OPTIMIZING STORE PERFORMANCE THROUGH ANALYSIS OF CONSUMER PURCHASE PATTERNS

Abstract

The retail industry is undergoing a significant transformation, driven by evolving consumer behaviors and the increasing importance of data-driven decision-making. This study, titled “Analyzing Customer Behavior and Purchase Patterns to Optimize Store-Level Performance,” aims to explore the intricacies of customer behavior within the consumer durable industry in India, with a specific focus on store-level performance for LG Electronics. Utilizing a blend of primary data collected from 450 Quick Commerce users in Pune, Maharashtra, and secondary literature sources, the research adopts a deductive approach to examine various dimensions of customer behavior, including demographics, shopping frequency, preferred purchase methods, and factors influencing brand switching. The findings reveal critical insights into customer preferences and behaviors. Notably, the age group of 26-35 years emerges as the most active segment, with a significant proportion of customers favoring weekly or monthly store visits. The study also highlights the pivotal role of discounts, promotions, and specific product needs in driving store visits. Moreover, the analysis underscores the importance of both online and in-store purchase methods, reflecting the hybrid shopping behavior prevalent among consumers. Key recommendations include enhancing the checkout process, improving customer service, and optimizing product availability and store layout. These recommendations are aimed at addressing identified pain points and enhancing overall customer satisfaction. The study's contributions are twofold: it provides a theoretical framework for understanding the "moment of truth" in retail environments and offers practical strategies for retailers to improve operational efficiency and customer engagement. This research not only fills a critical gap in the literature on retail analytics in the Indian context but also equips retail managers with actionable insights to drive store-level performance. The findings have broader implications for the retail industry, suggesting that a customer-centric approach, underpinned by data analytics, is essential for sustaining competitive advantage in today's dynamic market landscape.