PRODUCT OPTIMIZATI AND REVENUE CONTRIBUTION ANALYSIS: A DATA-DRIVEN FRAMEWORK FOR THE SPECIALTY COFFEE RETAIL INDUSTRY AFFICIONADO COFFEE ROASTERS -- MULTI-STORE PRODUCT PORTFOLIO STUDY

Abstract

This study addresses three explicit research objectives: (RO1) to identify which products and categories are primary revenue and volume drivers in a specialty coffee retail environment; (RO2) to quantify revenue concentration using Pareto analysis and identify portfolio rationalization candidates; and (RO3) to evaluate geographic performance variation across store locations and derive actionable recommendations. The study context is Afficionado Coffee Roasters, a three-store specialty coffee chain operating in New York City across Astoria, Hell's Kitchen, and Lower Manhattan.Using a transactional POS dataset of 149,116 customer transactions across 80 products and 9 categories, a multi-dimensional analytical framework was developed and operationalized through an interactive Streamlit dashboard. The framework integrates: (i) a composite product Efficiency Score combining normalized revenue and normalized sales volume; (ii) Pareto concentration analysis; (iii) median-based quadrant segmentation; and (iv) store-level comparative benchmarking. Key findings: total portfolio revenue is $698,812.33 at an average transaction value of $4.69. Coffee dominates at 38.63% ($269,952.45), followed by Tea (28.11%) and Bakery (11.78%). Pareto analysis shows 42 products (52.5%) account for 79.25% of revenue. Nine Hero-tier products -- led by Sustainably Grown Organic Lg ($21,151.75, 3.03%) -- achieve efficiency scores above 0.80. Store revenues are near-equal (gap < 2.8%). A strong positive Pearson correlation (r = 0.8468, p < 0.001, n = 80) between units sold and revenue validates volume-first optimization strategy.