Analyzing Consumer Behavior Through Big Data to Enhance Retail Marketing Strategies

Abstract

Developments in big data analytics allow retailers to capture and make sense of vast amounts of transactions, clickstream, and social media data to see delicate patterns in purchase behavior and loyalty behavior. Demand-driven forecasting models combine the benefits of time-series decomposition with external variables, such as seasonality and promotions, with much better accuracy, which is optimal in inventory and pricing strategies. Clustering and decision-tree classifiers can divide consumers into high-value and price-sensitive groups, and context-aware features can promote significant growth in repeat purchases when used to target promotions at individual consumers. Hybrid recommendation systems optimize multi-armed bandits with supervised learning profiles and achieve high double-digit lifts on click-through rates and average order values in e-commerce settings. The ethical implementation will rely on human-in-the-loop control and privacy-preserving methods to be ethical and keep the consumers trustful.