Research on characteristics of live e-commerce user consumption behavior and precision marketing strategies based on big data analysis
Hao Sun
Communication University of China Nanjing
DOI: https://doi.org/10.59429/esta.v13i3.15555
Keywords: live e-commerce; user consumption behavior; big data analysis; precision marketing; user segmentation
Abstract
Live e-commerce converts shopping into a live, interactive, data-rich decision scene in which product exposure, host credibility, time pressure, peer response, platform recommendation, payment convenience, and after-sales expectation jointly shape consumption behavior. Big data analysis makes these behavioral records useful when click paths, dwell time, coupon response, comment sentiment, purchase intervals, refund causes, and repeat visits are interpreted as connected evidence rather than isolated indicators. The main behavioral characteristics include impulse activation under limited-time offers, trust transfer from streamer to product, comparison avoidance during dense interaction, and loyalty decay when post-purchase service fails to match live-room promises. Precision marketing should therefore move beyond broad traffic purchase and build segmented offers from real-time behavior, value sensitivity, category preference, risk tolerance, service experience, and retention signals. A practical strategy combines clean data capture, feature identification, user segmentation, controlled offer delivery, and feedback update so that conversion, repeat purchase, and consumer protection remain aligned.
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