> ## Documentation Index > Fetch the complete documentation index at: https://docs.vortexiq.ai/llms.txt > Use this file to discover all available pages before exploring further. # Orders by Device, BigCommerce > Orders by Device for BigCommerce stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Supporting Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Ecommerce Platform](/nerve-centre/connectors#connectors-by-type) ## At a glance > Order count by device type (mobile, desktop, tablet) over the trailing 30 days. The mobile-vs-desktop split that determines whether the merchant's checkout investments should be mobile-first. BigCommerce's Stencil framework supports responsive themes natively, but theme implementation varies; this card surfaces whether the mobile experience is converting in line with desktop. Note: BC's mobile checkout doesn't have a native equivalent of Shopify's Shop Pay, so mobile conversion is more theme-dependent. | | | | --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | `COUNT(orders) GROUP BY device_type` over the trailing 30 days. Device type derives from `customer_user_agent` parsed at order time, classified into mobile / desktop / tablet / unknown. Excludes Cancelled and Incomplete orders. | | **API endpoint** | BC `/v2/orders` exposes `customer_user_agent`; we parse via UA-Parser library at index time. The OpenSearch index materialises per-device-per-day order counts. | | **VAT / tax treatment** | n/a, count metric. | | **Shipping** | n/a. | | **Discounts** | n/a. | | **Refunds** | Refunded orders still count by their original device. | | **Cancelled orders** | Excluded. | | **`Incomplete` orders** | Excluded. | | **Currency** | n/a. | | **Channel coverage** | Web channel only by default. POS sales have no meaningful "device" (the POS terminal IS the device); marketplaces don't expose device. **Configure Settings → Channel filter to web-only** for a clean read; default already does this. | | **B2B Edition behaviour** | B2B portal access is typically desktop-heavy (60-80%) because wholesale buyers use office computers. **A B2B portal mobile-share above 25% is unusual**, usually means a procurement-app integration. | | **Tablet treatment** | Tablets are often grouped with mobile in retail conventions (touch-first UX) but as desktop in some analytics conventions (larger screen). We surface as a separate row; configure under Settings → Device classification → Tablet-as-mobile if you prefer the merge. | | **Unknown / bot detection** | UAs that don't match common patterns are classified as "unknown"; bot UAs are excluded. The unknown bucket is typically \<2%; higher unknown rate indicates UA-spoofing or bot traffic that survived filtering. | | **Time window** | `30D` rolling. | | **Alert trigger** | None on this card; pairs with [BC Channel Conversion Rate](/nerve-centre/kpi-cards/bigcommerce/conversion-rate-by-channel) for device-specific funnel alerts. | | **Roles** | owner, marketing | ## Calculation Calculated automatically from your BigCommerce data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading. ## Worked example A US fashion brand on BigCommerce Pro, web-only view (DTC `channel_id = 1`). Snapshot for 1 Apr to 30 Apr 26. | Device | Orders | % | AOV (context) | Conversion (context) | | --------- | --------- | ----- | ---------------- | -------------------- | | Mobile | 2,820 | 58.5% | \$98 | 2.1% | | Desktop | 1,560 | 32.4% | \$145 | 4.2% | | Tablet | 380 | 7.9% | \$128 | 3.6% | | Unknown | 60 | 1.2% | \$112 | n/a | | **Total** | **4,820** | | \$120 (headline) | 2.7% (web headline) | What's interesting: 1. **Mobile dominates order count (58.5%) but desktop dominates AOV ($145 vs $98).** This is the classic mobile-vs-desktop pattern: more mobile sessions and orders, smaller mobile baskets, larger desktop baskets. **Mobile customers tend to be browse-and-buy in a single session; desktop customers tend to research, save-cart, return-and-buy with larger baskets.** 2. **Conversion rate gap (2.1% mobile vs 4.2% desktop) is the bigger lever.** Desktop converts at 2x mobile, even though mobile has more sessions. **The biggest revenue-lift opportunity for this merchant**: closing 30% of the mobile-desktop conversion gap (from 2.1% to 2.7%) would add \$86k/month in revenue. The cause is usually checkout friction, slow page-loads, awkward form fields, no Apple Pay / Google Pay. 3. **Mobile $98 AOV vs desktop $145 = a 32% gap.** Mobile customers buy fewer items per cart. Bundle / upsell strategies that work on desktop (sticky sidebar, "complete the look" suggestions) often render poorly on mobile. **The mobile bundle UX is the single highest-leverage AOV improvement opportunity.** 4. **Tablet at 7.9% with desktop-like AOV (\$128) and conversion (3.6%) confirms tablet behaves more like desktop than mobile** for this merchant. The Stencil theme renders well on tablet; tablet customers behave like desktop customers, just on a smaller screen. **For this merchant, tablet should be optimised alongside desktop.** 5. **Unknown at 1.2% is acceptable.** Above 5% would suggest UA-spoofing or bot traffic; 1.2% is normal hand-jamming and obscure-device noise. Action priority order: 1. **Audit mobile checkout flow.** Mobile conversion below 2x desktop is the single highest-leverage improvement on most BC stores. 2. **Implement Apple Pay / Google Pay.** BC supports both; many merchants haven't enabled them. Adding adds 0.3-0.7pp to mobile conversion. 3. **Audit mobile bundle / upsell UX.** Mobile AOV under $100 on a brand with $145 desktop AOV is a fixable gap. 4. **Audit page-load speed on mobile.** Stencil theme JS-heaviness is the most common cause of mobile-conversion underperformance. 5. **Pair with [BC Channel Conversion Rate](/nerve-centre/kpi-cards/bigcommerce/conversion-rate-by-channel)** to see device-specific funnel quality. ## Sibling cards merchants should reference together | Card | Why pair it with Orders by Device | | ----------------------------------------------------------------------------------------------- | --------------------------------------------------- | | [BC Channel Conversion Rate](/nerve-centre/kpi-cards/bigcommerce/conversion-rate-by-channel) | Device-specific conversion analysis. | | [BC Revenue by Device](/nerve-centre/kpi-cards/bigcommerce/revenue-by-device) | Revenue side; device count × AOV. | | [BC Alert Abandoned Cart Spike](/nerve-centre/kpi-cards/bigcommerce/abandoned-cart-spike-alert) | Mobile-specific abandonment is a common pattern. | | [Average Order Value](/nerve-centre/kpi-cards/bigcommerce/average-order-value) | Headline AOV context. | | [BC Orders by Channel](/nerve-centre/kpi-cards/bigcommerce/bc-orders-by-channel-id) | Channel context; web channel breaks down by device. | | [BC Channel AOV](/nerve-centre/kpi-cards/bigcommerce/aov-by-channel) | Per-channel AOV; web AOV is dragged down by mobile. | | [BC Top Coupons](/nerve-centre/kpi-cards/bigcommerce/top-coupons) | Mobile-specific promo cycles. | | [Total Revenue](/nerve-centre/kpi-cards/bigcommerce/total-revenue) | Revenue context for prioritisation. | ## Reconciling against the vendor's own dashboard **Where to look in BigCommerce Control Panel:** BigCommerce **does not natively surface device-level order analytics.** The closest views require GA4 connected: [Analytics → In-store Conversion](https://login.bigcommerce.com/deep-links/manage/analytics/in-store-conversion) shows store-wide; for device split connect GA4 and view in GA4's "Tech → Device category" report. Some BC apps (e.g. Yotpo, Klaviyo dashboards) surface device-level views, but they're third-party. For order-level UA inspection: [Orders → individual order](https://login.bigcommerce.com/deep-links/manage/orders) shows the customer user-agent string in the order details. **Why our device split may differ from GA4:** | Reason | Direction | | -------------------------------------------------------------------------------------------------------------------------------------------- | -------------- | | **UA-Parser version**. Different parsers classify edge-case UAs (older browsers, unusual devices) differently. | Sub-2% gap | | **Tablet classification**. We surface tablet as separate; GA4 typically merges into mobile. Stores comparing should align convention. | Different rows | | **Bot filtering**. We filter known bots; GA4 has its own bot exclusion. | Sub-1% gap | | **Order-time vs session-time UA**. We use the UA at order-completion; GA4 uses session-start UA. For multi-device sessions, the two diverge. | Sub-3% gap | | **Webhook-vs-pageview**. We detect at BC webhook receipt; GA4 detects at JS-tag fire. Different timing windows. | Cosmetic | **Cross-connector reconciliation (when GA4 is connected):** | Card | Expected relationship | What causes legitimate divergence | | -------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | | [`google_analytics.ga_orders_by_device`](/nerve-centre/google_analytics/ga_orders_by_device) | Match within 5-10% | GA4 misses 10-25% of orders (consent / blockers); the gap is consistent across devices. | | [`klaviyo.kl_purchase_by_device`](/nerve-centre/klaviyo/kl_purchase_by_device) | Klaviyo's email-attributed purchases by device align with web-channel device split | Klaviyo subscribes a subset of customers; coverage differs. | **The orders-by-device view is BC-aligned with similar cards on Shopify (per `Browser/UserAgent` parsing) and Adobe Commerce (per `client_ip` UA derivation); merchant-facing semantics are equivalent.** ***
Same-metric documentation cross-reference (for agencies running multiple platforms) * [`shopify.orders_by_device`](/nerve-centre/shopify/orders_by_device) (planned) * [`adobe_commerce.orders_by_device`](/nerve-centre/adobe_commerce/orders_by_device) (planned)
## Known limitations / merchant FAQs **Why is my mobile share so high (60%) but my mobile revenue share only 45%?** Because mobile AOV is lower. 60% of orders × ($98 mobile AOV / $120 headline AOV) = \~50% revenue contribution. The gap reflects basket-size difference, not a metrics issue. **Closing the mobile AOV gap (via mobile-friendly bundle UX) is the highest-leverage revenue lift.** **My mobile conversion rate is 1.4% but desktop is 3.8%. Is mobile broken?** Probably. A 2.5x desktop-to-mobile conversion gap is bigger than the BC ecosystem norm (typically 1.5-2x). Audit (a) page-load speed (Stencil JS-heaviness is the #1 culprit), (b) checkout form friction (number of fields, autofill support), (c) Apple Pay / Google Pay enabled (BC supports both natively, but many merchants haven't enabled them), (d) image-heavy mobile pages slowing down LCP. **Should tablet be counted as mobile or desktop?** Depends on your category. For touch-first UX (fashion, food), tablet behaves like mobile. For research-heavy categories (electronics, B2B), tablet behaves like desktop. We surface separately by default; configure under Settings → Device classification → Tablet-as-mobile if you prefer the merge. **My BC dashboard doesn't show device breakdown. Is this card BC-native?** No, BC's main analytics doesn't natively surface device-level orders. We compute via UA parsing on the order's `customer_user_agent`. **The card adds visibility BC's stock dashboard lacks**, which is one of the reasons it's a high-value card for BC merchants who haven't connected GA4. **Why is my unknown bucket 8%?** Above 5% suggests either (a) bot traffic surviving filters, (b) UA-spoofing customers (some privacy-focused users), (c) older browsers we don't recognise. Audit the UA strings in the unknown bucket; if patterns emerge, we can add classifications. Configure under Settings → Device classification → Custom rules. **My B2B portal shows 80% desktop. Real?** Yes, that's normal. Wholesale buyers use office computers; mobile B2B portal access is rare. **A B2B portal mobile-share above 30% is unusual** and usually means a procurement-app integration (the customer's procurement system makes the order via a mobile-tagged UA). **Does this card include marketplace orders?** No, marketplaces don't expose device. Default scope is web-only (`channel_id = 1`). Marketplace orders are device-blind and excluded. **Why is the alert trigger missing?** Because device shifts are slow and structural, not anomaly-driven. The right alarm is conversion-rate-per-device-drop, which lives on [BC Channel Conversion Rate](/nerve-centre/kpi-cards/bigcommerce/conversion-rate-by-channel) with device decomposition. Configure custom device-conversion alerts under Settings → Custom alerts. **My multi-storefront BC instance has different theme variants per storefront. Does this card aggregate?** By default per-storefront. Each storefront's theme has its own mobile UX; aggregating produces meaningless rollups. Use the storefront filter to compare device performance across storefronts. **Should I implement Apple Pay even if my mobile already converts well?** Generally yes. Apple Pay adds 0.3-0.7pp to mobile conversion across BC stores; the implementation cost is one settings toggle. Even mobile-strong stores benefit. Google Pay similar effect for Android-heavy traffic. *** ### Tracked live in Vortex IQ Nerve Centre *Orders by Device* is one of hundreds of KPI pulses Vortex IQ tracks across BigCommerce and 70+ other ecommerce connectors. Nerve Centre runs the detection layer; Vortex Mind investigates the cause when something moves; Ask Viq lets you interrogate any number in plain English. [Start for free](https://app.vortexiq.ai/login) or [book a demo](https://www.vortexiq.ai/contact-us) to see this metric running on your own data.