> ## 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. # AOV by Chinese Province, JD.com > AOV by Chinese Province for JD.com stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Card class:** [Non-Hero](/nerve-centre/overview#card-classes-explained) • **Category:** [Marketplace](/nerve-centre/connectors#connectors-by-type) ## At a glance > **AOV by Chinese Province** breaks your average order value down across the Chinese provinces, municipalities and autonomous regions your JD.com buyers ship to. China's regional markets are not uniform: tier-1 cities like Beijing, Shanghai and the Guangdong coast tend to carry higher basket values than inland and western provinces, and provincial demand patterns shift the mix of what people buy. Reading AOV by province tells you where your premium buyers are, where bundling or free-shipping thresholds could lift baskets, and where regional pricing or assortment is leaving money on the table. | | | | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **What it counts** | Average order value (total order revenue divided by order count) split by the buyer's shipping province across mainland China. | | **Sample type** | Backend API data from JD.com, aggregated by province over the trailing window and refreshed on the standard data refresh. | | **Why it matters** | Provincial AOV reveals where your high-value demand sits and where baskets are thin. It guides regional free-shipping thresholds, bundle offers, and assortment decisions, and it explains shifts in blended AOV that a single national number hides. | | **Reading the value** | Compare provinces against each other and against the national average. A province well below average may respond to bundling; one well above may justify premium assortment. Track movement period over period. | | **Currency** | currency (CNY) | | **Time window** | `30D` | | **Alert trigger** | `-` | | **Sentiment key** | `jd_aov_by_province` | | **Roles** | owner, marketing | ## Calculation For each Chinese province over the trailing 30-day window, the card divides total order revenue (in CNY) by the number of orders shipped to that province, giving a per-province average order value. Provinces are derived from the buyer's shipping address, so an order is attributed to the destination region rather than the buyer's registered address. The bar chart ranks provinces so the highest and lowest baskets are immediately visible against the blended national AOV. ## Worked example *A representative reading for a JD.com seller over the 30 days to 20 Mar 26.* The national blended AOV sits at CNY 312. The province bars tell a richer story: Shanghai leads at CNY 468 and Beijing at CNY 441, both well above the national figure, driven by larger multi-item baskets. Guangdong, the highest by order volume, sits close to the national average at CNY 305. Inland provinces run lower - Henan at CNY 238 and Yunnan at CNY 221 - where buyers more often place single-item orders. The action is regional: a free-shipping threshold set just above the inland single-item price could nudge Henan and Yunnan baskets upward, while the strong Shanghai and Beijing baskets justify featuring premium bundles to those regions. To explore why a province's AOV shifted between periods, use Vortex Mind; to ask which products drive the high Shanghai basket in plain English, use Ask Viq. ## Sibling cards merchants should reference together | Card | Why merchants reach for it | | ------------------------------------------------------------------------------------ | ---------------------------------------------------------------- | | [`jd_aov_cny`](/nerve-centre/kpi-cards/jd-com/average-order-value-cny) | The blended national AOV this card decomposes by province. | | [`jd_orders_by_province`](/nerve-centre/kpi-cards/jd-com/orders-by-chinese-province) | Order volume by province, the denominator behind provincial AOV. | | [`jd_total_revenue`](/nerve-centre/kpi-cards/jd-com/total-revenue) | Total revenue that provincial AOV and volume combine to form. | | [`jd_orders`](/nerve-centre/kpi-cards/jd-com/orders) | Total order count across all provinces. | | [`jd_revenue_over_time`](/nerve-centre/kpi-cards/jd-com/revenue-over-time) | Revenue trend that regional AOV shifts feed into. | ## Reconciling against the vendor's own dashboard **Where to look in JD.com's own dashboard:** JD's seller analytics include regional and geographic breakdowns of orders and sales, usually under a buyer-distribution or regional-sales report. AOV is typically derived there rather than shown directly, so reconcile by checking that the per-province revenue and order counts match before comparing the computed average. **Why the Vortex IQ value may legitimately differ:** | Reason | Direction | What to do | | ---------------------------------------------------------------------------------------------------------------------------------------- | --------- | -------------------------------------- | | **Province attribution.** Vortex IQ attributes by shipping destination; the vendor view may split by billing or buyer-registered region. | Variable | Confirm the attribution basis matches. | | **Period boundary.** Vortex IQ uses a 30-day rolling window; vendor reports may use calendar months. | Variable | Match the period range. | | **Order definition.** Cancelled or refunded orders may be treated differently in the AOV denominator. | Variable | Align the order-status filter. | **Cross-connector reconciliation:** read this alongside orders by province and the blended AOV card so volume and basket size are interpreted together. For divergence investigations, use Vortex Mind. ## Known limitations / merchant FAQs **Q: How often does AOV by Chinese Province update?** The card refreshes on the standard data refresh (typically every 30-60 minutes for live integrations). The 30-day window means individual orders have a small marginal effect on each province's average. **Q: A small province shows a wild AOV. Why?** Provinces with very few orders in the window produce a high-variance average, so a single large or small basket can swing the figure. Read low-volume provinces alongside the orders-by-province sibling card and treat their AOV as indicative rather than precise. **Q: Why does my JD dashboard show a different provincial split?** The usual reasons are attribution basis (shipping destination vs registered region), period boundary (30-day rolling vs calendar month), and how cancelled or refunded orders are handled in the average. Match these before assuming a real divergence. **Q: Can I customise the alert threshold?** This card is a comparison view and ships without an alert by default. If you want to be alerted when a key province's AOV drops below a baseline, configure a threshold per profile in the Sensitivity tab. *** ### Tracked live in Vortex IQ Nerve Centre *AOV by Chinese Province* is one of hundreds of KPI pulses Vortex IQ tracks across JD.com 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.