports out of the box. Most of them are noise. The 6–8 metrics that actually drive decisions are the ones every operator should know cold — and most don't.

Here's the metric stack we track on every store, why each one matters, and the targets to aim for.

Revenue and traffic basics

These are table stakes — track them daily.

• Sessions. Total store visits. Watch the trend, not the absolute number.
• Conversion rate. Orders ÷ sessions. Industry benchmark for e-commerce: 1.5–3.5%. Top quartile: 4%+.
• Average order value (AOV). Revenue ÷ orders. Use to evaluate upsell, bundle, and AOV-tactic effectiveness.
• Revenue. The final number, but the least useful for diagnosing problems.

Customer-level metrics that drive long-term decisions

These take 90+ days to build but matter more than session-level metrics.

• Customer lifetime value (LTV). Total revenue ÷ unique customers, or a more sophisticated cohort calculation. Track 30/60/90/180/365-day LTV separately.
• Customer acquisition cost (CAC). Total marketing spend ÷ new customers acquired. Includes paid media, content, agency fees, attribution tools — everything.
• LTV:CAC ratio. Should be 3:1 or higher. Below 2:1, you're losing money. Above 5:1, you're underspending and could be growing faster.
• Repeat purchase rate. Orders from returning customers ÷ total orders. Target: 25–40% depending on product category.

Marketing efficiency metrics

For paid acquisition specifically:

• ROAS (Return on Ad Spend). Revenue ÷ ad spend, attributed by platform.
• MER (Marketing Efficiency Ratio). Total revenue ÷ total marketing spend, blended across all channels. The truer number.
• nCAC (new customer acquisition cost). Spend ÷ new customers (excluding repeats). Stricter than blended CAC, more useful for scaling.
• Contribution margin per order. Order revenue minus COGS, shipping, payment fees, and ad spend. The actual profit before fixed costs.

Operational metrics

Easy to overlook, expensive to ignore.

• In-stock rate on top 20 SKUs. Should be 95%+.
• Order fulfillment time. Average hours from order to ship. Should be under 24 for modern e-commerce.
• Cart abandonment rate. Standard is 65–75%. Below 60% means your funnel is unusually clean — or your cart logic is misfiring.
• Refund rate. Orders refunded ÷ orders shipped. Above 5% is a quality or expectation problem.

Where to find these in Shopify

• Sessions, conversion, AOV, revenue. Native Analytics dashboard.
• LTV, repeat purchase rate. Custom reports (Advanced plan or Plus). Or pull from a third-party tool like Lifetimely or Triple Whale.
• CAC, MER, nCAC. Not in Shopify — calculate manually with a sheet, or use Triple Whale, Northbeam, or similar.
• Contribution margin. Custom calculation. Settle is one tool that does this well; many operators just build a sheet.
• Operational metrics. Inventory app + Shopify reports.

A daily/weekly/monthly cadence

• Daily glance (5 min): sessions, orders, revenue, ad spend, gross ROAS. Just to see if anything's broken.
• Weekly review (45 min): all the above plus channel-level performance, top SKUs, returns, refund rate, inventory exceptions.
• Monthly deep dive (3 hrs): cohort analysis, LTV by acquisition channel, contribution margin trend, marketing-mix analysis, inventory health.

The discipline is the practice itself. Operators who run this rhythm find issues 4–8 weeks before operators who only check the dashboard when something feels off.

Targets to benchmark against

For a healthy DTC store doing $1M–$5M ARR:

| Metric | Target | |---|---| | Conversion rate | 2.5–4% | | AOV | $60–$120 (varies hugely by category) | | Cart abandonment | 65–75% | | Repeat rate | 25–40% | | LTV:CAC | 3:1 minimum | | Email % of revenue | 25–35% | | In-stock rate (A items) | 95%+ | | Refund rate | <5% |

Use these as starting points, not gospel. Categories vary — replenishables hit 60%+ repeat rates; fashion does well at 20%. Know your category benchmark, not just the e-commerce average.

Common analytics mistakes

• Watching ROAS in isolation. A 4x ROAS campaign with 10% incrementality is worse than a 2x ROAS campaign with 80% incrementality.
• Not tracking new vs returning ROAS separately. Returning customers convert at 3–5x the rate of new ones. Mixing them inflates platform reporting.
• Ignoring 90-day cohorts. Day-1 metrics tell you nothing about whether the customer was profitable to acquire.
• Over-trusting platform attribution. Facebook, Google, and TikTok each claim credit for the same conversion. Reconcile against MER, which is the true number.
• Decision paralysis. Tracking 40 metrics and acting on none. Pick 6–8, watch them religiously, ignore the rest.

The single most important habit

Build a weekly metrics email to yourself (or your team) every Monday morning. Same format, same metrics, every week. After 8 weeks you'll spot trends you'd have missed staring at dashboards. After 26 weeks you'll see the patterns that drive your business — and that's what makes you a real operator instead of just a marketer.