The rapidly evolving retail industry demands innovative strategies to stay competitive and drive business growth. This case study presents a comprehensive framework that leverages data-driven insights to improve the top-line of a multi-category, multi-brand retail store. The framework comprises four essential components. Firstly, the revenue tree analysis provides a detailed breakdown of factors impacting the top-line, including customer growth and purchase patterns. By quantifying the relative impact of these factors, the revenue tree prioritizes strategic initiatives, enabling more informed decision-making. Secondly, customer segmentation based on the RFM analysis allows a deeper understanding of customer behavior. The segmentation process classifies customers into distinct groups based on recency, frequency, and monetary value. This segmentation strategy empowers retailers to tailor their marketing efforts, ultimately increasing customer retention and loyalty. Thirdly, the price elasticity analysis identifies products with elastic demand, helping retailers to optimize promotional offers to boost sales. Finally, the recommendation engine, derived from the intersection of two methods, the Jaccard Index and Market Basket Analysis algorithm, provides personalized product suggestions to customers that drive cross-selling and upselling opportunities. This hybrid revenue improvement framework was implemented for a North American multi-product multi-brand retail store and produced beneficial outcomes on top-line. The segmentation can be made more robust by combining it with other methods of clustering, CLV etc. Data mining techniques can be further applied by using time, location, and buying sequence studies to further improve recommendations. This framework can be applied in ecommerce apart from physical retail stores.