The Order Rescue Data Insight Report
Failed discount codes are not checkout noise. They are a measurable conversion risk and a recovery opportunity.
An independent analysis with University College London (UCL) of what actually happens when shoppers enter invalid discount codes at checkout. It examines three detailed datasets, 47,753+ checkout records across Sports & Lifestyle, Furniture & Home and Fashion & Apparel, sampled from a wider Order Rescue pool of around 3 million sessions across roughly 20 merchants.
What this report reveals
Four things every finance and ecommerce team should know about failed discount codes.
The scale of recoverable revenue.
How much completed order value traces back to a failed code, and why it is larger than most teams assume.
When rescue works hardest.
How rescue effectiveness shifts across the week and across low, mid and high-value baskets.
What customers actually type.
The codes shoppers try most, the ones most likely to trigger a rescue, and where typos and expired promos create avoidable friction.
How discount-seekers behave.
What persistent code-triers and higher-value shoppers do after a code fails, and what it signals about intent and spend.
Headline figures from the dataset
Real recovered order value, measured across the three category datasets.
Rescued order value across 277 rescued orders, the largest rescued total in the analysis.
Overall rescued rate from June to August, representing £11,000+ of recovered order value.
About the analysis
Conducted with UCL across real, anonymised merchant checkout data.
The work covers data cleaning, currency filtering, status classification, order value distribution, purchase rate and AOV by code behaviour, day-of-week analysis, and a detailed look at the failed codes themselves. Three category datasets span Sports & Lifestyle, Furniture & Home and Fashion & Apparel, drawing on 47,753+ real checkout records.
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