A Predictive Analytics Approach to Brand Equity Optimization: Unveiling Consumer Insights for Masstige Marketing

In the age of big data, a plethora of consumer information is available; nevertheless, leveraging this data to comprehend client behavioral preferences is a significant difficulty. A data-driven methodology is suggested to tackle critical concerns associated with masstige marketing, emphasizing price sensitivity. A machine learning framework has been established, producing a cohesive model that amalgamates transactional RFM (Recency, Frequency, Monetary) factors, behavioral dimensions from consumer interactions, and several demographic metrics. RFM is recorded at the transactional level, while customer interactions are assessed by metrics including active seconds, product views, product searches, session participation, interaction duration, and interaction view count. Clustering techniques such as K-means facilitate the identification of analogous consumers; further labeling of the clusters assists in classification using multinomial logistic regression and in extracting behavioral traits. A 2017 case study utilizing data from a fashion e-commerce company revealed findings that warranted an investigation into predictive analytics. Five <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C s$</tex> are delineated, and research gaps are identified concerning consumer behavior and brand identity ideas. The findings from the price sensitivity study demonstrate the importance shoppers place on unique brands and premium products. An new strategy is offered to re-engage dormant customers and augment brand equity in fashion e-commerce. The findings offer recommendations for branding and marketing strategies. Both specific and general measures were identified and analyzed using the proposed methodology.