Understanding Age-Driven Preferences: A Weighted Sum Analysis of Consumer Behavior in E-commerce

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Abstract

Consumer activity in online commerce plays a crucial role in modern business functions, impacting market trends and guiding approaches for digital merchants. This research utilizes the weighted sum technique to assess consumer actions among different age brackets, emphasizing significant indicators like Average Order Value, Satisfaction with Customer Support, Timeliness of Delivery, and Rate of Product Returns. The results underscore unique preferences and performance patterns across age segments, offering actionable intelligence for e-commerce enterprises aiming to refine their tactics. The examination indicates that individuals aged 45-54 stand out as the leading performers across every metric, showcasing the highest expenditure, most satisfactory customer support ratings, swiftest delivery times, and minimal product return rates. Conversely, the 35-44 age bracket displays the lowest overall performance, marked by comparatively lower spending, moderate satisfaction with customer support, extended delivery durations, and elevated rates of product returns. Intermediate groups include the 18-24 and 55-65 age groups, with the former showing strong overall performance and the latter displaying good customer support satisfaction and reasonable delivery times but lower spending and slightly higher return rates compared to the top performers. Additionally, the 25-34 age group performs moderately well, with decent customer support satisfaction and delivery times but lower spending and higher product return rates. These findings provide invaluable direction for e-commerce enterprises aiming to customize their strategies adeptly for diverse age segments. By comprehending the unique preferences and actions of each demographic, businesses can refine their online shopping offerings, elevate customer contentment, bolster retention figures, and foster comprehensive advancement within the competitive e-commerce.

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REST Publisher

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