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Card class: Non-HeroCategory: Marketplace

At a glance

Days Since Last Payout is a simple cash-flow watchdog: how many days have passed since JD last settled money into your account. JD pays POP marketplace sellers on a settlement cycle, and a healthy store sees that clock reset regularly. When the counter climbs past the normal cycle, it is an early warning of a held settlement, an account-standing flag, a banking or compliance hold, or simply a cycle that has stalled. For finance it is the difference between assuming cash is on the way and discovering a freeze a week too late. It sits in the Economics family and reads best beside the pending-payouts, oldest-pending-payout, and net-revenue cards below.
What it countsThe number of days elapsed since the merchant last received a JD settlement payout, counted to the present.
Sample typeBackend API data from JD.com, refreshed on the standard data refresh.
Why it mattersA payout clock that runs longer than the normal settlement cycle is the earliest sign of a held or stalled settlement, an account-standing issue, or a banking hold. Catching it early protects cash flow and gives time to resolve the cause before it compounds.
Reading the valueRead it against your normal settlement cycle. A value at or below the cycle length is healthy; a value climbing past it, especially past the 7-day alert, signals a payout that has not landed when expected.
Currencycount
Time windowRT
Alert trigger>7d
Sentiment keyjd_payout_age
Rolesowner, finance

Calculation

The date of the most recent settled JD payout subtracted from the current date, in whole days. The clock resets to zero each time a new payout settles. Pending or scheduled payouts that have not yet landed do not reset the counter, so the value reflects actual cash received, not promised cash. It is a real-time read, recomputed on every refresh.

Worked example

A representative reading of Days Since Last Payout for a typical merchant on JD.com. Suppose your settlements normally land every 5 to 7 days, so the counter usually sits between 0 and 6. This week it reads 9 and the alert has tripped. Cross-referencing the pending-payouts card shows a sizeable balance accrued and ready, which rules out “no sales to settle” and points to a held or delayed settlement. The likely causes narrow to an account-standing flag, a bank-detail mismatch, or a compliance review. The action is to check JD Seller Centre’s settlement and account-health pages immediately, confirm bank details, and open a ticket if the hold is not self-explanatory, rather than waiting for the next cycle and losing another week. For deeper investigation, use Vortex Mind to correlate the payout gap with account-standing events; for natural-language exploration, ask Ask Viq “has my JD payout cycle slipped past its normal cadence”.

Sibling cards merchants should reference together

CardWhy merchants reach for it
jdc_payouts_pendingEconomics sibling: the balance waiting to settle, which the payout clock should be clearing.
jdc_payout_age_daysEconomics sibling: how long the oldest specific payout has been pending.
jdc_net_revenueRevenue sibling: the net revenue that ultimately feeds settlements.
jdc_fee_totalEconomics sibling: fees deducted before settlement.
jdc_revenue_at_riskEconomics sibling: revenue exposure that a held payout can compound.

Reconciling against the vendor’s own dashboard

Where to look in JD.com’s own dashboard: JD’s Seller Centre settlement and bill views list each payout with its settlement date. The most recent settled date there should match the reset point this card counts from. The account-health page is where a hold or standing flag would explain a stalled counter. Why the Vortex IQ value may legitimately differ:
ReasonDirectionWhat to do
Settled vs scheduled. Vortex IQ counts from the last settled payout; a scheduled-but-unpaid payout does not reset the clock.VariableConfirm the payout has actually settled, not just scheduled.
Time zone. The day count uses China Standard Time; confirm your reporting profile aligns.MarginalConfirm time zone match.
Refresh lag. A payout that settled minutes ago may not have synced; the counter resets on the next refresh.MarginalForce a manual refresh after an expected settlement.
Banking transit. JD may mark a payout settled before it clears your bank; the clock follows JD’s settled date.MarginalAllow for bank transit time separately.
Cross-connector reconciliation: read with the pending-payouts card to tell apart “nothing to settle” from “settlement is held”. For divergence investigations, use Vortex Mind.

Known limitations / merchant FAQs

Q: How often does Days Since Last Payout update? It is a real-time read, recomputed on every refresh (typically every 30-60 minutes for live integrations). When a payout settles, the counter resets to zero on the next refresh. For an immediate reset after an expected settlement, force a manual refresh. Q: Why does my JD.com dashboard show a different number? The usual causes are the settled-versus-scheduled distinction (only a settled payout resets the clock), time-zone alignment, and refresh lag right after a settlement. Match those before assuming a real divergence. Q: The counter is high but I have pending payouts ready. What does that mean? That combination, a high clock plus a healthy pending balance, points to a held or stalled settlement rather than a lack of sales. Check JD’s account-health and settlement pages for a standing flag or bank-detail issue, and open a ticket if needed. Q: Can I customise the alert threshold? Yes, the day threshold (default 7) is configurable per profile in the Sensitivity tab. Set it just above your normal settlement cycle so the alert fires only when a payout is genuinely overdue rather than on routine timing.

Tracked live in Vortex IQ Nerve Centre

Days Since Last Payout 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 or book a demo to see this metric running on your own data.