At a glance
Share of Interlink Express consignments that hit any non-success scan event in the period: failed first attempt, customer-not-home, address issue, refused, damage, “out for delivery, returned to depot”. Exception is broader than “late” because some exceptions ultimately deliver on-time after a re-attempt, and the DPD-group predicted-window infrastructure makes some exceptions invisible to the customer (driver re-routes via the predicted-window engine).
| What it counts | COUNT(DISTINCT shipments WHERE any_event IN ('FAILED_ATTEMPT','REFUSED','RTS','DAMAGED','UNDELIVERABLE','REDIRECT')) / COUNT(shipments) over rolling 30 days. A consignment with multiple exception events counts once. |
| What counts as “exception” | Any non-success scan event in Interlink’s tracking webhook. Carded “Sorry we missed you” is an exception. Customer-not-home is an exception. Refused-at-door is an exception. The DPD-group predicted-window auto-redirect (where a driver re-routes around a not-home recipient via real-time customer SMS) reduces the headline; this is a network-feature advantage. |
| Customer-fault vs carrier-fault | Pooled. Roughly 60 to 70% of premium-tier exceptions are customer-fault (recipient absent, address wrong, refused); 20 to 30% are carrier-fault (driver mis-route, depot delay); 5 to 10% are external (weather, force-majeure). Pair with int_failed_delivery_count for the carrier-fault-only view. |
| Service level scope | All services pooled (Pre10:30, Pre12, Next-Day, 2-Day, Saturday). |
| Money-back-on-late interaction | Customer-fault exceptions do not qualify for service-failure refund. Carrier-fault exceptions on time-definite tiers usually do. The exception count and the refund-eligible-late-count diverge predictably; pair with int_open_claims. |
| Predicted-window advantage | Interlink (DPD-group) inherits the “1-hour predicted window” customer SMS / app comms. Customers who receive the SMS and rebook delivery before the carded attempt happens reduce the exception rate by 0.5 to 1.5 percentage points vs comparable non-predicted-window carriers. |
| B2B vs B2C | Pooled. B2B addresses (corporate reception with named-recipient signature) generate fewer recipient-not-home exceptions. A merchant shifting B2C-heavy will see exception rate creep upward. |
| Time window | 30D vsP |
| Alert trigger | >3% critical, >1% good. Interlink premium tier typically runs 1.0 to 2.5% (slightly below Parcelforce due to predicted-window advantage). |
| Roles | owner, operations |
Calculation
Calculated automatically from your Interlink Express 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 premium-fashion merchant: £210 average-order-value womenswear, mostly Next-Day DTC consumers with a small B2B reseller cohort on Pre12. Brand sets a high bar on customer experience; exception rate is a tracked KPI in monthly stakeholder review. Reading taken at 09:00 GMT on 02 Apr 26 for the trailing 30 days (02 Mar 26 to 01 Apr 26).| Exception reason | Consignments | % of exceptions | % of total volume |
|---|---|---|---|
| Recipient absent (no safe place) | 45 | 38% | 0.9% |
| Address details required | 22 | 19% | 0.4% |
| Refused at door | 18 | 15% | 0.4% |
| Damaged in transit | 12 | 10% | 0.2% |
| Driver mis-route / depot delay | 14 | 12% | 0.3% |
| Auto-redirect via predicted-window | 7 | 6% | 0.1% |
| Total exceptions | 118 | 100% | 2.4% |
- Recipient-absent at 38% is below the carrier average (~50 to 60% is typical). The DPD-group predicted-window SMS reaches customers in time to reschedule, reducing carded-redelivery exceptions. The merchant benefits from the network feature without explicit configuration.
- Refused-at-door at 15% is high for fashion. Customers are receiving and refusing the parcel. Likely causes: (a) sizing-buyer-regret, (b) checkout error / duplicate order, (c) packaging arrived visibly damaged. Cross-reference Shopify cancellation patterns; refused-at-door spike often follows a customer-initiated cancellation that did not reach the warehouse in time.
- Damaged-in-transit 0.2% of total volume is healthy for fashion (under 0.5% benchmark). Premium-fashion packaging tends to be over-engineered (rigid boxes, branded inner-wrap) which protects contents.
- Refund-eligible exceptions are the 14 driver-mis-routes (12%) plus a portion of the damaged-in-transit (case-by-case). That is ~£140 of carriage refund opportunity (14 × £10 Next-Day) plus ~£300 of damage-claim recovery. File within the 14-day window.
- 2.4% exception rate against a 1.0 to 2.5% premium-tier benchmark is in spec. No urgent action; track the refused-at-door cohort because it correlates with downstream refund rate (
shopify.refund_rate). A 1-point refused-at-door rise typically shows up as a 0.3 point Shopify refund-rate rise at 5 to 10 day lag.
Sibling cards merchants should reference together
Exception rate is the broad operational-noise gauge. To triage:| Card | Why pair it with Exception Rate | What the combination tells you |
|---|---|---|
| Failed Deliveries | Subset of exceptions, specifically carrier-fault first-attempts. | Subtract failed-deliveries from exceptions to see the customs/damage/mis-route bucket. |
| On-Time Delivery Rate | Some exceptions still deliver on-time after re-attempt. | High exception + high OTD = re-attempts working; high exception + low OTD = exceptions converting to misses. |
| Open Claims | Refund-eligible carrier-fault exceptions feed claim filings. | If exception rate is 3% and filing rate <30% of carrier-fault exceptions, money on the table. |
| Returned to Sender | Terminal-exception subset. | High exception + rising RTS = re-attempt logic failing. |
| Pre-10:30 Service Promise | Pre10:30 cohort exceptions are most expensive (B2B contract penalties). | Pre10:30 exception cluster = corporate-customer escalation risk. |
| Shipments by Destination | Geographic split. | Cluster geography = depot-level fix. |
Cross-connector: shopify.refund_rate | Downstream impact. | A 1-point exception-rate rise shows up as 0.3 to 0.6 point refund-rate rise at 5 to 10 day lag. |
Cross-connector: apc.apc_exception_rate | Peer UK premium. APC’s structurally similar operating profile. | Useful for shop-around. |
Cross-connector: parcelforce.par_exception_rate | Peer UK premium, different network. | Compare exception rates after calibrating for different scan vocabularies. |
Reconciling against the vendor’s own dashboard
Where to look in Interlink Express’s own dashboard: Interlink Express MyDPD Business → Reports → Exceptions Summary. Filter by All services / Last 30 days. Per-consignment audit at Track and Trace → Filter “Issues”. Why our number may legitimately differ from Interlink’s portal:| Reason | Direction | Why |
|---|---|---|
| Distinct vs event-count | Ours lower | Portal counts each exception event individually; consignment carded twice then delivered = 2 events. The card de-duplicates at consignment level. |
| Auto-redirect handling | Either | DPD-group predicted-window auto-redirects (driver re-routes via real-time customer SMS) may or may not register as exceptions in the portal depending on whether the original attempt scanned. |
| Customer-fault inclusion | Ours higher | Account-team sometimes quote “carrier-fault-only” rate. The card includes all exceptions because all affect customer experience. |
| Time zone | Boundary | UK local time on both sides. |
| Card | Expected relationship | What causes legitimate divergence |
|---|---|---|
shopify.refund_rate | Downstream lag. | App-install events, B2B / pre-order workflows. |
apc.apc_exception_rate | Peer. | Different consignments. |
parcelforce.par_exception_rate | Peer, different network. | Different consignments, different scan vocabulary. |
Known limitations / merchant FAQs
Why is Interlink’s exception rate typically lower than Parcelforce’s or APC’s? The DPD-group predicted-window infrastructure: customers receive a 1-hour-window SMS or app notification on the morning of delivery, with the option to rebook before the driver arrives. This shifts a portion of would-be carded-redelivery exceptions out of the count entirely. Parcelforce and APC have less mature predicted-window features and therefore see higher recipient-absent exception rates. The auto-redirect-via-predicted-window count is rising; is that good or bad? Good in absolute terms (the customer rerouted before failing), modestly bad in relative terms (it indicates the customer was not at the original address). Operationally it is a net win: the consignment delivers, the customer is happy, and the merchant is not paying for a redelivery. Track it as a leading indicator of customer-mobility patterns. Refused-at-door is climbing on our fashion brand; is it a sizing issue? Often. Fashion refused-at-door correlates with sizing-buyer-regret (customer realises in transit they ordered the wrong size and refuses to accept). Cross-reference Shopify “cancelled in transit” patterns; if there is a pre-shipped cancellation cohort that did not reach the warehouse in time to halt dispatch, this is the root cause. Solution: improve mid-funnel size-guidance and reduce checkout-to-cancel velocity. Damaged-in-transit looks lower on Interlink than other carriers; why? DPD-group sortation centres are heavily automated, with relatively gentle parcel handling vs hand-sorted networks. Damaged-in-transit on Interlink typically runs 0.1 to 0.3% of total volume, lower than APC (0.3 to 0.5%) and Parcelforce (0.2 to 0.4%). For glass/fragile cohorts, Interlink is often the carrier of choice. Customer-fault exceptions: should we filter them out of the headline? Today no; the card includes all exceptions because all affect customer experience and CS-team load. If you need the carrier-fault-only view for refund-recovery sizing, useint_failed_delivery_count plus int_open_claims.
During Q4 peak, exception rate spikes; is this signal or noise?
Both. Q4 saturation lifts UK premium-carrier exception rate by 1 to 2 percentage points. Interlink’s automation footprint holds up slightly better than smaller competitors (sortation centres scale up); expect a 1 to 1.5 point Q4 dip rather than the 2 to 3 point dip on hand-sorted networks. Read Q4 numbers in seasonal context.
B2B reseller exceptions are near-zero; why?
Predictable corporate addresses, named-recipient sign-off, business-hours-only delivery. B2B-cohort exceptions on Interlink Pre10:30 typically run under 0.5% (a fifth of consumer DTC). The selection bias is structural; B2B-cohort exception rate is not a comparable benchmark.
The carded-redelivery cohort dropped after the merchant connected the predicted-window SMS; how does that work?
Interlink’s tracking webhook publishes a 1-hour predicted window on morning-of-delivery. If the merchant or merchant’s commerce platform forwards that to the customer (Shopify Shipping Notifications, BC Order Confirmations, or via a Klaviyo flow), the customer can act on it. Carded-redelivery falls 30 to 50% on accounts that wire the SMS / email through. Pair this card with the rollout-week of any such integration to see the lift.
How do exception clusters in specific postcodes show up?
Not in this headline rate. Use int_route_otd (geographic OTD split) and int_shipments_by_destination (volume by postcode) for the geographic view. Some postcodes structurally generate higher exception rates (apartment blocks, rural addresses, restricted-access B2B sites); they aggregate into the headline.
Compared to Parcelforce, when should we choose Interlink for a cohort?
Interlink wins on consumer-DTC-customer-experience (predicted-window SMS, predicted 1-hour delivery slot communication) and on damage-sensitive cohorts. Parcelforce wins on rural coverage and on Saturday-uplift availability. Most multi-carrier merchants run Interlink for urban DTC and Parcelforce for rural and weekend.