How to Measure the Impact of Showing a Delivery Date in Your Store

Swapping a generic “ships in 24-72h” for a concrete “get it by Thursday the 23rd” is one of the best effort-to-payoff changes you can make in a store where the date actually weighs on the purchase — gifts, outfits for an event, seasonal decor, fresh food, urgent spare parts. But it comes with two conditions: your logistics have to be able to deliver on what you promise, and you have to measure the effect instead of assuming it. This guide covers both.

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First things first: make sure you can deliver on what you’re about to promise

A concrete date only works if it’s true. Before you show anything, measure your real fulfillment rate: take your most recent orders and work out what percentage arrived within the window you would have promised. With a high fulfillment rate you can show tight dates with confidence; with a low one, showing concrete dates is counterproductive — every missed promise turns into a support ticket, a negative review or a return. In that case, fix your logistics first, or show dates with a buffer built in.

The hypothesis you’re going to test

Showing a concrete estimated date on the product page and in listings should produce four effects, each one measurable on its own:

• More add-to-carts — the “will it arrive in time?” doubt gets resolved before adding to cart, not at checkout or in an email to support.
• Fewer support questions about delivery times — the “will it be here by Saturday?” and “does it ship today?” messages disappear when the answer is right on the product page.
• Less checkout abandonment — the “it won’t make it in time” moment happens before the customer ever reaches payment.
• Fewer “arrived late” returns — on orders bought for a specific date, customers who knew the real timeline up front return less.

The setup

The implementation with the Delivery Date module, in order:

1. Installation and initial configuration of the module.
2. Daily shipping cutoff time — the real time of day after which an order no longer goes out that same day. Be honest with this one: it’s the foundation of the whole calculation.
3. Working calendar with the national and local holidays of your warehouse — not the customer’s.
4. Preparation days per product or category. The classic mistake lives here: leaving personalized or made-to-order products at 0 preparation days makes the calculation promise impossible deliveries. Configure them separately from day one.
5. Copy oriented to the date, not the lead time. “Get it by Thursday the 23rd” says more than “delivery in 24-48h” — the concrete date is the whole point of the change; generic copy throws it away.
6. Consistency between product page and listings. Both surfaces should show the same calculation — “one page says X and another says Y” creates exactly the distrust you’re trying to remove.

What to measure and how

Define your baseline before activating the module: a closed period with the four metrics from the hypothesis. After the change, compare against an equivalent period — and seasonality matters especially here: in gift-driven sectors, comparing a month that includes Christmas against one that doesn’t invalidates any conclusion. Use the same period from the previous year, or windows well away from peaks.

• Add-to-cart rate — from your analytics, on the product pages where the date is shown.
• Checkout abandonment — your analytics tool’s standard funnel.
• Support questions about delivery times — start tagging that ticket type now; without tagging there’s no metric.
• Returns with “arrived late” as the reason — record the return reason if you’re not doing it yet.

One extra check that’s worth its weight in gold: periodically review the real fulfillment of the dates the module is showing. It’s your health metric — if fulfillment slips, every other improvement will eventually slip right behind it.

Typical first-month mistakes

• Personalized products left at 0 preparation days. The calculation treats them as standard stock and promises impossible deliveries. It’s the most common corrective fix — far better to configure it before going live than to correct it with orders already promised.
• Copy that’s too generic. Building the whole date calculation only to end up showing “delivery in 24-48h” wastes the change — the concrete date with the day of the week is what makes the difference.
• An optimistic cutoff time. If the warehouse stops picking at 1 pm but you configure 5 pm, every afternoon order is born with a broken promise.

Which stores see the biggest effect

The effect is proportional to how much the date weighs on the purchase decision. For products bought for a specific date — personalized gifts, outfits for an event, seasonal decor, fresh food, urgent spare parts — the visible date attacks the buyer’s main doubt head-on. In recurring-purchase or no-urgency sectors (home decor, routine restocking) the effect exists but is smaller. And in any sector, the requirement of logistics that actually deliver still applies: the date you show only builds trust for as long as it stays true.

Frequently asked questions

What results can I expect from showing the delivery date?

It depends on your sector and your fulfillment rate — which is why this guide focuses on measuring it in your own store: a prior baseline of add-to-cart, checkout abandonment, support tickets about delivery times and late-delivery returns, then a comparison against an equivalent period after the change.

What do I need to check before showing concrete dates?

Your real fulfillment rate: what percentage of your recent orders would have arrived by the promised date. With low fulfillment, showing concrete dates is counterproductive — every missed promise turns into a support ticket, a negative review or a return.

What are the most common mistakes when setting up the delivery date?

Leaving personalized or made-to-order products at 0 preparation days (which promises impossible deliveries), using generic copy like “delivery in 24-48h” instead of the concrete date, and configuring a shipping cutoff time more optimistic than the warehouse’s real one.

What kind of store does this strategy work best in?

In date-sensitive sectors (gifts, outfits for an event, seasonal decor, fresh food, urgent spare parts) with logistics able to deliver on the promise shown — without that, the effect is smaller or can even be counterproductive.

Zeyvro builds PrestaShop 8 modules from a real store running in production. Unobfuscated code.