Does Faster Delivery Actually Pay? How to Run the Test Properly

Most D2C brands that switch on same-day delivery can show you a conversion chart that went up. Far fewer can show you that delivery speed caused it, or that the extra orders still made money once failed deliveries and returns were counted.

That distinction decides whether you should scale. Faster delivery means stock held closer to customers, tighter cut-offs and a higher cost per order. If part of the lift came from a campaign that launched the same week, a festive bump or a city that always converted better, you will scale a cost the business can't carry.

Short answer: Faster delivery can increase sales, but the measured effect is usually modest, varies by category and city, and can arrive with higher returns. The reliable way to know whether it pays for your brand is a controlled test that changes one lever at a time, randomizes by pincode or city rather than by customer, and is judged on contribution margin and repeat purchase rather than checkout conversion alone.

What does the research say about delivery speed and sales?

The strongest evidence says speed does move sales, by less than most internal dashboards suggest, and not always in a direction that helps margin.

In a study of a US apparel retailer published in the Journal of Marketing Research, Marshall Fisher, Santiago Gallino and Joseph Xu used the opening of a new distribution centre that made deliveries faster without announcing it. Online sales rose by about 1.5% for each business day cut from a seven-day baseline.

A second study, published in Manufacturing & Service Operations Management in 2024, worked the other lever. Ruomeng Cui and co-authors changed only the delivery estimate shown to shoppers while physical delivery stayed the same. A faster promise lifted sales and profit, but it also increased product returns and reduced customer retention.

Three things follow for an Indian D2C brand. The effects in both studies are small per day and measured from multi-day baselines. We could not find comparable published causal evidence for moving from same-day to 60-minute windows, so a brand testing inside quick commerce windows is working in territory the research hasn't mapped. And both studies come from US retail, where cash on delivery and pincode-level serviceability don't shape outcomes the way they do here. Your own test is the evidence that counts.

The promise and the parcel: two levers you have to test separately

Faster delivery is really two interventions: what you promise on the product page and at checkout, and what actually reaches the customer's door. Change both at once and you cannot tell which one paid.

The practical sequence is reliability first, then the parcel, then the promise. Test how aggressively to promise only after the delivery itself is holding, otherwise you are measuring how much damage an overpromise does. We've argued before that a kept promise often outperforms a faster one; a test is how you find where that line sits for your brand. If the promise is the lever you're working on, start with how to present a delivery date on the product page.

How do you test delivery speed when customers share the same dark store?

Randomise by geography, not by customer. Two shoppers in the same pincode are served from the same stock and the same riders, so a customer-level split on actual delivery speed contaminates itself: the faster arm draws capacity from the slower one, and the result measures your operations under strain rather than the value of speed.

Pincodes are also how serviceability already works in India, which makes them a natural test unit. Google's researchers formalised the same logic for advertising: geo experiments randomly assign non-overlapping regions to test or control and compare the outcomes. For brands live in only a few cities, Google's time-based regression method was built for matched-market comparisons with as little as one test region and one control region.

Before any of this, ask your fulfilment partner one blunt question: can serviceability and SLA be set at pincode level and held fixed for the test period? If the answer is no, a clean holdout isn't possible, and every result after that is a before-and-after estimate.

What should the scorecard measure beyond conversion?

Judge the test on contribution per order placed and on what customers do next. The research above shows a faster promise can raise conversion while increasing returns and lowering retention, so conversion alone can point the wrong way.

Contribution per order placed = (revenue kept after returns, minus product cost, forward delivery cost, reverse cost on failed deliveries and returns, and payment costs) ÷ total orders placed

Divide by orders placed, not orders delivered. A faster promise can pull in cash-on-delivery orders that never arrive, and dividing by delivered orders hides that cost.

Read these secondary metrics per arm as well: conversion from session to order; failed-delivery and RTO rate split by payment mode; prepaid share; return rate; 60-day and 90-day repeat purchase; "where is my order" contacts per 100 orders; and on-time rate against the promise as a guardrail. Most of these already sit among the Quick Commerce fulfilment KPIs most ops teams track; the change is reading them by test arm rather than by month.

Measuring past checkout is what makes a speed test decision-grade. In a case McKinsey described in 2021, a large US home-goods retailer ran delivery-promise tests for about three months, tracking conversion alongside customer satisfaction, loyalty and repurchase, then tailored promises by customer group and reported a conversion improvement of nearly 10%. The design matters more than the number: the scorecard went past checkout before anything was scaled.

How long should the test run, and what will ruin it?

Run it for at least one full repurchase cycle of your core SKU, outside the festive season. Short tests measure novelty, and festive weeks measure everything except speed.

1. Too short. In the Fisher study, customers new to the online store responded faster, while experienced customers responded more over the long run. A two-week test captures first reactions to a new promise and misses the repeat behaviour where speed may matter most for replenishment categories such as supplements and pet food.
2. Festive overlap. October and November change order volumes, cash-on-delivery mix and courier performance all at once. Schedule around the window, or extend the control period across it.
3. Marketing drift. Performance teams move budget toward cities that are converting well, which will be your test cities. Agree a media and pricing freeze with marketing before launch and log every exception.
4. Empty shelves. A faster SLA on a dark store that keeps running out measures availability, not speed. Get demand forecasting for each dark store right before the test starts.

Be careful with the evidence you already have, because observational data will flatter speed. In Shipway's FY25 analysis of non-prepaid D2C shipments, orders attempted within one to two days had a 22% RTO rate against 35% for those attempted after five days. The same analysis put intra-city shipments at 20% RTO and inter-state non-metro shipments at 27%. Fast orders are disproportionately short-distance and metro orders, so the speed gap and the geography gap overlap. The data is real; it simply cannot tell you how much of the difference speed alone would buy. Only a holdout can.

Reading the result: when does faster delivery pay?

Faster delivery pays when the margin from extra delivered, kept and repeat orders exceeds the added cost per order, in the cities and categories you actually tested.

Read results per city and per category before pooling them. McKinsey's work found that same-day delivery does not create value uniformly even across US markets, and a supplement brand and a home décor brand can get opposite answers from the same design. For the cost side of the equation, use the numbers in our breakdown of what same-day delivery costs on your own site.

Why a clean test needs delivery capacity you control

A speed test is only as clean as your control over the variable, and on a quick commerce platform you control neither the promise nor the data. The platform sets the delivery window, decides when your listing shows as fast and keeps the customer record, so you can't hold out a pincode and you can't see contribution per customer. We've set out that trade-off in our comparison of what brands give up on quick commerce platforms.

Your own website is the one setting where a brand can change a single lever and read the full result. That is the ground Zippee's Quick Commerce-as-a-Service model is built for: dark stores across 21+ cities powering 60-minute, 2-hour and same-day delivery on the brand's own channel. Delivery partners are full-time employees rather than gig workers, and capacity is dedicated to the brands on the network, so the variation in a test arm comes from the speed you chose, not from who happened to be available that day. Because orders land on the brand's own site, every number on the scorecard stays with the brand.

The bottom line

Faster delivery is not a belief to hold; it is a variable to price. Pick one lever, randomise by pincode or city, agree the scorecard with finance before launch, run the test for a full repurchase cycle and read contribution per order placed before conversion. Brands that do this end up with a delivery promise they can defend city by city, rather than one they adopted because the market moved.

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