Retail organizations now generate continuous streams of transaction, inventory, and customer data, and dashboards built from that data have become a routine part of how store managers, category planners, and executives make daily decisions. Despite this widespread adoption, systematic understanding of which visualization techniques actually support accurate and efficient interpretation of retail sales performance remains limited. Here we build and apply an evaluation framework that examines the interpretation accuracy, efficiency, and error patterns produced by six commonly used visualization techniques across six everyday retail analysis tasks: overall sales trend monitoring, product-level performance comparison, regional/store comparison, customer segmentation, inventory status monitoring, and short-term sales forecasting. Using a structured trial protocol in which 450 business users interpreted charts built from a common retail sales dataset and were scored against a verified ground-truth answer key, we quantify comprehension accuracy, misinterpretation rate, task-completion time, and consistency across repeated viewing. Results show that simple techniques such as bar and line charts support strong comprehension (85–94%) for trend and comparison tasks, while denser multivariate techniques such as treemaps and geographic heat maps degrade substantially (55–66%) once more than a handful of dimensions are shown at once, and that accuracy falls further as dashboard complexity and the number of simultaneous filters increase. We further identify a measurable gap between users' perceived clarity of a chart and their actual measured comprehension of it, particularly for color-encoded and densely layered chart types. We close by tracing these limitations to their perceptual and design origins and by proposing concrete steps toward clearer, more decision-ready retail sales dashboards.

Keywords— data visualization, retail analytics, sales performance, business intelligence, dashboard design, chart comprehension, graphical perception, human-computer interaction, decision support