This booklet synthesises findings from:

Cognitive Load and Behavioural Theories: Research incorporates Cognitive Load Theory (Sweller), Prospect Theory (Kahneman & Tversky), and Construal Level Theory (Trope & Liberman) to explain how specific dates reduce “extraneous load” and “psychological distance”.
Neurophysiological Measures: Studies utilise advanced metrics like NASA-TLX scores and EEG data to measure how task uncertainty and “dual-task interference” impact the shopper’s brain during the checkout process.
Logistic Regression Analysis: Academic papers, such as a Lund University thesis, use sophisticated quantitative modelling on actual sales data (e.g., a dataset of 20,000 furniture orders) to determine exactly how a one-day delay impacts purchase probability.
Repurchase Modelling: Research published in the Journal of Marketing Research (Harter et al., 2025) analysed over 537,000 quick commerce transactions to prove that late deliveries significantly harm repeat purchase rates.

Large-Scale Industry Research and Benchmarks

Findings are supported by broad datasets from industry leaders and specialized research firms:

UX Benchmarking: The Baymard Institute conducted large-scale usability testing across 325 top-grossing e-commerce sites, observing that participants often “come to a complete halt” when forced to calculate arrival dates from vague ranges.
Consumer Sentiment Surveys: Organisations like McKinsey, Narvar, and Radial surveyed thousands of shoppers to identify shifting priorities, discovering that 90% of consumers now value delivery reliability over raw speed.
Platform Performance Data: Data-driven insights from platforms like Shopify and ShipperHQ compare the performance of checkouts that use specific dates versus those that do not.

Controlled Experiments and A/B Testing

The articles cite empirical evidence from specific split-tests designed to isolate the impact of delivery information:

“Decision Mirror” Methodology: OnTrac’s 2025 study used this novel approach to observe actual shopper behaviour rather than just relying on stated preferences, revealing that vague ranges make shoppers twice as likely to abandon carts.
High-Volume Split Tests: A case study of a large European fashion retailer analysed over 50,000 sessions to compare specific arrival dates against “ships in 2 days,” resulting in a 5.4% conversion lift.
Conversion Optimisation Audits: Brillmarkand Channelape documented results from over 200 experiments, showing that estimated delivery dates (EDDs) can achieve a 24.43% conversion increase.

Real-World Case Studies

The sources analyse the operational and financial outcomes of specific brand implementations:

Market Leaders: The strategies of Amazon (the “Prime Effect”), Nike, and Sephora are used as de facto industry standards for using countdown timers and ZIP-code-based precision to create urgency.
Operational Efficiency: The case of Jeni’s Ice Creams is highlighted for its automation of delivery dates, which saved the company 131 hours of labour annually by reducing “where is my order” inquiries.
Niche Retailer Success: ThinkCrucial and Kronans Apotek are cited for using delivery optimisation to boost revenue by 10% and improve net margins while eliminating low-margin orders.

Every claim is cited. Every statistic is sourced. Every recommendation is testable.

Estimated Delivery Dates (EDD)