At a glance
Total revenue split by customer device (mobile / tablet / desktop) at order placement, captured from Storefront API order metadata plus server-side user-agent inference. Critical for prioritising mobile-vs-desktop checkout investment, mobile is over 60% of traffic on most BC stores but often 40-50% of revenue; that gap is your optimisation target.
| What it counts | SUM(total_inc_tax) GROUP BY device_type over the period, where device_type ∈ {mobile, tablet, desktop, unknown}. Classification comes from Storefront API order metadata or server-side user-agent inference at checkout. |
| VAT / tax treatment | Tax-inclusive (total_inc_tax). |
| Shipping | Included. |
| Discounts | Already deducted. |
| Refunds | Not deducted (gross). |
| Cancelled / voided orders | Included. |
| Currency | Multi-currency without FX. Filter by currency for clean comparison. |
| Channels / sources | Web channel only by default (channel_id = 1). POS orders have no device dimension; marketplace orders set device_type = unknown. |
| Device classification gotcha | Best-effort signal. Tablets occasionally mis-classify as desktop (iPad Pro with desktop-mode); mobile in-app browsers (Instagram, Facebook) classify as mobile but behave differently from native browsers. |
| Reconcile against GA4 | Cross-reference google_analytics.ga_revenue_by_device; BC and GA4 should agree to ±5-10%. |
| B2B Edition note | B2B portal orders default to desktop unless using a mobile field-sales app. B2B AOV is typically much higher. |
| Time window | 30D (rolling 30 days, vs prior 30 day comparison) |
| Alert trigger | None on this card directly. |
| 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 channel only. 30-day window 14 Apr 26 to 14 May 26.| Device | Sessions | Conversion rate | Orders | AOV | Revenue | Share | vs prior 30d |
|---|---|---|---|---|---|---|---|
| Mobile | 184,000 | 1.42% | 2,613 | $58 | $151,554 | 38.5% | +6.2% |
| Tablet | 22,000 | 2.01% | 442 | $74 | $32,708 | 8.3% | +1.4% |
| Desktop | 88,000 | 3.84% | 3,379 | $62 | $209,498 | 53.2% | +2.8% |
| Unknown | n/a | n/a | 24 | $51 | $1,224 | 0.3% | n/a |
| Total | 294,000 | 2.20% | 6,458 | $60.95 | $394,984 | 100% | +3.8% |
- Mobile has 63% of sessions but only 39% of revenue. Classic mobile-conversion gap. Desktop converts 2.7× better than mobile (3.84% vs 1.42%); even though desktop traffic is half of mobile, it generates more revenue. The optimisation target: closing the mobile conversion gap from 1.42% to 2.0%+ would add ~$60k/month of revenue at no traffic cost.
- Tablet is the highest-conversion segment (2.01% on a smaller base). Tablet users are typically older, more deliberate, on a couch with WiFi, less abandonment, higher AOV (58 mobile). Don’t ignore tablet UX; the conversion rate justifies attention.
- Mobile AOV at 62 is a small gap. Many BC stores see a much wider AOV gap (mobile -25% vs desktop) because of one-handed scrolling, fat-finger errors, and inability to comfortably review cart. A $4 gap suggests this merchant has done good mobile-cart work; the conversion gap is the bigger opportunity.
- Mobile growth at +6.2% on a 38% share means mobile is the marginal growth engine. Desktop +2.8% is decelerating relative to traffic share. The gap will close over time; planning should assume mobile is the primary surface within 12-18 months.
- The “Unknown” bucket at 0.3% is acceptable noise. If unknown rises above 2%, the storefront’s device-detection script may have broken, audit the Storefront API integration.
- Mobile checkout audit this week. Common wins: Apple Pay / Google Pay one-tap (typically +20-40% mobile conversion), shorter form fields, sticky cart summary, address autocomplete.
- Mobile PDP audit this week. Big-button hero image, clear price + Add to Cart above the fold, sticky add-to-cart bar on scroll.
- Mobile speed test. Run PageSpeed Insights on top-3 product pages; aim for LCP <2.5s on 4G mobile.
- A/B test one mobile UX change per month, measure the conversion-rate delta in this card.
- Desktop holdover, don’t sacrifice desktop conversion for mobile gains; track both side-by-side after each change.
Sibling cards merchants should reference together
| Card | Why pair it with Revenue by Device |
|---|---|
| BC Orders by Device | The order-count split. Combined with this card gives you the per-device AOV. |
| Conversion Rate | Per-device conversion rate; the mobile-vs-desktop gap is the optimisation lever. |
google_analytics.ga_revenue_by_device | The GA4-side cross-check; should agree to ±5-10%. |
google_analytics.ga_lcp_mobile | Mobile page-speed; correlates strongly with mobile conversion gap. |
| BC Channel Conversion Rate | Per-channel conversion; web-channel split typically follows the device split. |
| Total Revenue | The denominator. |
| BC Revenue by Hour | Time × device cross-tabs reveal evening-mobile vs business-hours-desktop patterns. |
shopify.aov | Cross-platform reference for device-stratified AOV reporting. |
Reconciling against the vendor’s own dashboard
Where to look in BigCommerce Control Panel: Analytics → In-Store Conversion on Pro / Enterprise plans includes a device split for sessions and conversions; revenue-by-device is implied but not directly displayed. The cleanest reconciliation source is GA4, not BC’s native analytics. For the merchant authoritative view of mobile experience, run PageSpeed Insights on key pages; mobile LCP and CLS scores are leading indicators of the mobile conversion gap. Why our number may legitimately differ from BC / GA4:| Reason | Direction |
|---|---|
| Tablet classification. We classify iPads as tablet by default; some GA4 configurations group them with desktop, others with mobile. | Different shapes by default; configurable |
| In-app browsers. Instagram / Facebook in-app browsers classify as mobile; Pinterest’s in-app browser sometimes classifies as desktop. The exact ratio varies. | ±2-3% differences |
| Server-side fallback. When Storefront API metadata is missing, we infer device from user-agent. This inference is ~95% accurate vs the explicit metadata. | Small noise |
| GA4 sampling. GA4 samples high-traffic stores; this card uses 100% of orders. | Vortex IQ generally HIGHER counts |
| Time-zone. BC uses store time zone; we use UTC. | Boundary differences |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
google_analytics.ga_revenue_by_device | Should match within ±5-10%. | GA4 misses 10-25% of orders to ad blockers / cookie rejection. |
google_analytics.ga_sessions_by_device | Sessions split should match the order-count device split closely. | Bot traffic in GA4 sessions; we count only orders, never sessions. |
device_type on Order metadata) and Adobe Commerce (similar metadata); semantics are equivalent across platforms.
Known limitations / merchant FAQs
Why is mobile revenue so much lower than mobile traffic share? The mobile conversion gap. Most stores see mobile convert at 40-60% the rate of desktop. Causes: smaller screens make product evaluation harder, fat-finger checkout errors, reluctance to enter card details on a phone (improving but still real), one-handed scrolling discouraging cart review. The optimisation is closing the gap, not changing the traffic mix. My mobile share is rising fast, what should I prioritise? (1) Enable Apple Pay / Google Pay; one-tap mobile pay typically lifts mobile conversion 20-40%; (2) audit mobile checkout for unnecessary fields; (3) add a sticky add-to-cart on mobile PDPs; (4) test mobile-specific imagery (square crops outperform widescreen on phones). Why does my GA4 show a different device split? Several reasons: GA4 misses 10-25% of orders (ad blockers, cookie rejection); GA4 samples high-volume stores; tablet classification differs (iPad sometimes desktop in GA4); time-zone alignment. Within ±10% is normal; >15% gap suggests a tagging or sampling problem. Should I run separate ad campaigns per device? Generally yes, mobile-specific ad creative (vertical video, square images) outperforms reused desktop assets. But measure carefully: per-device campaigns split your budget and reduce machine-learning training data. For stores under 500 orders/month, run one campaign and let the platform optimise; above that, split. My in-app traffic (Instagram, TikTok) classifies as mobile but converts terribly, what’s happening? In-app browsers have known issues: limited cookie persistence (you log out between visits), restricted JavaScript (some checkout features fail silently), inability to launch Apple Pay / Google Pay reliably. The “open in Safari” pattern (deeplink to native browser) typically lifts in-app conversion 2-3×. Add an “Open in Safari” prompt to your social-traffic landing pages. Why is desktop conversion so much higher? Desktop sessions are typically longer (more research time), customers more likely to be on a familiar device with stored payment, larger screens make product evaluation easier, less abandonment from accidental navigation. Desktop is your highest-intent surface; don’t over-optimise it for mobile-style brevity. My tablet share is unusually high (>15%), what does it mean? Often signals an older customer demographic (tablet skews 40+) or a “lean back” consideration product (homewares, art, premium fashion). Tablet conversion rates are typically the highest of any device; if your tablet share is high and converting well, lean into it (better tablet imagery, premium brand polish). My desktop revenue is falling while mobile is flat, healthy or worrying? Likely structural shift to mobile across the industry; this is the long-term trend. Concerning if total revenue is falling; otherwise it’s just mix shift. Track total revenue for the headline; this card for the mobile-experience investment decision. Can I exclude marketplace traffic from this card? By default the card is web-channel only (marketplace orders have unknown device). To explicitly limit to web, filterchannel_id = 1. Useful for measuring storefront-checkout improvements specifically.
My B2B portal shows 100% desktop, expected?
Mostly yes for B2B Edition merchants; B2B buyers typically order from desk-bound work computers. If you have field-sales staff using a B2B mobile portal app, you’ll see some mobile share. A sudden mobile B2B lift may indicate a new field-sales workflow rolling out.
Should I disable mobile if it converts so poorly?
Absolutely not. Mobile is the surface customers discover you on; even with poor mobile conversion, mobile traffic that bounces to desktop later still converts on desktop and gets attributed to desktop in this card. Disabling mobile would kill discovery. The lever is closing the gap, not abandoning the surface.