Optimizing Retail Application Performance: A Systematic Review of Monitoring Tools, Metrics, And Best Practices

The increasing popularity of digitization in retail has emphasized application performance as a critical component of customer satisfaction, business continuity, and operational efficiency. Retail applications today operate in hyper-dynamic environments, where performance can be introduced by concerns such as workload peaks, scaling cloud infrastructure performance, spikes in transactions, and real-time data processing. Poor performance erodes user experience, depletes objectives, and lowers competitive benefit. Although many surveys of cloud or performance monitoring exist, they do not incorporate retail contexts that emphasize customer facing KPIs, seasonal demand variability, and other omnichannel complexities. The current paper seeks to address this gap in research by providing a systematic review of 45 peer-reviewed studies (2015–2025) specific to monitoring tools, optimization frameworks, and best practices in retail performance monitoring. The findings in the review synthesize performance monitoring platforms (i.e. Splunk, Datadog, New Relic), log management tools, key performance indicators, performance continuous monitoring, performance visualization, and performance integration into retail systems. The paper also offers comparison evaluations of Amazon and Google Cloud monitoring offerings, as well as limitations for optimization planning, and the effect of intelligent analytics to improve scalability and resiliency. The paper contributes by connecting scholarly viewpoints with practical suggestions, providing a distinctive road map for practitioners and academics to develop and enhance high-performance retail applications in cloud-based corporate settings.