This report is built from anonymized order and revenue data captured across Shopify stores in Shogun's network. A store is included if it recorded at least 250 orders during the reporting period. Stores with an average order value above $5,000 are excluded as non-representative of the mainstream ecommerce market.

Benchmark figures (medians, means, and percentile ranges) reflect 2,934 active stores in H1 2026, January through June. Quarter-over-quarter movement figures compare only stores active in both Q1 and Q2 2026 with at least 125 orders in each quarter. Year-over-year figures compare only stores active in both H1 2025 and H1 2026. Within-quarter monthly trend figures compare only stores active in all three months of Q2 with at least 50 orders per month. All matched comparisons hold the merchant cohort constant to isolate real changes in basket size from shifts in cohort composition.

Industry categories are derived from Storeleads data using Google Product Taxonomy classifications. Revenue bands are based on Storeleads estimated annual GMV. Both are third-party estimates and may not reflect exact reported figures.

Across 2,934 active stores in H1 2026, the median AOV was $312 and the cohort mean was $607. The middle 50% of stores fell between $159 and $667. A "good" AOV depends heavily on your industry — Autos & Vehicles stores median at $652, while Food & Beverage stores median at $172. Use your industry-specific median as the right reference point, not the cross-industry average.

AOV equals total order value divided by total orders. We calculate it at the store level — summing each store's orders and order value across every month in the reporting period, then dividing the totals — rather than averaging each month's AOV separately. A store's monthly AOV figures are never averaged together; the period figure is always recomputed from summed orders and order value.

The median across our 2,934-store H1 2026 dataset is $312. The mean is $607 — roughly double the median — pulled higher by a persistent right tail of high-AOV merchants, particularly in Autos & Vehicles, Business & Industrial, and Consumer Electronics. Both are useful: the median describes the typical merchant, the mean describes the cohort average skewed by outliers. For benchmarking your own store, use the median.

Autos & Vehicles (median $652), Business & Industrial (median $428), and Apparel & Accessories (median $418) led the dataset in H1 2026. These are considered-purchase or higher-unit-price categories where customers commit to fewer, larger transactions.

Food & Beverage (median $172), Health & Wellness (median $177), and Beauty & Personal Care (median $270) had the lowest medians in H1 2026. These are repeat-purchase categories with lower per-unit prices — and, notably, they're also the categories where conversion rates tend to run highest.

Up, on both a quarterly and annual basis. Same-store matched AOV grew 4.6% from Q1 to Q2 2026 ($297 to $311), with eight of eleven industries gaining ground. Year over year, same-store matched AOV rose 7.8% ($314 to $339 median), with eight of nine reported categories posting gains. Within Q2 itself, the trend softened slightly: $317 in April, $305 in May, and $304 in June.

Start with your industry's own median, then work toward its P75 — cross-industry comparisons are usually the wrong benchmark, since a $300 AOV can be strong for one category and weak for another. Revenue scale matters too: enterprise stores ($50M+ GMV) post a $399 median AOV, 51% higher than stores below $15M ($264), largely reflecting product mix, bundling, and upsell maturity rather than traffic differences.

Median. It's the right number for "what does a typical merchant look like" and isn't pulled around by a handful of very high-ticket outliers. The mean is useful as a diagnostic — in this dataset it runs roughly double the median in every category, which signals a persistent right tail rather than a broad-based shift. When mean and median move together, the trend is broad-based; when they diverge, the cohort shape is changing. For setting your own targets, use median.

Not on its own. Our data shows an inverse pattern: the categories with the highest conversion rates — Health & Wellness, Beauty & Personal Care, Food & Beverage — tend to have the lowest AOVs, and vice versa for Autos & Vehicles and Business & Industrial. Apparel & Accessories is a notable exception, ranking well on both. AOV and conversion rate are both levers on revenue, and optimizing one in isolation can work against the other — the more complete picture is revenue per customer, not AOV alone.

No, not directly. The AOV data here comes from full Shopify order data across all tracked merchants, while the conversion rate benchmark is based on Shogun-instrumented session data — a different and non-overlapping universe of stores and traffic. Multiplying conversion rate by AOV across these two reports would not produce an accurate revenue-per-session figure. Treat them as separate benchmarks answering separate questions.