ONLINE RETAIL: PURCHASING OCCASIONAL GIFTS THROUGH ONLINE CHANNELS IS A PREVALENT TREND IN THE UNITED KINGDOM

Abstract

This study develops and evaluates a predictive model for online gift purchasing behavior in the United Kingdom, utilizing transaction-level data collected from a UK-based online retailer over the period of December 2010 to December 2011. Drawing upon multiple linear regression, the research investigates the extent to which variables such as product type, purchase timing, and unit price influence the quantity of items purchased. The resulting model achieved a highly robust R² value of 0.98599, underscoring its accuracy in capturing consumer behavior within an e-commerce setting. Notably, the model successfully reflects seasonal fluctuations in consumer demand, with particularly pronounced surges during key holiday periods such as Christmas. These temporal spikes emphasize the strong role of cultural and economic factors in shaping online purchasing trends. The findings further reveal distinctive shifts in product popularity across the year, with specific items, such as the White Hanging Heart T-Light Holder, emerging as dominant choices during peak periods. Such patterns demonstrate not only the cyclical nature of gift-oriented consumption but also the importance of symbolic and aesthetic product attributes in influencing purchase decisions. By highlighting both temporal rhythms and product-specific dynamics, the research provides valuable insights into how consumer preferences evolve across different seasonal contexts. In addition to offering empirical evidence of behavioral patterns, this study integrates broader economic and sociocultural perspectives, thereby bridging theoretical frameworks from consumer behavior research with practical retail analytics. The model’s predictive strength enhances its utility for retailers, who may employ these insights to refine inventory management, forecast demand, and design targeted marketing strategies aligned with consumer motivations. Overall, the study contributes to the growing body of literature on predictive modeling in e-commerce by presenting a comprehensive and adaptable framework that underscores the complexity and dynamism of the online gifting market in the UK.