A STATISTICAL FRAMEWORK FOR ASSESSING SALES UPLIFT FROM RETAIL STORE LAYOUT CHANGES
Keywords:
Retail Layout Analytics, Spatial-Temporal Analysis, Store Performance Metrics, Multi-criteria Decision Making (MCDM), Customer Behavior OptimizationAbstract
Article presents a comprehensive framework for evaluating the impact of retail store layout changes on sales performance through advanced statistical methodologies. The article introduces a two-step matching algorithm for control store selection, incorporating multi-criteria decision-making frameworks and time series analysis. By employing difference-in-differences regression with spatial panel data analysis, the article quantifies the causal effects of layout modifications while controlling for various market factors. This article demonstrates robust results across diverse retail formats and market conditions, validated through multiple statistical checks including parallel trends analysis, sensitivity testing, and placebo tests. Findings reveal significant improvements in sales performance, customer engagement, and operational efficiency following layout modifications, particularly in stores optimized for omnichannel shopping experiences.
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