Estimated revenue exposed to refund / chargeback / churn from late deliveries this week. Hero - the finance-CFO wake-up call.
At a glance
Estimated revenue exposed to refund, chargeback, and churn from late DPDLocal deliveries this week. Joins the late-shipment list to commerce-sibling order totals and applies a refund-propensity multiplier per merchant baseline. The CFO’s wake-up call number, the moment OTD becomes a finance metric.
| What it counts | SUM(commerce_sibling.order.total) WHERE order_ref IN (dpdlocal.shipment WHERE actual_delivery > expected_delivery + 24h) × refund_propensity_pct. The “+24h” buffer excludes mildly-late deliveries that rarely refund. |
| Refund propensity | Per-merchant baseline computed from the trailing 90-day refund-rate-on-late-cohort vs all-cohort refund rate. Default 12 to 25% for fashion DTC, 5 to 10% for supplements / health, 2 to 5% for B2B. Configurable per workspace. |
| Currency | Merchant base currency (typically GBP for DPDLocal-native UK merchants). |
| What it excludes | Mildly-late deliveries (within 24h grace), B2B contract orders with no refund right, and orders already refunded (those are realised, not at-risk). |
only_when: has_commerce_sibling | Card only renders when a commerce connector (Shopify / BC / Adobe / SFCC) is also live. |
| Time window | 7D (the actionable window, the CFO’s “this week” view) |
| Alert trigger | >£1000. Default; tune per merchant size. |
| Roles | owner, finance, marketing |
Calculation
Calculated automatically from your DPDLocal 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 UK fashion DTC merchant on Shopify, AOV £85, refund propensity baseline 18% on late-cohort orders. Reading 12 Mar 26, trailing 7 days.| Late shipment cohort | Count | Sum order_total | Refund propensity | Revenue at risk |
|---|---|---|---|---|
| Late by 24-48h | 142 | £12,070 | 18% | £2,173 |
| Late by 48-72h | 31 | £2,635 | 28% (raised on severity) | £738 |
| Late by 72h+ | 9 | £765 | 45% | £344 |
| Total at risk (this card) | 182 | £15,470 | weighted ~21% | £3,255 |
>£1000 is firing. Three things to notice:
- The CFO sees a £3,255 number, not a 96% OTD percentage. This is the right framing for finance. The OTD card is for operations; this card is for the CFO and the finance partner.
- Severity-weighted propensity matters. Parcels late by 72+ hours refund at roughly 2.5x the rate of 24-48h-late parcels; the simple flat propensity understates the risk on the worst tail.
- The pre-emptive intervention budget is £500 to £900. Even a 30% reduction in refund rate from the late cohort (via “we know your parcel is late, here’s a £10 voucher” outreach via Klaviyo) saves more than the voucher cost on this exposure.
Sibling cards merchants should reference together
| Card | Why pair |
|---|---|
| Late Shipments | The count behind the £ exposure. |
| On-Time Delivery Rate | The percentage view; aligns operations and finance frames. |
| Carrier OTD by Sales Channel | Splits the £ exposure by channel. |
| Claim Value as % of DPDLocal Revenue | Realised cost on the carrier; this card is the at-risk forecast. |
Cross-connector: shopify.refund_rate | Validates the refund-propensity baseline. |
Reconciling against the vendor’s own dashboard
Where to look: This is a Vortex IQ-derived value. DPDLocal does not produce it (DPDLocal does not see commerce-side order totals); the commerce platform produces refund-rate-on-late-cohort but does not see the DPDLocal late list. The join is the card. Why retroactive reality may differ from the at-risk forecast:| Reason | Direction | Why |
|---|---|---|
| Refund propensity baseline drift | Either | The 18% (or per-merchant) baseline is from trailing 90 days; if customer behaviour has shifted, the forecast under- or over-shoots. Re-baseline quarterly. |
| CS intervention | Realised lower | Pre-emptive outreach typically cuts realised refund rate by 25 to 45%; the at-risk forecast assumes no intervention. The gap between forecast and realised is the value of the CS programme. |
| Order cohort heterogeneity | Either | A single high-AOV order in the late list dominates the £ exposure; cohort variance can be high week to week. |
| Card | Expected relationship |
|---|---|
| Realised refund value attributable to late cohort (commerce-sibling, lagged 14 days) | Should land within ±30% of this card’s forecast. Persistent under-realisation suggests CS intervention is working; persistent over-realisation suggests baseline propensity needs to be raised. |