Enhancing Sales Performance Analysis Through Advanced Data Visualization Techniques: Evidence from E-Commerce Transaction Data

Conventional‍‍‍‍​‍​‌‍​‍‌ sales reporting instruments heavily depend on static tables and rudimentary visualizations, which may hide intricate data patterns and thus limit the ability of managers to take proactive measures. This article introduces a novel advanced visualization framework that integrates hierarchical, flow-based, and predictive analytics into a single dashboard. The layout, supported by a long-term dataset of e-commerce transactions, utilizes tree maps, Sankey diagrams, animated time-series plots, and Prophet-based forecasting with seasonality decomposition. The results show that these visualization techniques uncover a greater variety of insights that are also more actionable than those revealed by traditional dashboards, thus empowering managers not only to foresee future sales trends but also to seasonal demand fluctuations. The proposed framework is a scalable analytical model for converting the simplest sales data into valuable business intelligence; thus it not only makes the decision-making process more precise but also shortens the time required for the ​‍​‌‍​‍‌analysis.​‍​‌‍​‍‌