> ## 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. # Exception Rate, Interlink Express > Exception Rate for Interlink Express stores. Tracked live in Vortex IQ Nerve Centre. How to read it, why it matters, and how to act on it. **Metrics type:** [Key Metrics](/nerve-centre/overview#metrics-types-explained) • **Category:** [Shipping & Courier](/nerve-centre/connectors#connectors-by-type) ## 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%** | Total volume: 4,950 consignments. The card reads **2.4%**, comfortably under the 3% alert. Five things to notice: 1. **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. 2. **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. 3. **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. 4. **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. 5. **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](/nerve-centre/kpi-cards/interlink-express/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](/nerve-centre/kpi-cards/interlink-express/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](/nerve-centre/kpi-cards/interlink-express/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](/nerve-centre/kpi-cards/interlink-express/returned-to-sender) | Terminal-exception subset. | High exception + rising RTS = re-attempt logic failing. | | [Pre-10:30 Service Promise](/nerve-centre/kpi-cards/interlink-express/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](/nerve-centre/kpi-cards/interlink-express/shipments-by-destination) | Geographic split. | Cluster geography = depot-level fix. | | Cross-connector: [`shopify.refund_rate`](/nerve-centre/kpi-cards/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`](/nerve-centre/kpi-cards/apc/exception-rate) | Peer UK premium. APC's structurally similar operating profile. | Useful for shop-around. | | Cross-connector: [`parcelforce.par_exception_rate`](/nerve-centre/kpi-cards/parcelforce/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](https://www.interlinkexpress.com/) → **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. | **Cross-connector reconciliation:** | Card | Expected relationship | What causes legitimate divergence | | -------------------------------------------------------------------------------------- | ------------------------ | -------------------------------------------------- | | [`shopify.refund_rate`](/nerve-centre/kpi-cards/shopify/refund-rate) | Downstream lag. | App-install events, B2B / pre-order workflows. | | [`apc.apc_exception_rate`](/nerve-centre/kpi-cards/apc/exception-rate) | Peer. | Different consignments. | | [`parcelforce.par_exception_rate`](/nerve-centre/kpi-cards/parcelforce/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, use `int_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. *** ### Tracked live in Vortex IQ Nerve Centre *Exception Rate* is one of hundreds of KPI pulses Vortex IQ tracks across Interlink Express 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. 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