Delivery Experience KPIs: The 8 Metrics Every Logistics Team Should Track in 2026

Why Most Teams Under-Measure Delivery Experience KPIs

Three failures recur in how teams choose delivery experience KPIs, and they compound.

On-time delivery rate is not a delivery experience metric. It measures whether a delivery landed inside its window. It says nothing about whether the customer knew when to expect it, whether the item was placed where they wanted it, or whether the experience was one they would repeat. A delivery can be on time and bad.

Ownership is split. Operations owns on-time performance. Customer experience owns satisfaction. Finance owns cost per delivery. Nobody owns the combined picture, which is why the three sets of numbers are frequently reported in different meetings and never reconciled.

The data source is wrong. Many teams measure delivery performance from carrier-provided metrics, which are the carrier’s account of their own performance, at their own definition, on their own reporting cadence. That is useful and it is not the same as measuring your operation.

Delivery Experience KPI Layer One: Operational Performance

On-Time Delivery Rate

The share of deliveries completed inside the committed window. Universal, necessary, and insufficient.

Measure it by window width rather than blended. A 98% on-time rate against four-hour windows and a 90% rate against two-hour windows are not comparable, and blending them lets window widening masquerade as performance improvement.

What moves it: realistic service-time modeling in planning, achievable windows at the point of promise, and re-optimization when the day changes.

First-Time Delivery Rate

The share of deliveries completed on the first attempt. The metric with the most direct financial consequence, because every failure adds a re-delivery and consumes capacity allocated to the following day.

On benchmarks: no research firm publishes a credible cross-industry first-attempt rate, and the ranges in circulation trace to vendors. Baseline your own, segmented by geography and product type, since a dense urban residential route and a suburban scheduled delivery are different problems.

What moves it: address and geocoding quality at intake, pre-delivery notification with an accurate window, and windows the operation can actually hold.

Stop Completion Rate

The share of planned stops completed on the shift. Relevant on multi-stop routes and the clearest early signal that plans are not surviving execution.

What moves it: service-time accuracy by stop type, workload balance across routes, and the ability to re-sequence mid-day.

Also Read: The First-Attempt Delivery Rate: A Key Metric That Decides Last-Mile Profitability in 2026

Delivery Experience KPI Layer Two: Customer Experience

WISMO Rate

Inbound “where is my order” contacts as a proportion of deliveries. The most direct measure of whether your communication is doing its job.

Normalize per thousand deliveries rather than as a share of support volume, because support volume moves for reasons unconnected to delivery and will muddy the trend.

On benchmarks: there is no credible published WISMO rate or cost-per-contact figure. Calculate your own fully loaded cost per contact from agent time, tooling, overhead, and escalation handling. Your support organization can produce it quickly and will defend it in a review, which no published figure will.

What moves it: proactive notification before the customer thinks to ask, and ETA accuracy, since a notification carrying an unreliable ETA trains customers to contact you instead.

Delivery CSAT or NPS

Customer sentiment on the delivery specifically, instrumented at the delivery rather than the order.

The instrumentation detail that matters: survey after delivery completion, reference the delivery rather than the purchase, and segment by whether the delivery had an exception. A blended score across clean and exception deliveries tells you very little; the gap between them tells you what to fix.

What moves it: window accuracy, notification quality, and placement or handling at the door.

Delivery Exception Rate

The share of deliveries with at least one exception, whatever the outcome.

Segment by cause code. This is the single most useful discipline in the entire framework. A count of exceptions tells you volume. Exceptions coded by cause, aggregated over a month, tell you whether to fix address data, window design, capacity, or crew handling, and those are four different projects.

Delivery Experience KPI Layer Three: Business Impact

Cost Per Delivery

Total delivery cost over successful deliveries, not attempts. Attempt-based costing systematically understates the cost of failure, which is precisely the cost the framework exists to surface.

What moves it: route efficiency, vehicle fill rate, allocation between capacity sources, and first-attempt success. The available range on the planning side is documented: constraint-aware routing delivers 10 to 25% cost reduction versus a static daily plan, per McKinsey routing analysis.

Failed Delivery Cost

The full cost of a failure: the wasted attempt, the re-delivery, warehouse handling, the support contacts it generates, and the capacity it consumes from the next day.

Build this from your own inputs. No credible published per-failure figure exists, and the circulating numbers trace to vendors. Your own arithmetic from driver time, vehicle cost, handling, and re-delivery is both defensible and usually more favorable.

The reason this metric earns its place in the business layer: last-mile carries 41 to 53% of total logistics cost, per Capgemini last-mile research, so failure cost concentrated in the final leg has disproportionate effect on total spend.

Also Read: Delivery Experience Optimization in 2026: Three Operational Shifts Reshaping Customer-Facing Logistics

The Ninth Delivery Experience KPI Most Teams Miss

Eight delivery experience KPIs above. There is a ninth that belongs in Layer One and almost never appears on a delivery experience dashboard, and it explains movement in most of the others.

Plan execution rate: stops completed as planned, over stops planned.

A route plan is a financial model of the day. It commits vehicles, drivers, hours, and fuel against expected output. Plan execution rate measures how much of that intended output actually materialized, and the gap is capacity you have already paid for and did not realize.

It moves before on-time rate does, which makes it the earliest available signal that a day is going wrong. It also explains the others: a plan executing at 75% will show weak stop completion, weak first-attempt performance, and elevated cost per delivery, and treating those three as separate problems produces three projects where one would do.

A Fortune 50 parcel provider running 4,500+ drivers lifted plan execution from 75% to 92%, surfacing $14M+ in annualized capacity it already owned. The gap had persisted while every individual system reported working correctly, which is the characteristic signature of an operation measuring outputs without measuring whether the plan survived.

How to Baseline Delivery Experience KPIs Honestly

Published benchmarks for most of these metrics are unreliable, and the reason is worth stating plainly: first-attempt delivery rates, WISMO rates, fleet utilization percentages, and per-failure costs all circulate widely and trace to vendor material rather than research. A target borrowed from one of them will either flatter you or alarm you without telling you anything about your operation.

Four delivery experience KPI disciplines instead.

Baseline for four weeks minimum, with the measurement methodology written down and fixed. A change measured against a moving definition is not a measurement.

Set targets from your own best-performing depot on comparable geography and product mix, and track the spread between your best and worst. Closing that spread is a more actionable goal than chasing an external number, and it is defensible in a review.

Segment before you aggregate. Urban against suburban, product category, window width, capacity source. Blended figures hide the variation that tells you where to act.

Name a metric owner. Each of the nine needs someone accountable for it, and the delivery experience score needs someone accountable for the combined picture, which is the ownership gap most operations have.

Also Read: WISMO Costs You Twice: The Support-Ticket Math Behind Poor Delivery Communication in 2026

What Each Delivery Experience KPI Requires From Your Platform

The column deliberately describes requirements rather than improvements. Any vendor quoting a typical improvement percentage per metric is quoting an undisclosed baseline, and that is the number a reviewer will ask about first.

Building the Dashboard

Daily, operational: plan execution rate, stop completion, exception rate by cause, on-time rate. These drive intervention today.

Weekly, business review: first-time delivery rate, WISMO rate, cost per delivery, all segmented. These drive changes to planning parameters, window design, and capacity allocation.

Monthly, trend: delivery CSAT or NPS, failed delivery cost, and the spread between best and worst depot. These drive investment decisions.

Data sources across all three: the routing and dispatch platform for planned versus actual, the driver app for execution and exception capture, carrier APIs for the portion of the network you do not operate, support systems for contact volume, and survey instrumentation for sentiment. The integration requirement is that a delivery record can be joined across all of them, which is what makes segmentation possible at all.

How Locus Supports Measurement

Locus is the world’s first Decision-Intelligent, Agentic Transportation Management System, and the property relevant to measurement is that the platform that planned the delivery is the platform that observed it.

That matters specifically for plan execution rate, which cannot be computed at all unless the plan is retained and compared against execution, and for exception rate, since cause codes captured at the point of occurrence are structurally better data than causes reconstructed afterward. It also means driver performance can be measured against each route’s expected difficulty rather than a flat fleet average, which is the difference between analytics a driver accepts and analytics a driver disputes.

Decisioning runs against 250+ real-world constraints, so the planned figures the measurement compares against reflect the operation’s real conditions rather than a simplified model. Across the deployed base: 1.5B+ deliveries orchestrated for 360+ enterprise customers across 30+ countries at 99.99% uptime. Locus is ranked #1 in Enterprise Route Planning on G2.

Learn more, visit locus.sh