A Data-Driven Visual Analytics Framework for Transaction-Level Retail Profit Modeling and Decision Support

Retail companies are becoming increasingly dependent on data science to inform their pricing, assortment, and regional strategy decisions. Profitability drivers, however, are often difficult to interpret because they span product-level, geographic, discount, and operational environments. In most real-life applications, analytics and visualization are treated as two distinct processes, thereby restricting interpretability and undermining their ability to support decision-making. This study provides a decision-level visual analytics model of retail profit analysis at the transaction level. The model is illustrated on the publicly available Superstore dataset as a benchmark, which contains 9,994 order-line records between 2014 and 2017 with the variables time, product, geographic, customer-segment, shipping, sales, discount, and profit. The workflow combines feature engineering, hierarchical slicing, variance-based comparison, and five publication-ready dashboards covering temporal trends, product profitability, geographic heterogeneity, discount sensitivity, and fulfillment context. The overall profit margin in the analyzed dataset is 12.47%, and 18.72% of order lines are loss-making. The findings show uneven profitability across product groups and states, with loss pockets observed in sub-categories such as Tables and Bookcases, as well as in states like Texas and Ohio. There is also a distinct nonlinear discount behavior: profitability tends to be positive at discount rates below about 20%, then margins decline sharply, and loss rates increase exponentially at higher discount rates. These benchmark-specific results illustrate how integrated visual analytics can support structured inspection of pricing, portfolio, and region-related patterns within transaction-level retail data.

@article{Badawood2026,
  title     = {A Data-Driven Visual Analytics Framework for Transaction-Level Retail Profit Modeling and Decision Support},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Donia Badawood},
  doi       = {10.14569/IJACSA.2026.0170635},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170635}
}