At a glance
Customers placing their first lifetime order in the period. The acquisition signal: how many genuinely new buyers did marketing bring in?
| What it counts | COUNT(DISTINCT customer.id WHERE customer.numberOfOrders = 1 AND first_order.createdAt IN window). Each newly-acquired customer counts once. |
| API endpoint | Admin GraphQL. Customer.id, Customer.numberOfOrders, Customer.firstOrderId. |
| What “new” means | First-order customers, ever. A customer who returns after a year of inactivity is NOT a new customer; they are returning. Truly new = lifetime first order. |
| VAT / tax treatment | Not applicable. |
| Shipping | Not applicable. |
| Discounts | Not applicable. |
| Refunds | New customers whose first order was refunded still count. |
| Cancelled / voided orders | A first-time customer whose only order was cancelled may still count as new (depends on whether customer.numberOfOrders was incremented before cancellation). |
| Currency | Multi-currency safe. |
| Channels / sources | Online + POS-with-account + B2B contribute. POS-guest checkouts don’t create customer records and don’t count. |
| Time window | 30D vsP (default 30D vs the prior 30D) |
| Alert trigger | None on this card directly; significant drops surface via period comparison. |
| Roles | owner, marketing |
Calculation
Worked example
A UK DTC apparel brand on Shopify. Period: 12 Apr 26 to 11 May 26. Total 1,978 orders, 1,612 unique customers.| Cohort | Count | Share of unique | Share of orders | AOV |
|---|---|---|---|---|
| New customers (first lifetime order) | 712 | 44.2% | 712 / 1,978 = 36.0% | £58 |
| Returning customers | 900 | 55.8% | 1,266 / 1,978 = 64.0% | £64 |
| Total | 1,612 | 100% | 1,978 (100%) | £62 |
- 44% new customer share is acquisition-heavy. Established DTC brands typically run 20-35% new-customer share. A 44% share suggests either rapid acquisition growth (good if profitable) or weak retention dragging existing customers out (bad). Cross-reference Repeat Rate to confirm.
- New customer AOV (£58) is below returning AOV (£64). Standard pattern: first purchases are smaller, exploratory. The 9% gap is healthy. A new-AOV equal to or above returning-AOV would be unusual and suggests the new customers are coming in at the top of the funnel (e.g. heavy gift-card or bundle-driven acquisition).
- 712 new customers represent a significant retention opportunity. If the brand can convert 30-40% of these to a second order within 90 days (industry-typical for apparel), that’s 215-285 incremental returning customers next quarter.
- The +21% growth needs unit economics check. Higher acquisition is only good if CPA is in line with LTV. If ad spend grew >21% to deliver this volume, CPA degraded. Pair with channel-level acquisition costs.
- Multi-channel acquisition split matters. Online-store new customers are different from POS new customers (POS new is a walked-in passer-by; online new is an ad-clicker). Filter by sales-channel for the channel-specific story.
Sibling cards merchants should reference together
New-customer count is one half of growth math. Pair with these:| Card | Why pair it with New Customers | What the combination tells you |
|---|---|---|
| Customer Count | Total customers. | New ÷ total = new-customer share. |
| Repeat Rate | Returning subset. | New + returning split shows the engine. |
| Customer Trend | Customer acquisition over time. | Plot new-customer trend to see acquisition shape. |
| Churn Risk | Lapsed-customer count. | New > Lapsed = growing customer base; New < Lapsed = leaking. |
| AOV | First-purchase AOV vs returning AOV. | First-purchase AOV is usually 5-15% lower than returning. |
google_ads.google_new_customer_revenue | Paid-channel acquisition $. | Ad-spend efficiency = new-customers ÷ ad-spend. |
facebook.fb_new_customer_revenue | Meta-channel acquisition $. | Same logic. |
Reconciling against the vendor’s own dashboard
Where to look in Shopify Admin: Analytics → Reports → “First-time vs returning customers” → set the date range. Should align closely with this card’s new-customer count. Other Shopify Admin views:- Customers → All customers → segment by Number of orders is 1: approximate first-time-customer list.
- Reports → Customer cohort analysis (Plus only): cohort-level retention.
| Reason | Direction | Why |
|---|---|---|
| Definition | Same | Both count customers whose first order was in window. |
| Time zone | Boundary days | Standard time-zone gap. |
| Customer merging | Transient | Shopify’s auto-merge changes the definition retroactively; pre-merge data may differ. |
| Test orders | Ours slightly higher | Test order filter not applied. |
| Sync lag | Ours lower for “today” | 5 to 15 minute index lag. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_ads.google_new_customer_revenue | Paid acquisition signal | Google’s “new customer” definition uses GA4 first-time-buyer flag; not 1:1 with Shopify’s. |
facebook.fb_new_customer_revenue | Meta acquisition signal | Same logic. |
Known limitations / merchant FAQs
Why is my new-customer count rising while ad spend is flat? Organic acquisition is improving. Could be SEO, word of mouth, PR mentions, or referral. Healthy; profitability unchanged or improving. Why is my new-customer count falling? Three usual causes:- Ad spend decline. Direct relationship; more spend usually = more acquisitions.
- Audience saturation. Ad-account hit diminishing returns; CPA rising.
- Channel decay. A specific channel (e.g. Meta) tightening attribution or losing performance.
- Check ad-spend per channel; recent pauses are most common cause.
- Check campaign performance; CPA may have spiked.
- Check site uptime and conversion rate; poor performance can mask traffic.
- Check organic / SEO; algorithm changes can crash organic acquisition.
- Compare year-over-year if seasonal noise is suspected.