Shopify’s analytics dashboard puts five numbers front and centre: total sales, sessions, conversion rate, AOV, returning customer rate. Every one of them can mislead you if you read it wrong. Here’s how to read them properly — and what to look at instead.
Conversion rate
The lie: “Our conversion rate is 2.1%, industry average is 2.5%, we have a problem.”
The truth: conversion rate is meaningless without traffic context. A store with 100% organic-search traffic from buying-intent keywords should convert at 5%+. A store with 100% top-of-funnel paid social traffic might be doing brilliantly at 1.5%. The “industry average” is averaged across both, so it tells you nothing about your mix.
Look at instead:
- Conversion rate by traffic source
- Conversion rate trend over 90 days (is it improving?)
- Conversion rate by mobile vs. desktop (mobile is usually 0.5× desktop — alarming if much worse)
- Conversion rate of returning visitors specifically (this should be 4–8% for any decent store)
Average order value
The lie: AOV “increased” 12% — celebrate the upsell strategy.
The truth: AOV can rise because you actually upsold customers, OR because lower-AOV customers stopped buying. Same number, opposite stories. The first is good; the second is collapse disguised as improvement.
Look at instead:
- AOV alongside total order count (both up = real win; AOV up + orders down = something’s wrong)
- Distribution, not average. Median order value tells you more about typical customer behaviour. A handful of $2,000 wholesale orders can lift the mean while typical customers spend $40.
- AOV by acquisition channel (paid vs. organic vs. email)
Sessions
The lie: “Traffic is up 20%.”
The truth: sessions can spike from bot traffic, scraping, ad fraud, or mass-emailing the same list twice. Useful sessions are qualified sessions — humans, with intent.
Look at instead:
- Sessions filtered to your real audience (exclude internal IPs, bot user-agents)
- Engaged sessions (sessions > 30s, or with multiple page views)
- Sessions by traffic source (organic up = SEO win; paid up = budget increase)
Returning customer rate
The lie: “Returning customer rate is 30%, customers love us.”
The truth: this metric counts whether a customer has made multiple orders, not how often or how recently. A customer who bought twice three years ago still counts as “returning.”
Look at instead:
- 90-day repeat rate (of customers who bought 90 days ago, what % bought again?)
- Time-between-orders (median days between order 1 and order 2)
- Cohort retention curves (visualised properly — most stores never look at these)
Total sales
The lie: “Sales are up 15% month-over-month.”
The truth: sales lag — they reflect marketing spend from previous months, seasonal demand, and one-off campaigns. A 15% MoM lift after Black Friday is normal; a 15% MoM lift in May from “doing nothing differently” is unusual and worth investigating before celebrating.
Look at instead:
- Sales smoothed over 30 days vs. 30 days prior (vs. 30-day lookback)
- Sales by acquisition cohort (are customers acquired in March worth more than customers acquired in February?)
- Marketing-attributed sales vs. organic baseline
The metrics actually worth tracking
If we audited a store and got to keep five metrics, we’d pick:
- CAC blended — cost of acquiring one new customer, all channels combined
- LTV : CAC ratio — should be > 3:1 for a healthy DTC business
- 90-day repeat rate — proxy for product-market fit
- Mobile conversion rate vs desktop — proxy for storefront UX quality
- Email-attributed revenue % — proxy for retention infrastructure
Total sales is downstream of all five. Conversion rate is one input among many. AOV is contextual. Treat the headline numbers as starting questions, not answers.
A weekly ritual
Every Monday, look at:
- Last week’s revenue vs. 4-week trailing average
- Last week’s CAC vs. trailing
- Last week’s email-attributed share
- One question from each: what changed, why?
Five minutes. More signal than the dashboard can offer in an hour.