WHAT A BROKEN PROMISE COSTS

Ask a team what a late order costs and they’ll add up the credit, the agent’s time and maybe a reship. The cost they can’t see is the customer who quietly orders less often afterward. It shows up months later, in a cohort report nobody connects to the day the package was late.

What the research finds

• Printed goods, online. Shashank Rao, Stanley Griffis and Thomas Goldsby followed previously loyal customers of an online retailer after an order was delayed, against comparable customers whose orders weren’t. Afterward, they ordered less often and placed smaller orders Published. These were the loyal customers a brand assumes will forgive it.
• Ride-hailing. In a study of 1.5 million Uber riders by Basil Halperin, Benjamin Ho, John List and Ian Muir, riders whose trips ran furthest past the arrival time Uber had shown went on to spend 5% to 10% less on the platform than comparable riders Published. Nothing was wrong with the product: the car came, the rider got there. The only failure was the promise.
• Food delivery. Alice Harter, Lucas Stich and Martin Spann looked at about two million orders from a European quick-delivery service. Orders that arrived later than communicated lengthened the time to the customer’s next order, and lateness hurt more than earliness helped Published.
• Restaurants, over years. Tim Norvell, Piyush Kumar and Mayukh Dass tracked diners after service failures. Customers whose problem was fixed well came back about as often as unaffected customers for a while, then drifted down toward the customers whose problems were never fixed. Over the long run, the customers who were never failed visited most Published.

Two details matter later. Harter’s team, and a study of ratings at a South American online retailer by Serkan Akturk and colleagues, both found that the damage from lateness grows more slowly as the delay gets longer Published. The first day late does a large share of the harm, which makes the promised date in chapter 4 the cheapest lever in this guide. And Norvell’s result previews chapter 10: recovery softens a failure but doesn’t erase it.

A late order doesn’t cost you a refund. It costs you part of the next order, and the one after that.

Measure it on your own store

Studies give the direction. Your own data gives the size, and the size wins budget. The method is a failure cohort:

1. Take first-time customers from a full year agoSo everyone has had twelve months to come back.
2. Label what happened to that first orderOn time, late 1 to 2 days, late 3 or more, wrong or missing, damaged, lost, split without warning. One label per order, the worst.
3. Compare repeat rates within matched groupsLate orders cluster in far regions and peak weeks. Compare within the same month, region and first product.
4. Read the gap, with its uncertaintyPool quarters until each group has a few hundred customers.

Matching narrows the bias without removing it. The cleanest evidence comes from failures you didn’t choose, like a carrier’s regional outage: customers caught in it were failed more or less at random. Keep a dated list of such events. The query is in Appendix A.

A worked example

Say a brand ships 10,000 orders a month and 6% of them fail in some way: 600 orders. Customers with no failure come back within a year at 30%. The failure cohort shows that a failure cuts that rate by a fifth, to 24%. So of the 600 failed customers, 36 who would have come back don’t. Each returning customer places 2.5 more orders in the year, at $25 of contribution each.

That’s 36 × 2.5 × $25 = $2,250 of contribution lost each month, from one month’s failures, before any refund or reship. At $12 of direct cost per failure, the direct cost is $7,200 a month. The hidden cost is almost a quarter of the total, and it’s the quarter nobody reports Derived.

With the defaults, each failure costs $15.75, 24% of it in lost repeat contribution, and one point off the failure rate is worth about $18,900 a year. The 20% drop is a placeholder; replace it from your failure cohort, since it decides the answer. For scale, the Uber study’s 5% to 10% fall in spending followed a car ride that arrived late; a damaged or lost order is a bigger failure than that.

Do this

• Build the failure cohort this month for first orders placed twelve to eighteen months ago. Put the repeat rate for each failure type beside the no-failure rate, on one page.
• Run the tool with your own drop and put the per-point value at the top of the operations roadmap, so a packaging fix can compete with an ad budget.