At a glance
The absolute count of Bring consignments that arrived after the agreed delivery promise in the last 7 days. The companion to On-Time Delivery Rate. OTD is the percentage you put in front of the board; this is the ticket workload that hits Customer Service. A 95 percent OTD on 4,000 weekly parcels reads “almost perfect”; the same dial expressed as 200 late deliveries per week reads “we need a third CS agent on Mondays”.
| What it counts | COUNT(shipments WHERE delivered_at > promised_delivery_date AND delivered_at IS NOT NULL) over the trailing 7 days. Each consignment is counted once at the recipient delivered scan. |
| API endpoint | Bring Tracking API GET /tracking/v3/tracks/{consignmentNumber}, joined to the booking-time expectedDelivery.estimatedDate from the original POST /booking/v3/bookings response. The card stores both timestamps and computes the late-flag at delivery time, not at booking time. |
| Late definition | Delivered after midnight Oslo time on the Bring-promised date for that service code. There is no grace period; a parcel delivered at 00:30 the day after the promise is late. Bring offers a service-credit conversation only on consignments that miss by more than 24 hours; the card surfaces every miss, not just credit-eligible ones. |
| Service-tier scope | All tracked services that report a delivered scan: Home Delivery Parcel, Pickup Parcel, Business Parcel, Cargo. Mail products and untracked Bring 1st-class are excluded because there is no delivered scan to time against. |
| Returns / RTO | Outbound only. Return-leg consignments are filtered. RTO volume is on Returned to Sender. |
| In-transit handling | A consignment that has not yet been delivered is not counted as late, even if it is past the promise date. The card only counts confirmed-late deliveries; for forward-looking risk see Exception Rate. |
| Industrial action / weather | Not auto-excluded. Norwegian postal strikes are rare but happen; severe winter weather on northern routes happens annually. The card records the actual count without smoothing; merchants should annotate any board-level reads during declared incidents. |
| Currency | The card is a count, not a value. To estimate the cost of the late tail (CS time + remediation refunds + reship cost) join with Avg Shipping Cost and an internal CS-cost-per-ticket assumption. |
| Time window | 7D (rolling 7-day count, refreshed on every tracking-event update). 30D and 90D variants are accessible by toggling the time-window control. |
| Alert trigger | >5% of total. Triggers when the late-count exceeds 5 percent of the period’s delivered consignments. For a 4,000-parcel-week merchant the alert fires above 200 late; for a 400-parcel-week merchant it fires above 20. |
| Roles | owner, operations |
Calculation
Calculated automatically from your Bring data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
The same Drammen-based outdoor-apparel brand from the OTD card, 3,800 outbound parcels per week. Reading taken at 09:00 CET on Monday 09 Mar 26 for the trailing 7 days (02 Mar 26 to 08 Mar 26).| Service | Delivered last 7d | Late count | Late % | Avg days over promise |
|---|---|---|---|---|
| Home Delivery Parcel | 1,855 | 67 | 3.6% | 1.4 |
| Pickup Parcel | 825 | 14 | 1.7% | 1.1 |
| Cross-Border Standard SE / DK | 745 | 89 | 11.9% | 2.3 |
| Business Parcel Bulk B2B | 210 | 3 | 1.4% | 0.8 |
| All Bring tracked (this card) | 3,635 | 173 | 4.8% | 1.7 |
>5% of total is sitting just below the trip line at 4.8 percent. Five things to notice:
- Cross-Border is the entire problem. 89 of the 173 late deliveries (51 percent) come from Cross-Border Standard, which is only 20 percent of the volume. Pulling that single lane out brings the rest of the network to 2.9 percent late, comfortably inside SLA. The action is to either upgrade those orders to Cross-Border Express at checkout or set later expectations in the cross-border checkout copy.
- The headline count is the CS staffing number. 173 late parcels = roughly 173 to 250 customer-service interactions over the next 14 days (some customers contact multiple times, some never contact). At 8 minutes per ticket this is 23 to 33 hours of CS time. Use this when deciding whether to add a Monday-morning shift or buy a chatbot deflection layer.
- Average days-over-promise tells you which late tail you have. 1.7 days over promise across the network is the “slipped a day or two” tail that most customers will tolerate; it is not the “lost in transit” tail. For lost-in-transit count look at consignments past 14 days from booking with no delivered scan, surfaced via Exception Rate.
- Pickup Parcel late counts are structurally lower. Once the parcel reaches the pickup point on time, the customer collects it on their own schedule; the late-flag triggers only if the parcel did not reach the pickup point by the promise date. This is why Pickup Parcel is operationally easier to run than Home Delivery for cost-sensitive merchants.
- The week-on-week trend is the actionable read. Last week this same dial read 142, the week before 138, the week before that 131. The 173 reading this week is a 22 percent jump driven entirely by Cross-Border. Pair with Cost Per Shipment Trend and Shipments by Destination to confirm the lane mix did not shift.
Sibling cards merchants should reference together
Late Shipments is a count, not a percentage. Pair it with these to read the count in context:| Card | Why pair it with Late Shipments | What the combination tells you |
|---|---|---|
| On-Time Delivery Rate | The percentage form of the same data. | OTD is the board-level number; this card is the operations workload. They move together but tell different stories at different volumes. |
| Exception Rate | Forward-looking signal. Exceptions today predict late deliveries tomorrow. | A spike in exceptions with a flat late-count usually means the next 24 to 72 hours will see late-count climb. |
| Failed Deliveries | Subset where the parcel could not be handed over at all (not just delivered late). | Late + failed = the full customer-felt failure population. Late tickets recover (parcel arrives day +2); failed tickets escalate (where is my parcel, refund please). |
| Returned to Sender | The consignments that became RTO after multiple attempts. | A small fraction of late shipments graduate to RTO; chasing late on day +1 prevents RTO on day +14. |
| OTD by Route | Where the late count is concentrated. | Most late counts are not network-wide; they are 2 to 5 routes carrying the bulk. Route-level remediation is faster than network-level. |
Cross-connector: shopify.fulfillment_speed | Upstream cause. Slow warehouse pick-and-pack feeds late deliveries. | If warehouse-to-carrier handover is creeping above 24 hours, the late count climbs even with a perfectly performing carrier. |
Cross-connector: gorgias.tickets_open | Downstream impact. Late deliveries drive WISMO tickets. | A late-count spike on Monday morning typically shows up as a Gorgias ticket spike on Tuesday afternoon at 1.3 to 1.8x the parcel count. |
Cross-connector: klaviyo.email_engagement | Defensive comms lever. Proactive “your parcel is running late” emails reduce ticket inflow. | A 30 percent open-rate proactive-late-email reduces the WISMO ticket conversion by 40 to 60 percent; worth setting up if the late count is structural. |
Reconciling against the vendor’s own dashboard
Where to look in Bring’s own portal: Mybring → Reports → Delivery Performance → Detailed View, then filter to Late deliveries and the trailing 7 days. The detail view returns a row-per-consignment list; the headline count for the week should match this card’s reading within the lag tolerance noted below. For Bring Customer Service Account holders, the monthly Quality Report PDF is the authoritative count for any service-credit conversation. Why our number may legitimately differ from Mybring:| Reason | Direction | Why |
|---|---|---|
| Timezone (CET / CEST vs UTC) | Off by 1 day at boundary | Mybring counts in Oslo local time; the card stores in UTC. A delivery completed at 23:30 Oslo time on Sunday is “Sunday” in Mybring and could be “Monday” in the card depending on the cut. Across a 7-day window the effect averages out to less than 1 percent of the count. |
| In-transit consignments | Mybring sometimes higher | Bring’s Mybring portal occasionally counts consignments that are past the promise date but not yet delivered; the card waits for the delivered scan and only counts confirmed-late deliveries. The portal’s number reads “expected late” while the card reads “actually late”. Both are valid; they answer different questions. |
| Cross-Border partner-feed | Either | For non-Nordic destinations the late-flag triggers on the partner-carrier delivered scan. If the partner feed is slow or absent the card may classify the parcel as in-transit while Mybring sees the Bring-side hand-over scan as “delivered to partner” and counts it as on-time. |
| Service-credit-eligible vs all late | Mybring lower if filtered | The Quality Report PDF is sometimes pre-filtered to credit-eligible consignments (more than 24 hours late on services with an SLA contract). The card reads every miss. Compare like-for-like by toggling the credit filter off in Mybring. |
| Returns inclusion | Ours lower | Mybring’s default report sometimes includes return-leg late deliveries; the card excludes them. |
| Card | Expected relationship | Causes of legitimate divergence |
|---|---|---|
gorgias.tickets_open | Late deliveries drive WISMO tickets at 1.3 to 1.8x ratio. | Not every late ticket relates to a Bring shipment; the merchant has other carriers and other failure modes. |
shopify.fulfillment_speed | Slow warehouse-to-carrier hand-over feeds the late count. | The warehouse-leg lag is upstream; once handed to Bring, the carrier-leg performance is what this card measures. |
postnord.pos_late_shipments_count | Adjacent Nordic carrier late count for the same period. | Different carrier, different shipments; cross-compare to decide carrier mix, not for reconciliation. |
Known limitations / merchant FAQs
Why does my late-count climb every Monday morning? Three reasons. (1) Weekend tracking lag. Bring’s network operates Sat sortation but limited Sat / Sun residential delivery in many areas; the parcels that miss Friday’s promise show up as late on Monday’s first scan. (2) The Friday / Saturday booking surge. Norwegian DTC orders concentrate on Friday and Saturday; those parcels enter Bring’s system on Saturday or Monday and any minor terminal congestion shows up on Monday-arriving consignments first. (3) Customer expectation gap. Customers ordering on Friday read “1 to 2 working days” as “Monday or Tuesday”; if checkout copy is not clear that “working day” excludes Saturday, they perceive any Tuesday-or-later arrival as late. The card counts contractually late; the customer counts perceptually late. Tighten checkout copy to close the gap. A single late parcel from October just showed up in this week’s count. Why? The card filters ondelivered_at (the date the recipient actually got the parcel), not on expected_delivery_at. A parcel that booked in October and was finally delivered in March (typical for international misroute or held-at-customs scenarios) shows up in March’s count when the delivered scan finally lands. Pair with Avg Transit (days) to identify outlier-aged consignments; consignments delivered more than 21 days after booking are usually the recovery-from-misroute tail.
My count keeps oscillating between 150 and 250 weekly with no business change. Why?
Volume noise. At 4,000 weekly parcels and a 4 percent late rate, the expected weekly count is 160 with a standard deviation of around 40 parcels (poisson approximation). Anything between 80 and 240 is statistical noise; anything above 280 is an actionable signal. Use 4-week rolling averages for trend, raw weekly counts only for outage detection.
The card says 173 late; my CS team logged 240 WISMO tickets. Why the gap?
WISMO tickets do not map 1:1 to late shipments. Common over-count drivers: (1) the same customer asking 2 to 3 times over a few days, (2) tickets opened proactively before the parcel is actually late (anxious customers), (3) tickets about non-shipping issues miscategorised as WISMO. Common under-count drivers: late deliveries with no ticket because the customer just shrugged. The healthy ratio is 0.8 to 1.5x; far above 1.5x means CS triage is over-weighting WISMO; far below 0.8x means customers are silently churning instead of complaining.
Should I exclude Pickup Parcel from the count? They are technically not late if the parcel is at the locker waiting.
No. The card’s definition is “delivered to the recipient after the promise date”. For Pickup Parcel the recipient is whoever collects from the locker; if the customer collects on day 4 from a parcel that arrived at the locker on day 1 with a day-2 promise, the parcel is on-time at the locker but late to the customer. The card counts the customer-felt experience because that is what drives reorder and review sentiment. If you want to measure carrier-leg only, use First-Attempt Delivery Rate or build a per-card derived view.
My OTD says 96 percent and the count says 173 late. Are these consistent?
Check the math. If you delivered 4,325 in the period and 173 were late, that is (4325 - 173) / 4325 = 96.0 percent. The two numbers should always reconcile. If they differ by more than 1 percentage point, one of three things is happening: (1) the time windows differ (OTD is on 30D, this card is on 7D), (2) the late definition differs (OTD uses the same definition; check that you are not on a custom view), or (3) the carrier population differs (one card includes Cargo, the other does not). Open a ticket with the Vortex IQ support team if the gap is real.
How does Norwegian winter affect this count?
Materially. From mid-November to mid-March, northern routes (postcode 90xx and above) see a 2 to 4x increase in late count vs summer. The card does not auto-smooth; the count just goes up. Three actions: (1) update checkout copy for those postcodes from November, (2) shift volume to Pickup Parcel where the locker network is more weather-resilient, (3) re-baseline the alert threshold per route via OTD by Route rather than re-baselining globally.
My CS team wants a list of the late consignments, not just the count. Where?
Click the card to open the drill-down view; the list is a sortable table of consignmentNumber, recipient_postcode, service_code, expected_delivery_at, delivered_at, and days_over_promise. Export to CSV for ticket triage. The same data is in Mybring’s Reports → Delivery Performance → Detailed View; the Vortex IQ drill-down is the operationally faster path because it joins back to the originating Shopify or BigCommerce order so you can contact the customer with order context already loaded.
Will Bring credit me for these late consignments?
Some of them. Bring’s standard service-credit policy applies to consignments more than 24 hours past the promise on services with a contractual SLA (Bring Cargo, Business Parcel Bulk, Special Delivery overnight). Standard residential Home Delivery and Pickup Parcel are aim-only and do not carry an automatic credit. For the credit-eligible subset, file claims through Mybring’s claims portal within 30 days of the missed promise. The card surfaces every miss; you decide which subset is worth pursuing.