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Metrics type: Cross-Platform Metrics • Category: Shipping & Courier
Estimated revenue exposed to refund / chargeback / churn from late deliveries this week. Key Metric - the finance-CFO wake-up call.

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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.

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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.

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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. The card reads £3,255. The alert at >£1000 is firing. Three things to notice:
  1. 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.
  2. 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.
  3. 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.

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Sibling cards merchants should reference together

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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: Cross-connector reconciliation:

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Known limitations / merchant FAQs

Why use a refund-propensity multiplier instead of just summing the late-cohort revenue? Not every late delivery causes a refund; merchants who refund every late parcel would have a far worse refund rate than they actually do. The propensity adjusts for the fact that customer tolerance, severity, and CS intervention all dampen realisation. Without the multiplier the card would dramatically overstate. The propensity baseline doesn’t match my finance team’s number. Adjust it. The default baseline is computed from your own trailing-90-day data; if you have an internal CFO model with a different rate, configure it in the connector. The card is meant to surface the magnitude, not be the source of truth for finance. Should I include B2B orders in this calculation? Default no. B2B trade orders typically have contracted delivery windows with no consumer refund right; including them inflates the at-risk number meaninglessly. The default filter excludes B2B-tagged orders; toggle on if your B2B contracts include service-credit clauses. How does the card change with a CS intervention programme running? The forecast number stays the same (it’s the unmitigated risk); the realised number drops 25 to 45%. Compare forecast vs realised over 30 days to quantify the programme’s value. The number jumps up and down weekly. Why? Two reasons. (1) Cohort heterogeneity. A single £500 high-AOV late delivery moves the number more than 50 small ones. (2) Severity distribution. A week with mostly 24-48h-late vs a week with mostly 72h+-late produces very different propensities even at similar counts. Read the trend over 4 weeks rather than week-to-week. Can I drill into the late-cohort orders? Yes, click any segment of the card to see the underlying order list with order_ref, customer email, and the specific late-by-X-hours measurement. Useful for CS pre-emptive-outreach lists.

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Tracked live in Vortex IQ Nerve Centre

Late-Delivery Revenue at Risk is one of hundreds of KPI pulses Vortex IQ tracks across DPDLocal 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.