At a glance
Share of Sendle parcels that recorded any non-progress tracking event in the trailing 30 days, compared period-over-period. An exception is any tracking state that diverges from the normal pickup-to-delivery path: failed pickup, address problem, held at depot, recipient unavailable, damaged, lost, or returned to sender. This is the early-warning signal that predicts a falling On-Time Delivery Rate 24 to 72 hours before it shows, and the customer-service ticket wave that follows.
Calculation
Calculated automatically from your Sendle data. See the At a glance summary above for what the metric tracks and the worked example below for a typical reading.Worked example
An Australian DTC skincare brand based in Brunswick, Melbourne, shipping around 2,100 parcels a month, all on Sendle. Roughly 70 percent pickup (the Sendle driver collects from the studio each afternoon) and 30 percent drop-off at the local newsagent partner point on overflow days. Reading taken at 09:00 AEST on 14 Apr 26 for the trailing 30 days (15 Mar 26 to 14 Apr 26). Exception breakdown:
Total shipped in the window: 2,130 parcels. The card reads 3.1 percent on the gauge and the alert at
>3% is just tripped. Five things to notice:
- Pickup Failed is half the problem. 31 of the 66 exceptions are failed pickups, and pickup-driven exceptions are the signature of the Sendle network: when driver coverage in a postcode is thin, the driver does not arrive, the parcels sit, and the whole afternoon’s queue slips a day. This is a collection failure upstream of delivery, not a delivery failure. Pair this card with Pickup Booking Success Rate to confirm whether the failed pickups cluster on specific days or postcodes.
- The vsP comparison is what makes 3.1 percent actionable. If last period read 2.4 percent, a jump to 3.1 percent is a 0.7-point deterioration and worth investigating now. If last period read 3.0 percent, this is flat and the alert is noise. Always read the gauge against its own previous-period number, not against an absolute target.
- 66 exceptions is roughly 66 customer-service contacts in flight. At an estimated 7 minutes per WISMO contact, that is about 8 staff-hours of work generated this month by exceptions alone. The count, not the percentage, is the operational workload: see Failed Deliveries for the deliverability-specific subset.
- The damaged and lost tail converts to claims. The 2 damaged parcels and any lost parcels are the population that becomes a Sendle cover claim. Sendle includes cover (the standard tier carries cover up to AUD 1,500 per parcel, higher on Premium); check Open Claims and Claim Value (open) to see how much of the exception tail has escalated.
- Triage by cost-of-exception, not raw count. A failed pickup costs the merchant a re-book and a day; a damaged parcel costs a replacement plus a claim plus a review. Address Issue exceptions are often self-inflicted (bad checkout address data) and the cheapest to fix at source, so they are the highest-return lever even though they are not the highest count here.
Sibling cards merchants should reference together
Exception rate is the leading indicator. Pair it with these to diagnose and cost the friction:Reconciling against the source
Where to look in Sendle’s own tooling: The Sendle Dashboard → Orders view is the merchant’s source of record. Filter the order list by status (Sendle exposesPickup Failed, Delivery Attempted, Held, Damaged, Lost, Cancelled as filterable states) and set the range to Last 30 Days. The per-parcel Tracking page shows the full scan history for any single parcel, which is the same event stream the card classifies. The Sendle Tracking API (GET /api/tracking/{sendle_reference}) returns the identical events programmatically.
The closest like-for-like view is All Orders, Last 30 Days, status in the exception set, outbound only.
Why our number may legitimately differ from Sendle’s dashboard:
Cross-connector reconciliation:
Known limitations / merchant FAQs
Why is my Sendle exception rate higher than the courier I used before? Pickup. Sendle’s model leans on driver pickup from the merchant, and a failed pickup counts as an exception here even though no delivery has been attempted. A drop-off-only carrier never throws aPickup Failed state. If you compare like-for-like by filtering to delivery exceptions only, the gap usually narrows. The right comparison is exception rate against your own historical Sendle baseline, not against a carrier with a different collection model.
A parcel threw an exception but it arrived fine. Why does it still count?
Because the customer felt the friction. A Delivery Attempted card-left exception means the recipient had to arrange redelivery or visit a collection point, even if the parcel arrived a day later. The card measures friction, not just final-state failure. If you only care about parcels that never arrived, read Failed Deliveries and Returned to Sender instead.
What is the playbook when the gauge crosses 3 percent?
In order: (1) check Pickup Booking Success Rate, failed pickups are the most common Sendle exception driver and the first thing to rule out; (2) check whether the exceptions cluster on specific postcodes or zones via OTD by Route, a regional driver-coverage gap is local, a network-wide rise is systemic; (3) check Open Claims to see how much of the tail is damaged or lost; (4) if pickup and route are clean, raise a ticket with Sendle support and attach the Orders export for the affected window.
How much tracking latency should I expect before this card is accurate?
Treat anything inside the last 2 to 3 hours as provisional. Sendle’s scan-to-API latency is usually minutes but stretches to several hours at peak. The 30-day rolling window absorbs this; the “today” slice does not. Do not act on a single intra-day reading.
Does a cancelled-in-transit parcel count as an exception?
Yes, if Sendle records a Cancelled in Transit scan. A parcel cancelled before any pickup scan (still Booking Created) is not an exception, it never entered the network. The distinction matters: pre-pickup cancellations are merchant-side admin, in-transit cancellations are network events.
Why does the count bounce day to day even when the rate is steady?
Volume seasonality. A DTC merchant ships 30 to 50 percent more on the two days after a campaign send, so a steady 3 percent exception rate produces a visibly higher count on those days. Use the 30-day rolling reading for trend and the daily slice only for outage detection.
Can I exclude pickup failures from this card?
Not on this card; the exception set is fixed so the number is comparable across merchants. To see deliverability without the pickup leg, read Failed Deliveries (delivery-side only) and Pickup Booking Success Rate (collection-side only) side by side.