[论文解读] AI in Food Marketing from Personalized Recommendations to Predictive Analytics: Comparing Traditional Advertising Techniques with AI-Driven Strategies

Elham Khamoushi|arXiv (Cornell University)|Sep 14, 2024

Big Data and Business Intelligence被引用 7

一句话总结

本文将传统广告方法与以AI驱动的食品营销策略进行比较，重点在个性化、预测分析和活动优化，并强调收益与挑战。

ABSTRACT

Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.

研究动机与目标

• 评估基于AI的个性化推荐在食品营销中的定位效果相比传统渠道的提升。
• 分析消费者数据（购买历史、浏览记录、社交媒体）在塑造AI驱动的营销活动中的作用。
• 评估AI自动化在活动管理中的运营效益和成本影响。
• 识别与投资AI能力用于食品营销相关的挑战与局限。

提出的方法

• 调查食品营销中从传统广告（电视、广播、印刷媒体）向AI驱动策略的转变。
• 解释AI如何使用消费者数据生成定制化的营销活动和产品推荐。
• 讨论对消费者满意度、用户体验和活动效率的影响。
• 评估对技术、专业技能以及持续调整信息的投资需求。

实验结果

研究问题

• RQ1 AI驱动策略在食品营销的有效性方面如何与传统广告相比？
• RQ2 使用AI分析在个性化、预测准确性和客户满意度方面可能获得的提升是什么？
• RQ3 在采用AI用于食品营销时，主要挑战和成本考虑因素是什么？

主要发现

• AI能够利用购买历史、浏览行为和社交媒体数据来实现高度定制化的营销活动。
• AI通过自动化劳动密集型流程提升效率，并实现营销信息的持续调整。
• 传统方法建立了品牌认知度，但缺乏AI所能实现的个性化水平。
• AI驱动的策略在产品推荐和预测消费者需求方面具潜在改进。
• 采用AI涉及对技术和专业技能的重大投资。