Enhancing E-Commerce with Big Data: From Browsing to Buying Through Recommendation Systems

Nuraeni Johnson, M. S. Purwanegara, N. Mulyono
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Abstract

This research focuses on analyzing the impact of a recommendation system on customer behavior in the e-commerce industry. This study examines the use of big data-driven product recommendations and tailored promotions to enhance customer engagement, conversion rates, and revenue generation. The importance of prioritizing customer engagement in the early stages of the purchasing process is emphasized, and key statistics related to customer behavior in e-commerce are presented. The objective of this research is to investigate the effectiveness of a recommendation system in influencing customer behavior and driving conversions in the e-commerce industry. The research design incorporates a case study analysis of a prominent marketplace in Indonesia. Data were collected from three automation trigger campaigns: browsing abandonment and purchase reminders. The findings of this research indicate that a recommendation system based on big data has a significant impact on customer behavior in the e-commerce industry. This research highlights the importance of prioritizing customer engagement and implementing effective recommendation systems to drive conversion rates and revenue in the e-commerce industry.
利用大数据促进电子商务:通过推荐系统从浏览到购买
本研究的重点是分析推荐系统对电子商务行业客户行为的影响。本研究探讨了如何利用大数据驱动的产品推荐和定制促销来提高客户参与度、转化率和创收。本研究强调了在购买流程的早期阶段优先考虑客户参与的重要性,并介绍了与电子商务中客户行为相关的关键统计数据。本研究的目的是调查推荐系统在影响客户行为和促进电子商务行业转化率方面的有效性。研究设计结合了对印度尼西亚一家著名市场的案例研究分析。从三个自动化触发活动中收集了数据:浏览放弃和购买提醒。研究结果表明,基于大数据的推荐系统对电子商务行业的客户行为有重大影响。这项研究强调了优先考虑客户参与和实施有效推荐系统的重要性,以推动电子商务行业的转换率和收入。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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