At a glance
Average Order Value across all orders in the period: SUM(grand_total) / COUNT(orders). AOV on Adobe Commerce typically runs higher than Shopify/BigCommerce equivalents due to the B2B mix; a typical Adobe Commerce merchant sees blended AOV of 400 vs Shopify’s 150. Movements in AOV reveal product-mix changes, promotional impact, and B2B/B2C segment shifts.
Calculation
Worked example
A specialty kitchenware retailer on Adobe Commerce 2.4.6 with US, UK, and B2B Store Views. Trailing 30-day window ending Monday 4 May 26. Blended AOV (all Store Views, all customer types):
Per-segment breakdown:
Per-Store-View breakdown:
What this is telling the merchant:
- Blended AOV grew 5.6% but the headline obscures the segment story.
- B2B AOV grew 3.9%: healthy, accounts buying slightly larger baskets.
- B2C AOV dropped 2.3%: smaller consumer baskets. Could be promotional drag, smaller-basket demographic shift, or a shipping-fee change that pushed customers to single-item orders.
- The blended AOV is rising despite B2C contracting because B2B order count grew (412 trailing vs 386 prior, +6.7%); more B2B mix in the average pulls it up.
- The composition trap: a leadership view that watched only blended AOV would conclude “AOV is up, all is well”. The segmentation reveals B2C contraction that warrants its own action plan.
- Cross-checking B2B Revenue Share: B2B share rose from 47% to 53%. Confirms the mix shift driving blended AOV.
- Cross-checking Discount %: discount% on B2C is up 2.4 pts, pulling AOV down on that segment. A spring sale is running. Once the sale ends, B2C AOV should recover.
- Action: extend the discount monitoring window, set a checkpoint to re-measure B2C AOV in the 30 days after the sale ends. If it doesn’t recover, the issue is structural rather than promotional.
Sibling cards merchants should reference together
AOV is a composite; the value comes from the cards that decompose it.Reconciling against the vendor’s own dashboard
Where to look in Adobe Commerce Admin:Reports > Sales > Orders with the date range set to “Last 30 days”. The “Average” column shows AOV for the period. Adobe also displays AOV on the Dashboard > Lifetime Average Order Value tile, but that tile is all-time, not 30-day.For per-Customer-Group AOV (manual computation needed):
Reports > Sales > Orders with Customer Group filter applied (Adobe Commerce 2.4.4+). Sum the totals and divide by the order count.For per-Store-View:
Switch the scope selector at the top-left to a single Store View, then Reports > Sales > Orders.Why our number may legitimately differ from Adobe Admin:
Internal identity (within Adobe Commerce):
adobe_commerce.aov = total_revenue ÷ order_count
Component cards (these should be self-consistent, if they’re not, it’s a sampling or rounding issue, not real divergence):
Cross-connector reconciliation (when both connectors are connected for this merchant):
These connectors view the same orders/sessions/transactions through different lenses. Numbers should agree within tracking-gap accuracy. Divergence is a data-quality signal worth investigating.
Expected relationship: GA4 AOV ≈ Adobe AOV × (1 - tracking gap). GA4 typically misses 10 to 25% of orders (ad blockers, consent rejection, tag-fire failure on slow pages). The orders missed by GA4 tend to be a representative sample, so the AOV figure should match within 2 to 5%. Material divergence (>10%) signals one of: (a) GA4 is missing high-AOV B2B orders (B2B portal customers often run private/incognito for security, blocking GA tags), in which case GA4 AOV is biased low; (b) GA4 is double-counting on multi-step checkout (purchase event firing twice), biasing high; (c) currency mismatch: GA4 reports in the property’s reporting currency, Adobe in base_grand_total.
Known limitations / merchant FAQs
Why is my Adobe Commerce AOV so much higher than industry benchmarks for ecommerce? Adobe Commerce skews toward complex use cases: B2B, multi-store, custom-attribute-driven catalogues, hospitality and industrial verticals. Industry “ecommerce AOV” benchmarks are dominated by Shopify-class pure DTC consumer brands (120 typical). A typical Adobe Commerce store with any B2B mix runs 400 blended; pure B2B easily 2,500. Use category-and-edition-matched benchmarks (B2B distribution vs DTC fashion), not generic ecommerce benchmarks. Adobe Commerce vs Magento Open Source: any AOV difference? None at the calculation level; both editions usegrand_total identically. The merchant-mix difference matters: Adobe Commerce paid edition is overwhelmingly used by larger merchants (and B2B-heavy merchants), so the average Adobe Commerce installation has higher AOV than the average Open Source installation. This is a customer-base effect, not an edition difference.
My multi-store AOV is misleading because of currency mixing, what should I do?
The card defaults to base_grand_total (FX-converted at order time) for cross-currency rollup. For per-currency views, configure per-Store-View variants. Don’t rely on grand_total for multi-currency comparison; FX swings from order time to display time make the figure unreliable.
My B2B AOV is much higher than B2C, will the blended AOV always trend with B2B mix?
Yes. Blended AOV is a weighted average; the higher-AOV segment (B2B) has outsized influence. A 5pt mix shift toward B2B can move blended AOV by 30%+ even if neither segment’s own AOV changed. The correct interpretation: blended AOV is a mix-mix indicator, not a quality-of-customer indicator. For quality, use per-segment AOV from B2B AOV vs B2C AOV.
Why include pending_payment orders in AOV?
They are genuine pipeline; B2B net-30 specifically. Excluding them would understate AOV (since net-30 orders skew large) and create false volatility (when a batch of pending orders flips to processing, AOV would jump). Including them gives a stable cash-pipeline view.
Why is AOV gross of refunds rather than net?
Two reasons. First, refunds happen days to weeks after the order, so net-of-refund AOV is always a backward-looking moving target. Second, the act of refunding is a separate operational signal tracked in Refund Rate; folding it into AOV obscures both signals. For a net view, divide net Total Revenue by Order Count manually.
A flash sale ran for 3 days, AOV dropped 8%, is that a problem?
Probably not. Promotional AOV drag is expected behaviour: discounts pull grand_total down by definition. The diagnostic question is whether the volume lift compensated. Cross-check Total Orders: if order count rose more than AOV fell, total revenue is up. Cross-check Total Revenue directly. If revenue is up despite AOV falling, the campaign is working.
Why does AOV rise on weekends but Total Revenue falls?
Common pattern on B2B-heavy stores. Weekend orders are mostly consumer (smaller AOV would suggest revenue should drop) but B2B accounts who DO order on weekends tend to be operations-team-driven emergency orders (high AOV). The mix shift toward fewer, larger orders pushes the average up while the volume push down on revenue. The pattern is healthy mix variation, not a problem.
My multi-store Adobe Commerce, AOV varies 5x across regions, normal?
Yes. Region-level AOV differences reflect (a) tax-inclusive pricing vs exclusive (UK +20% VAT, US varies), (b) shipping fee structures, (c) regional product mix (the UK store may stock lower-priced lines), (d) currency-strength effects. A 5x variance suggests US is mostly B2B and UK is mostly consumer (or vice versa); check Store View routing and segment classification.
Should I optimise for AOV or for Total Revenue?
Total Revenue. AOV is a means, not an end. Pushing AOV by raising minimum-order-value thresholds reduces volume; the net revenue impact is what matters. The exception: when AOV is dropping and volume is flat (the bad mix-shift case), the AOV signal is the indicator that something needs fixing.