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Digital service failures have become routine features of contemporary markets, yet their consequences are rarely routine for customers. Platform outages, data breaches, personalisation errors, failed transactions, and online service breakdowns can quickly damage customer confidence because they interrupt access, create uncertainty, and expose the fragility of digital relationships. Traditional service recovery models provide important foundations, but they do not fully address the speed, visibility, scale, and technical opacity of digital failure. The central problem addressed in this article is that digital service failures differ from face-to-face or phone-based failures in both mechanism and meaning. A late employee apology or a replacement offer may work in a human-delivered service encounter, but digital failures often involve thousands or millions of affected users, automated systems, sensitive data, and unclear accountability. These characteristics require a dedicated conceptual model that connects failure type to recovery response. The objective of this article is to propose the Digital Service Recovery Model. The model explains how firms can restore customer confidence after three core categories of digital service failure: platform failures, data errors, and online service breakdowns. It argues that recovery effectiveness depends on matching the recovery strategy to the failure mechanism rather than applying a generic service recovery script. The resulting model identifies failure triggers, recovery strategy selection, confidence restoration pathways, and feedback loops for systemic improvement. It shows that rapid response, transparent communication, tangible redress, personalised reassurance, and demonstrable technical fixes are not separate tactics but interdependent recovery capabilities. The article contributes a practical and forward-looking framework for firms operating in the digital trust economy.