At a glance
Distinct customer count whose first BC order falls in the last 30 days, vs the prior 30 days. The single-period summary that pairs with Customer Acquisition Trend (the daily shape). New customers are the leading indicator for top-of-funnel health, where the trend tells you “what’s happening”, this number tells you “by how much”.
| What it counts | CARDINALITY(customerId WHERE first_order_date in last 30 days). We identify each customer’s first-order date; if it falls in the last 30 days, the customer counts as new. Returning customers don’t contribute. |
| VAT / tax treatment | n/a, customer count. |
| Shipping | n/a. |
| Discounts | n/a. |
| Refunds | Not deducted. A customer whose first order was later refunded still counts (commerce reality is messier than booking accounting). |
| Cancelled / voided orders | Included. We count the customer at the moment they placed an order, not after payment outcome. |
| Currency | n/a. |
| Channels / sources | All BC channels contribute. POS first-time customers (often walk-ins providing email at the till), Amazon Channel Manager first-purchasers, B2B portal first-buyers, all count. The channel mix of new customers is more meaningful than the headline count. Pair with BC Channel Revenue Mix. |
| Guest checkout treatment | A guest checkout has customerId = 0. We do NOT roll all guests into a single customer; each guest order’s customerId = 0 row is treated as its own first-time event. For stores with heavy guest traffic this may overcount. A registered-customer-only view filters customerId != 0. |
| B2B Edition note | B2B procurement teams typically register once and order repeatedly. The first-time count reflects new accounts, not new orders; a B2B store with a flat new-customer count and growing revenue is healthy. |
| Vs prior 30 days comparison | The card shows two numbers: current 30 days and prior 30 days. Delta % = (current - prior) / prior. Useful for spotting acquisition acceleration or decay. |
| Time window | 30D vsP (rolling 30 days vs prior 30 days) |
| Alert trigger | None at this card directly, but the delta is monitored as part of BC Channel Revenue Drop Alert. |
| Roles | owner, marketing |
Calculation
Worked example
A US homewares brand on BigCommerce Pro, comparing 14 Mar 26 to 12 Apr 26 vs 12 Feb 26 to 13 Mar 26.| Metric | Prior 30 days | Last 30 days | Delta |
|---|---|---|---|
| New customers (total) | 1,140 | 980 | -14.0% |
| New via web (Stencil) | 880 | 720 | -18.2% |
| New via POS | 95 | 110 | +15.8% |
| New via Amazon Channel Manager | 165 | 150 | -9.1% |
- Headline 14% drop in new customers is concerning. That’s roughly 80 AOV × 1.5 orders avg first 30 days). Compounded over 12 months this is the kind of dip that defines an acquisition decay arc.
- The web channel is the cause. -18.2% drop on web outweighs the small POS gain. Acquisition decay in this store is web-channel-specific, almost always paid-channel-driven (CPM rises, fatigued creative, ad-account issues). Pair with Google Ads cost-per-acquisition data.
- POS rose 15.8%, masking part of the headline drop. A new physical store opened mid-period, generating walk-in acquisition. POS is structurally a different acquisition mechanism than web; treat the two channels separately for diagnostic purposes.
- Amazon Channel Manager dropped 9% which is normal seasonal noise. Below ±15% on a single channel month-over-month is rarely worth investigating in isolation.
- The 980 new customers are a stockpile, not a flow. Each becomes a candidate for the welcome flow, the cross-sell flow, and the first-repeat-purchase trigger. The retention-marketing team should be working harder during acquisition droughts, repeat orders from the existing base offset acquisition decay.
- Decompose by channel. Use BC Channel Revenue Mix plus a channel-filtered view of this card. Drops almost always concentrate in 1-2 channels.
- Cross-reference paid acquisition cost. Open Google Ads and Meta Ads dashboards; rising CPC / CPM with stable spend = fewer customers acquired. The drop is platform-side, not your campaign-side.
- Audit organic traffic via BC Organic Recovery Rate. Slower-moving organic decay can compound over months; check whether ranking has slipped on key landing pages.
- Email / CRM activity check. If
klaviyo.kl_new_subscribersis also down, top-of-funnel reach has shrunk; if subscribers are stable but customers dropped, conversion of subscribers to first-purchase has weakened. - Check for technical regressions. A theme update, a checkout-flow change, or a payment-gateway misconfig can all suppress new-customer counts in ways the marketing team doesn’t immediately see. Pair with BC Incomplete Rate.
Sibling cards merchants should reference together
| Card | Why pair it with New Customers |
|---|---|
| Customer Acquisition Trend | The daily-shape version. This card is the period summary; that one shows the daily build. |
| Customer Count | The total customer base. New customers / total customers = acquisition velocity. |
| Repeat Customer Rate | The retention complement. New customers grow the base; repeat rate determines whether they become long-term. |
| BC Guest vs Registered | Guest vs registered new customer split. Registered new customers convert to repeat at 3-5x the guest rate. |
| BC Channel Revenue Mix | Channel decomposition; new customers split by channel. |
| Customer Countries | Geographic decomposition. Drops often concentrate by country (currency or local-language SEO regression). |
google_analytics.ga_new_users | GA4 new-user count, broader funnel signal. |
klaviyo.kl_new_subscribers | Email-list growth, leading indicator for new customers by 1-3 weeks. |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce Control Panel: Analytics → Customers on Plus / Pro / Enterprise has a “New vs Returning” tile that shows new-customer counts by period. Standard plan stores need to compute manually from the Customers → View export. For B2B Edition, B2B → Companies shows new B2B accounts; consumer-facing new-customer counts live under the retail analytics view. Why our number may legitimately differ from BC Analytics:| Reason | Direction |
|---|---|
| First-order date vs registration date. BC may count new customers as “registered in the period” rather than “first ordered in the period”. A non-purchasing registrant counts in BC’s view but not ours. | BC HIGHER |
| Guest aggregation. BC’s analytics may collapse all guest checkouts into a single super-customer; we count each guest order. | Vortex IQ HIGHER |
| Channel coverage. BC Analytics may default to web-only; we include POS, marketplace, B2B, etc. | Vortex IQ HIGHER |
| Time zone. UTC vs store TZ. | Boundary effects |
| Cancelled orders. We include them; BC may exclude. | Vortex IQ HIGHER |
| Sync lag. Recent orders may be missing. | Vortex IQ slightly LOWER |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_analytics.ga_new_users | GA4 new-users count traffic level; this card counts purchase-converted | The two should rise and fall together; absolute levels differ by 50-200x. |
klaviyo.kl_new_subscribers | Email subscribers should lead new customers by 1-3 weeks | Subscription-to-purchase conversion typically 5-15%. |
google_adwords.ga_new_customer_conversions | Paid-search-attributed new customers | Should be a subset (10-40%) of total new customers. |
shopify.new_customers(planned)adobe_commerce.new_customers(planned)
Known limitations / merchant FAQs
My new-customer count dropped 14% but revenue is steady, should I act? Yes, before the lag catches up. Steady revenue on falling acquisition means returning customers are doing the work; that pattern is sustainable for 1-2 quarters before LTV runs out. Investigate channel mix and paid-acquisition cost now; don’t wait for revenue to follow. Why does my new-customer count look low compared to GA4 new users? GA4 new-users counts every new device-level visitor; this card counts new purchasing customers. The two should rise and fall together but the absolute levels differ by 50-200x. The ratio (purchasing customers / new users) = the new-visitor conversion rate; below 1% suggests reaching the wrong audience. Should guest checkouts count as new customers? Definitionally yes, each guest checkout is a first-time purchase. Pragmatically, guests aren’t long-term customers because you can’t reach them post-purchase (no email, often no consent). For LTV-focused stores filter to registered new customers only. My B2B store has flat new-customer count, is that bad? No, that’s normal for B2B. B2B customer counts grow slowly (5-15% year-over-year is healthy) because each customer is a relationship. Use BC Top Customers and order frequency metrics instead. Why does this card differ from my Klaviyo new-subscriber count? Klaviyo’s new-subscriber count is people who opted in to email; this card is people who placed a first order. Subscriber-to-customer conversion is typically 5-15%. The two cards together tell a richer top-of-funnel story. Can I segment new customers by acquisition source? Not from this card directly. Pair with GA4 acquisition for source-medium attribution and with BC Channel Revenue Mix for channel decomposition. We’re working on a per-channel new-customers card; track the V2 backlog. My new-customer count spiked 200% week-over-week, what does that mean? Almost always a campaign-driven surge. Influencer collab, viral moment, PR placement. Spikes are healthy; the watch-out is the 30-60 day cohort retention. Surge-acquired customers often convert to repeat at 5-15% vs the typical 25-40% baseline. Track the cohort via BC Top Customers cohort filter. Why is my POS new-customer count low? POS captures email at typically 50-80% rate (lower than web’s 95-99%). Customers who don’t provide email show as guest (customerId = 0) and inflate the guest count rather than the registered new-customer count. Use a registered-only view for the cleaner POS signal.
Does this card include subscription orders?
Yes, every order type. A new customer whose first BC order is a subscription product counts as a new customer on the date of subscription start. Subsequent recurring shipments don’t create new-customer events.
My acquisition velocity dropped, what’s the order of operations?
(1) Confirm the drop is real and not a seasonal artefact (compare year-over-year, not just month-over-month). (2) Decompose by channel; almost always 1-2 channels are responsible. (3) For paid channels, check CPM / CPC trends, fatigue may be the cause. (4) For organic, check ranking via Search Console; ranking decay is slow but compounding. (5) For email/CRM, check subscription growth and welcome-flow conversion.