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Card class: Non-Hero • Category: Ecommerce Platform

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At a glance

Customers split by 90-day spend tier: One-time, Casual (2-3 orders), Regular (4-9 orders), VIP (10+ orders or top-spend cohort). Plus a separate New vs Returning split based on first-order timing. The card the marketing team uses to segment campaigns, set loyalty-program tiers, and prioritise retention investment.
What it countsEach registered customer with at least one order in the 90-day window assigned to a tier based on order count and / or total spend. Default tiers: One-time (1 order), Casual (2-3), Regular (4-9), VIP (10+ OR top 5% spend). Configurable thresholds per merchant. Headline shows count and revenue share per tier.
VAT / tax treatmentTax-inclusive for the spend dimension.
ShippingIncluded in customer spend.
DiscountsAlready deducted.
RefundsNot deducted (gross spend tier; refunded customers stay in their original tier).
Cancelled / voided ordersExcluded from order count and spend.
CurrencyMulti-currency without FX in the spend dimension. Order count is currency-agnostic.
Channels / sourcesAll channels contribute. Customer order count sums across all channels (web + POS + B2B + marketplace if linked).
New vs returningOrthogonal to the tier breakdown. New = first-ever order in the period; Returning = first order before the period. The two splits combined tell you the acquisition / retention dynamic.
B2B Edition noteB2B accounts often only place 4-12 orders/year (quarterly cycles), so the default tier thresholds underweight B2B. Configure B2B-specific tiers (e.g. VIP = 4+ orders rather than 10+) for meaningful B2B segmentation.
Healthy distributionTypical B2C BC store: 50-65% one-time, 20-25% casual, 8-15% regular, 2-5% VIP. The tail (regular + VIP) drives 40-60% of revenue despite being 10-20% of customers. Strong Pareto.
Time window90D (longer window than 30D for more stable segmentation; tier assignments shift slowly)
Alert triggerNone on this card directly.
Rolesowner, marketing

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Calculation

RANGE(totalIncTax) by spend bucket
  WHERE date BETWEEN [period_start, period_end]

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Worked example

A US homewares brand on BigCommerce Pro, 90-day window 14 Feb 26 to 14 May 26.
TierCustomers% of baseAvg order countAvg total spendRevenue contribution% of revenue
One-time5,84058%1$86$502,24030.4%
Casual (2-3 orders)2,42024%2.4$194$469,48028.4%
Regular (4-9 orders)1,36013.5%5.8$456$620,16037.6%
VIP (10+ or top 5%)4804.8%14.2$1,180$566,40034.3%
Total10,100100%1.95$164 avg$1,651,000100%
New customers4,82048%1.4$108$520,56031.5%
Returning customers5,28052%2.6$214$1,129,92068.5%
What’s interesting:
  1. VIPs at 4.8% of customers drive 34.3% of revenue. Classic Pareto, the top tier deserves disproportionate retention investment. White-glove customer service, exclusive access, dedicated account manager (if budget allows). Cross-reference BC Top Customers for the named VIP list.
  2. Regular tier (13.5% of customers / 37.6% revenue) is the hidden growth engine. These customers are demonstrably loyal but not yet VIP. Loyalty-program tier upgrade for high-Regular customers (those at 8-9 orders trending toward 10+) creates VIP-aspiration motivation; many will upgrade themselves with the right incentives.
  3. One-time at 58% of customers / 30.4% of revenue is the conversion target. Half of these will never come back; the other half can be converted to Casual with the right post-purchase flow (welcome series, replenishment reminder, “you might also like” emails). Even a 10% one-time-to-casual conversion adds 290 new Casual customers.
  4. Returning customers carry 68.5% of revenue at 52% of count. This is the retention-economy reality: returning customers spend roughly 1.6× more than new customers in the period. Retention investment ROI is structurally higher than acquisition.
  5. 48% new customer share is balanced. Above 60% suggests acquisition heavy / leaky retention; below 30% suggests stagnant base / weak acquisition. 40-55% is healthy growth dynamic.
Action priority order:
  1. Loyalty-program tier definitions match this card’s tier structure; make tier benefits visible to encourage upgrade.
  2. Post-purchase flow for one-time customers automated email sequence to convert one-time to Casual; biggest absolute opportunity by customer count.
  3. VIP retention program identify the 480 VIPs by name; assign account-management attention; quarterly check-ins.
  4. Regular-tier upgrade incentive “spend $X more this quarter and unlock VIP” creates aspiration without giving away margin.
  5. Quarterly: review tier thresholds customer behaviour drifts; tier definitions may need adjustment to maintain meaningful segmentation.

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Sibling cards merchants should reference together

CardWhy pair it with Customer Segments
BC Top CustomersThe named VIP list; this card is the aggregate; that card is the individuals.
Customer CountThe total customer base denominator.
Repeat RateCross-segment repeat-rate; VIPs by definition repeat; one-time conversion to repeat is the key metric.
New CustomersThe new-customer subset; this card splits new vs returning.
Churn RiskAt-risk customers concentrate in casual and regular tiers (one-time can’t be at-risk; VIPs are usually safe).
Order FrequencyPer-customer order frequency; informs tier definitions.
BC Top CouponsCoupon usage by segment; first-purchase coupons primarily target one-time-to-casual conversion.
klaviyo.kl_lifecycle_stagesKlaviyo’s predictive customer-lifecycle segments; should align with this card’s tiers.

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Reconciling against the vendor’s own dashboard

Where to look in BigCommerce Control Panel: Customers → Customer Groups lets you define groups (typically used for B2B / wholesale tiers, not RFM-style spend tiers). Analytics → Customer Reports on Plus / Pro / Enterprise plans includes a “Top Customers” view but no native RFM-tier view. The spend-tier segmentation is Vortex IQ’s contribution; BC has the data but doesn’t surface it as tiers. Why our number may legitimately differ from any BC native view:
ReasonDirection
Tier thresholds. Default thresholds (1, 2-3, 4-9, 10+) are configurable. Different thresholds produce different distributions.Configurable
VIP definition. Default is “10+ orders OR top 5% spend”. Some merchants prefer pure spend-based; configurable.Configurable
Time window. We use 90D rolling; BC’s “Top Customers” is lifetime by default.Different baselines
Cancelled orders. Excluded from order count; some BC views include.Vortex IQ LOWER counts
Guest dedup. Default excludes guests; toggle includes.Different totals
Multi-currency. Spend dimension doesn’t FX; tier-by-spend may misclassify multi-currency customers.Configuration-dependent
Cross-connector reconciliation (when CRM and email integrations are connected):
CardExpected relationshipWhat causes legitimate divergence
klaviyo.kl_lifecycle_stagesKlaviyo’s predictive segments; one-time / casual / regular / VIP should align broadly.Klaviyo uses email-engagement and predictive ML; this card uses raw order data. ~75-85% overlap typical.
hubspot.hs_lifecycle_stageHubSpot’s lifecycle stages; partial alignment depending on configuration.HubSpot includes B2B-specific stages (lead, opportunity, customer); not 1:1 with retail spend tiers.
The customer-segment view is BC-aligned with Shopify and Adobe Commerce; semantics broadly equivalent though tier definitions vary.

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Known limitations / merchant FAQs

My one-time tier is 70% of customers, is that bad? Not necessarily, but it’s the lever. 50-65% one-time is typical B2C; above 70% means high acquisition / weak retention. Investigate post-purchase flow: do customers receive a welcome email, replenishment reminder, second-purchase incentive? Each missing touchpoint loses one-time-to-casual conversion. My VIP tier shrinks every quarter, why? Either VIPs are being acquired slower than they’re aging out (regular promotion to VIP requires sustained behaviour), or some VIPs are downgrading. Cross-reference Churn Risk for at-risk VIPs. VIP retention is the highest-LTV-per-effort investment. Should I run different ad campaigns per tier? Yes for VIP (lookalike audiences from VIPs convert highest); maybe for one-time (acquisition targeting); rarely for Casual / Regular (these tiers are mid-funnel and respond to email marketing more than ads). Klaviyo / Mailchimp lookalikes from VIP cohort are typically the highest-ROAS audience. My B2B tier distribution looks weird, why? Default tiers are B2C-calibrated. B2B accounts often only place 4-12 orders/year, so they classify as Casual or Regular instead of VIP. Configure B2B-specific thresholds (VIP = 4+ orders for B2B) for meaningful B2B segmentation. Or run separate B2B and retail dashboards. **A customer with 1 huge order (5,000),whattier?∗∗Defaultorder−countbasis=One−timetier(despitehighspend).Configurabletospend−basedVIPdefinition:"VIPiftotalspend>5,000), what tier?** Default order-count basis = One-time tier (despite high spend). Configurable to spend-based VIP definition: "VIP if total spend >X”. Many BC merchants use a hybrid: VIP = 10+ orders OR top 5% spend. The single-large-order customer is usually B2B or wholesale; tag them in BC for separate treatment. Should the card include guests? Default no (guests can’t be tier-tracked across orders without email dedup). Toggle to include email-deduped guests if your guest rate is high (>30%); otherwise guest exclusion is cleaner. Why does my Klaviyo segment differ from this card’s tier? Klaviyo uses predictive ML (email engagement, click patterns, time-since-last-purchase); this card uses raw order data. They overlap 75-85% but diverge on edge cases (an active email-clicker who hasn’t purchased recently shows as engaged in Klaviyo but at-risk here). Both views are valid for different decisions. Should I exclude refund-heavy customers from tier calculations? Configurable. Some merchants exclude customers with >50% refund rate (they’re unprofitable regardless of order count); others include them as “active customers, problematic”. For LTV-focused decisions, exclude; for engagement-focused, include. My Regular tier is shrinking but VIP is growing, healthy? Mixed signal. VIPs growing means upgrades are happening; Regular shrinking means either upgrades to VIP (good) or downgrades to Casual (concerning). Cross-reference flow rates: out of last quarter’s Regular tier, how many became VIP vs Casual vs Lapsed? The flow direction tells the story. Multi-currency: do tier definitions adjust per currency? The order-count dimension is currency-agnostic. The spend-based VIP threshold doesn’t FX-adjust; multi-currency customers may be misclassified. Configure per-currency thresholds or use single-currency conversion for clean comparison. Should I email all tiers the same content? No. VIPs deserve exclusive previews and personal outreach; Regulars get loyalty / replenishment messaging; Casuals get repeat-purchase incentives; One-time get welcome / second-purchase nudges. Generic email-blast-everyone is missed segmentation opportunity. My new vs returning split shifted toward returning, why? Either acquisition slowed or retention improved (or both). Investigate: cross-reference New Customers trend; if new is flat / falling, acquisition is the issue (ad-spend efficiency, SEO, content). If new is growing but returning is growing faster, retention is winning, healthy mature pattern. How often should I re-baseline tier thresholds? Quarterly review; annual recalibration. Customer behaviour drifts (industry-wide trends, your catalogue evolution, channel mix changes). The tier definitions that segmented well 18 months ago may segment poorly today; periodic recalibration keeps the card actionable.

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Tracked live in Vortex IQ Nerve Centre

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