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”.
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.
What’s interesting:
- 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
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:
Cross-connector reconciliation:
Same-metric documentation cross-reference:
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.