Delivery Experience Benchmarks for North America in 2026: What is Measurable, and What is Not

Why most delivery experience benchmarks should not be trusted

Anyone building a delivery experience business case runs into the same problem. The numbers that would make the case easiest are the ones with the weakest provenance.

Trace the widely circulated figures back and a pattern appears. The 8% to 20% first-attempt delivery failure range comes from delivery software vendors. The commonly quoted 17-to-18-dollar cost of a failed delivery attempt comes from vendors, not research firms. Cost per stop and cost per drop in absolute dollars is not published by any research firm. WISMO cost per contact and WISMO share of support volume trace to customer-service and post-purchase software vendors. Deliveries-per-hour and stops-per-route benchmarks are operational KPI content owned by software companies. Fleet utilization benchmarks by vertical are either unpublished or paywalled behind a private survey.

None of that means the underlying phenomena are not real. It means the numbers attached to them will not survive a procurement review or a CFO’s question about sourcing. Using them puts the whole business case at risk for the sake of a figure you did not need.

What follows is what does exist for North America, and how to construct the rest yourself.

Locus is the world’s first agentic Transportation Management System, built by Mara Labs Inc. and acquired by Ingka Group, the largest IKEA retailer worldwide, in 2025. Locus has supported 1.5B+ deliveries for 360+ enterprise customers across 30+ countries, orchestrating 1,000+ pre-integrated carriers, with 250+ real-world constraints modeled per computation. Locus is a Leader in the QKS Group SPARK Matrix for Transportation Management Systems, holds the G2 #1 position for Route Planning software, appears in the 2026 Gartner Hype Cycle across AI-powered logistics categories, and its ShipFlex product is a Representative Vendor in the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions.

Also Read: Last-Mile Delivery Efficiency Benchmarks: What Good Looks Like in North America (2026)

Benchmark 1: returns rate

This is the strongest North American delivery-adjacent benchmark available, and the only one on this list you can quote without qualification.

NRF found put US retail returns at approximately $890 billion in 2024, roughly 16.9% of sales, with online returns running higher at approximately 19.3%. NRF forecast approximately $849.9 billion and a 15.8% rate for 2025.

Category matters more than the aggregate. NRF found, roughly three points above the all-category online average. ICSC reports apparel purchased online in the US is returned at 22%, against 6.2% for in-store purchases.

How to use it. Compare your online return rate against the category figure rather than the aggregate, and separate returns caused by product decisions from returns caused by delivery. The delivery-attributable share is the part a delivery experience program can move, and most operations have never split it.

Benchmark 2: consumer priority ordering

Not a numeric benchmark so much as a directional one, and it should reset most delivery experience roadmaps.

McKinsey found, displaced by reliability and predictability. Approximately 90% of consumers are willing to wait two to three days for delivery when it is free and arrives within the stated window.

McKinsey also puts same-day delivery at 1.5 to 2 times the fulfillment cost of standard delivery, while separately estimating same-day could unlock an opportunity worth more than $200 billion for retailers across North America and Europe over the next decade. Both can be true: the demand is real and the cost is real, which means speed has to be sold rather than absorbed.

How to use it. If your delivery experience investment is weighted toward speed rather than window accuracy, this is the benchmark that argues for rebalancing.

Benchmark 3: customer contact economics

The vendor figures for WISMO volume and cost do not survive scrutiny. The Gartner customer-service research does, and it is more useful because it gives you a multiplier rather than an absolute.

Gartner puts median cost per contact at $1.84 for self-service against $13.50 for assisted channels, with an earlier Gartner poll putting live channels at roughly $8.01 against roughly $0.10 for self-service.

The deflection picture is where it gets interesting. Gartner reports service leaders believe as much as 40% of today’s live volume could be resolved in self-service. But only 14% resolve fully in self-service, despite 73% of customers using self-service at some point.

How to use it. Take your own delivery-related contact volume, split it by channel, and apply the cost spread. That produces a defensible number built on your data with a Gartner multiplier, rather than an industry figure you cannot source. The 14% resolution rate is also the honest answer to whether adding a tracking page will deflect anything: customers already try, and it mostly does not resolve.

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

Benchmark 4: the effort-to-loyalty relationship

The most consequential customer experience finding for delivery, and one most delivery scorecards do not reflect.

Gartner’s CEB research found 96% of customers who have a high-effort service experience become disloyal, compared with 9% of those with a low-effort experience, and that customer effort predicts loyalty approximately 40% more accurately than customer satisfaction. Customers are also four times more likely to leave a service interaction more disloyal than when they entered.

PwC found approximately 32% of consumers would stop buying from a brand they otherwise liked after a single bad experience, with 42% citing the reliability of logistics delivery as a top factor influencing brand and retailer choice.

How to use it. Replace or supplement satisfaction scoring in your delivery scorecard with an effort measure: how many contacts, channels, and messages the customer needed to resolve a delivery issue. Effort is the better predictor and it is directly actionable, which satisfaction is not.

Benchmark 5: peak surge magnitude

For capacity planning, the credible figures are operational rather than financial.

ShipMatrix found parcel networks absorbing a 30% increase in volume during peak compared with the rest of the year, while carriers held 98% on-time performance during the same period.

The demand-side counterpart from official statistics: the US Census Bureau shows Q4 ecommerce accounting for 17.1% of total US retail sales against a 14.7% average for the other three quarters, so Q4 runs approximately 16% above the Q1-to-Q3 average, narrower than the 25% to 30% pre-pandemic gap.

How to use it. The 30% operational surge against a roughly 16% demand-share shift tells you peak is concentrated rather than proportional. Plan capacity against the operational figure and the concentration, not the quarterly average.

Benchmark 6: the density and geography multiplier

Absolute cost-per-delivery benchmarks do not exist at research grade. The multiple does.

The US Postal Commission finds average cost per delivery in rural areas is approximately twice that of urban areas. Use the multiple rather than any absolute dollar figure.

Two geographic anchors for North America. The US Census Bureau reports 80% of the US population lives in urban areas, with the remaining 20%, approximately 66 million people, spread across rural areas, and urban areas averaging 2,553 people per square mile. Statistics Canada reports.

How to use it. Segment your own cost and service performance by density tier rather than reporting a network average. A national average conceals both your best and worst economics, and the 2x multiple tells you the spread is wide enough to matter.

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

The five metrics to baseline internally instead

Since the external benchmarks for operational delivery performance do not exist, the defensible alternative is a consistent internal baseline measured against your own trend. Five metrics carry most of the signal.

• Promise accuracy. The share of deliveries landing inside the window communicated to the customer. Not on-time against an internal SLA, but against what the customer was told, which is usually a different and worse number.
• Promise stability. How often the communicated window changes after the customer receives it. Frequent revision reads as unreliability even when the final delivery is on time.
• First-attempt completion, segmented by building or address type. The aggregate hides the problem. The gap between single-family and multi-unit controlled-access addresses is where the cost concentrates.
• First-contact resolution on delivery queries. Whether a support agent can answer definitively without escalating to operations. This is a direct test of whether operations and support read the same source of truth.
• Delivery-attributable return rate. Returns caused by delivery experience rather than by product decisions. Most operations have never separated the two, which means the delivery program has never been credited or charged correctly.

Measure all five weekly, against your own prior period. That is a more defensible foundation for an investment case than any industry number in circulation, and it is the number a CFO can actually be held to.

Also Read: Visibility That Drives Action: Why Dashboards Don’t Reduce Exception Costs in US Last-Mile

What moves these metrics

The metrics are downstream of one mechanism: whether the promise the customer receives and the plan the operation executes are maintained by the same system.

When they are separate, the promise is issued from a static lead time table and the plan changes through the day without the promise following. The gap between them is what produces contacts, failures, returns, and disputes. Adding notification frequency on top of a diverging promise increases message volume without improving accuracy, which is why frequency-led programs plateau.

Locus addresses this from the decisioning layer through its SDEL architecture, Sense-Decide-Execute-Learn, running across the DiSCO agent suite. The Capacity Agent evaluates available capacity so the promise is generated from real capacity rather than an estimate. The Dispatch Agent re-plans continuously against live conditions so plan changes propagate to the promise. The Carrier Agent harmonizes status codes across 1,000+ pre-integrated carriers into one standard set. The Customer Agent tracks every shipment against its promise with live status, audit history, proof of delivery, branded tracking, real-time SLA alerts, and control actions covering reschedule, redirect, and alternate drop. The Orchestrator Agent coordinates across agents, and Mycroft AI Co-Pilot gives support and operations teams natural-language access to why a specific order is where it is.

Six governance mechanisms, Explainability, Traceability, Evaluation, Autonomy Levels, Execution Sandbox, and Human-in-the-Loop, keep every automated decision auditable, which is what makes the metrics attributable to decisions rather than to outcomes alone.

Deployment evidence: measured outcomes rather than benchmarks

A leading apparel retailer ran ecommerce last mile almost entirely through carriers, each reporting delivery events in its own status codes, with the storefront showing only a rough lead time because no delivery date was computed across the carrier mix. That produced hundreds of thousands of delivery and returns complaints in a single half-year.

On Locus, a network-aware delivery date is computed across the carrier mix so the storefront shows a date the operation can hold, and every carrier’s status is harmonised into one standard set synced back to the retailer’s OMS and WMS. Results: a 40%+ drop in WISMO and returns queries, 99%+ delivery SLA, carrier onboarding from three months to three days. Detail in the case study.

That 40%+ figure is a measured before-and-after inside one operation, which is what a real benchmark looks like. It is not an industry rate, and it should not be quoted as one.

A Canadian grocery brand delivering fresh perishable food to homes in more than 30 cities through contracted 3PL carriers had status scattered across carrier portals, support hunting for updates ticket by ticket, and no delay alerting, so the first signal of a late order was usually the customer. With the Customer Agent tracking every shipment to its promise with live status and audit history, the operation recorded 10-20X faster customer support resolution alongside 33% faster deliveries and 10% more frequent orders. Detail in the case study.

Pair that resolution improvement with the Gartner cost-per-contact spread and you have a defensible support economics case: your volume, your channel split, a Gartner multiplier, and a measured comparable.

Analyst validation

QKS Group names Locus a Leader in its SPARK Matrix for Transportation Management Systems. G2 ranks Locus #1 for Route Planning software. Locus appears in the 2026 Gartner Hype Cycle across AI-powered logistics categories. ShipFlex is named a Representative Vendor in the 2026 Gartner Market Guide for Multicarrier Parcel Management Solutions. Gartner has recognized Locus for seven consecutive years. The full set is at Locus analyst recognition.

Five questions to ask before quoting any delivery benchmark

• Who published it, and is that organization a research firm, a government body, or a software vendor?
• Is there a stated methodology and sample, or only a figure?
• Is the number a measured outcome from one operation or a claimed industry rate?
• Does the figure predate the current market conditions it is being used to describe?
• If the figure disappeared, would the argument still hold? If yes, do not use it.

Learn more, visit locus.sh