How to Evaluate a Delivery Experience Platform, and Build the ROI Case

The Five Value Levers

A delivery experience platform creates value in five places. Most vendor business cases present the first two, because they are easiest to demonstrate, and they are not where the money is.

1. Support contact deflection. Fewer inbound contacts asking where an order is. Real, measurable, and typically the smallest of the five.

2. Customer retention. Better delivery experience supporting repeat purchase. Real, slow-moving, and hard to attribute cleanly, which is why it belongs in a business case as directional support rather than as a headline number.

3. First-attempt success. Every avoided failure removes a re-delivery and returns the capacity it would have consumed. This converts to cost directly and is usually larger than the first two combined.

4. Plan execution recovery. The share of planned stops actually completed as planned. This is the lever most operations do not measure, and it is frequently the largest, because the capacity is already paid for and simply not realized.

5. Promise accuracy at checkout. Preventing infeasible commitments rather than managing their consequences. The hardest to model and the only one that operates upstream of the failure.

The ordering matters for a business case. A model built on levers one and two produces a modest number that is hard to defend. A model built on three and four produces a larger number that is defensible from your own operational data.

How to Compute Each Lever From Your Own Data

Use your figures rather than published benchmarks, for a specific reason: no research firm publishes a credible cost per support contact, cost per failed delivery, or cross-industry first-attempt benchmark. The numbers circulating for all three trace to vendors, and a CFO who checks will find that out. Your own arithmetic is both more defensible and usually more favorable.

Support contact deflection. Fully loaded cost per contact, meaning agent time, tooling, management overhead, and escalation handling, multiplied by your contacts per thousand deliveries, multiplied by volume. Your support organization can produce the cost figure in an afternoon.

First-attempt success. Your own cost per failed attempt, built from driver time, vehicle cost, warehouse handling, and re-delivery, multiplied by current failures. Then model conservative improvement against your current rate rather than toward an industry target.

Plan execution recovery. Current plan execution rate, meaning stops completed as planned over stops planned. Multiply the gap by your cost per route to size the capacity you are already funding and not using. This is the calculation most business cases omit entirely.

Promise accuracy. Failures attributable to windows the operation could not hold, which requires failure reason codes. If you do not have coded failure data, this lever cannot be modeled and that absence is itself a finding.

Retention. Include as sensitivity rather than base case. State the assumption explicitly and show the model working without it, so the case does not depend on the softest input.

Then halve your first-year assumptions. Adoption takes time, data remediation surfaces during integration, and a business case that survives being halved is one you can defend at renewal.

The Cost Side

Three costs, and the second is routinely underestimated.

Platform cost, typically quote-based at enterprise scale rather than published.

Integration and data remediation. Set by your own systems rather than by the vendor: ERP customization depth, the number of systems in scope, and the state of your address, geocoding, and master data. Audit address quality before signing rather than discovering it in week three, since remediation discovered mid-project moves go-live by more than the work itself takes.

Change management. Dispatchers whose expertise is partly encoded in workarounds need retraining, and adoption determines whether the platform’s benefit is realized or theoretical.

What to Verify Before You Sign

Six questions. The first two separate vendors fastest, and both ask for evidence rather than capability.

1. What is measured ETA accuracy at a reference customer, and how is accuracy defined? ETA accuracy underpins contact deflection, first-attempt success, and customer trust simultaneously. A vendor that does not measure it cannot improve it.
2. Show me one exception traced from signal to resolved action, on a real operation. This tests whether visibility connects to execution. Most platforms surface the exception and require a system switch to resolve it, which moves the delay rather than removing it.
3. Which of our specific systems are you live with in production today, at a reference we can call? Treat “we have an open API” as a non-answer; an API is permission to build an integration.
4. What does your reference customer’s baseline look like on the metrics you are claiming to improve? Any percentage improvement is meaningless without it.
5. What degrades first under peak load: solve time, constraint fidelity, or alert quality? Reference-check on last peak specifically, not steady state. Parcel networks absorbed a 30% volume increase during peak while sustaining 98% on-time, per ShipMatrix peak analysis.
6. Which outcome metrics will you commit to contractually, with what baseline and what remedy?

The industry-wide reason question two matters: 95% of supply chains must react quickly to change while only 7% can execute decisions in real time, per Gartner supply chain research, and only 22% of shippers above $1 billion in revenue believe their control tower is highly effective at driving action, per Gartner control tower research. Most buyers in this category have already purchased visibility. Fewer have purchased the ability to act on it.

Which Category You Are Evaluating

Before comparing vendors, establish which of three categories you need, because comparing across them is the most common evaluation error.

Post-purchase experience specialists own the customer communication layer across carriers you do not operate. Strong on tracking, notifications, and returns. They cannot change delivery outcomes.

Delivery orchestration platforms decide and execute the delivery, then generate the communication from that operational state. Applicable where you control owned or contracted capacity.

Fulfillment providers operate the fulfillment itself and expose experience tooling as part of the service, so the technology is not separable from the operation.

The resolving question is whether you control delivery execution or only communication about it. If parcel carriers make every decision after the label prints, only the first category applies, and levers three, four, and five of the ROI model are unavailable to you regardless of platform.

Where Locus Fits, and Where It Does Not

Locus is the world’s first Decision-Intelligent, Agentic Transportation Management System, in the orchestration category.

The architectural claim, stated so it can be tested: the customer-facing layer is generated from the same operational state that plans routes, allocates carriers, and dispatches drivers. ETAs are computed from live route progress and learned service times rather than transit assumptions, exceptions are detected against promise risk so recovery is attempted before the customer is notified, and slot and promise management exposes window feasibility as an API concern so checkout can validate a commitment rather than assert one. Decisioning runs against 250+ real-world constraints, and carrier reach comes through ShipFlex, connecting a 1,000+ carrier network with 160+ pre-integrated carriers.

Against the five levers. Locus addresses all five, and the two it addresses most distinctively are the two most vendors omit: plan execution recovery and promise accuracy, both of which depend on the platform making delivery decisions rather than reporting them.

Where Locus is not the answer. Brands whose fulfillment is entirely outsourced to parcel carriers and who need only post-purchase tracking, notifications, and returns messaging. Locus is more platform than that problem requires, and a post-purchase specialist will serve it better and more cheaply. Locus is also not a returns-management specialist, and implementation is a project rather than a signup.

Evidence against the levers, rather than a blended ROI percentage:

A Fortune 50 parcel provider running 4,500+ drivers lifted plan execution from 75% to 92%, surfacing $14M+ in annualized capacity it already owned. That is lever four, measured, at scale.

Indonesia’s leading FMCG distribution brand achieved 100% track and trace, 100% proof-of-delivery digitization, and a 34% reduction in distance per order, with a 9% volume utilization increase from the first month after go-live.

A retail enterprise consolidating six legacy systems reduced manual dispatch effort by more than 80%, sustained 99%+ on-time delivery, and reached break-even inside year one.

Across the deployed base: 1.5B+ deliveries orchestrated for 360+ enterprise customers across 30+ countries at 99.99% uptime. Locus is designated a Leader in the QKS Group SPARK Matrix for Transportation Management Systems.

Bring your plan execution rate, your contacts per thousand deliveries, and your failure reason codes. We will build the model on your numbers, not ours.

FAQs

How do you build an ROI case for a delivery experience platform? On five levers: support contact deflection, customer retention, first-attempt success, plan execution recovery, and promise accuracy at checkout. Compute each from your own baseline rather than vendor percentages, include retention as sensitivity rather than base case, and halve first-year assumptions so the model survives scrutiny.

Which ROI lever is largest? Usually plan execution recovery, and it is the one most operations do not measure. The gap between stops planned and stops completed as planned represents capacity you already fund and do not realize, which makes it the cheapest capacity available. One 4,500-driver operation found $14M+ annualized by closing that gap from 75% to 92%.

Why not use published benchmarks in the business case? Because no research firm publishes a credible cost per support contact, cost per failed delivery, or cross-industry first-attempt benchmark. The circulating figures trace to vendors, and a percentage improvement quoted without a disclosed baseline is not a number a CFO can defend.

What should I verify before signing with a delivery experience vendor? Measured ETA accuracy at a named reference with the definition stated, one exception traced from signal to resolved action on a real operation, named production integrations with your specific systems, the reference customer’s baseline on the metrics being claimed, what degrades first under peak load, and which outcomes the vendor will commit to contractually.

How do I know which category of platform I need? Establish whether you control delivery execution or only communication about it. If parcel carriers decide everything after the label prints, only a post-purchase specialist applies and three of the five ROI levers are unavailable to you regardless of vendor.

What costs are usually underestimated? Integration and data remediation, which are set by your ERP customization depth and the state of your address, geocoding, and master data rather than by the vendor. Audit address quality before signing; discovered in week three it moves go-live by more than the remediation itself takes.

How long before the benefit appears? Contact deflection and first-attempt improvement typically appear first, since both respond to changes in communication and planning. Plan execution recovery follows as adoption stabilizes. One FMCG deployment recorded a 9% volume utilization gain from the first month, which is unusually fast and indicates capacity that was already present and previously invisible.