[Paper Review] 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 Intelligence7 citations

TL;DR

The paper compares traditional advertising methods with AI-driven food marketing strategies, focusing on personalization, predictive analytics, and campaign optimization, highlighting benefits and challenges.

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.

Motivation & Objective

• Assess how AI-based personalized recommendations improve targeting in food marketing compared to traditional channels.
• Analyze the role of consumer data (purchase history, browsing, social media) in shaping AI-driven campaigns.
• Evaluate operational benefits and cost implications of AI automation in campaign management.
• Identify challenges and limitations associated with investing in AI capabilities for food marketing.

Proposed method

• Survey the shift from traditional advertising (TV, radio, print) to AI-driven strategies in food marketing.
• Explain how AI uses consumer data to generate tailored marketing campaigns and product recommendations.
• Discuss implications for consumer satisfaction, user experience, and campaign efficiency.
• Evaluate the investment requirements for technology, skilled expertise, and ongoing adaptation of messages.

Experimental results

Research questions

• RQ1 How do AI-driven strategies compare to traditional advertising in effectiveness for food marketing?
• RQ2 What are the potential gains in personalization, prediction accuracy, and customer satisfaction when using AI analytics?
• RQ3 What are the key challenges and cost considerations in adopting AI for food marketing?

Key findings

• AI enables highly tailored marketing campaigns using data from purchase histories, browsing behavior, and social media.
• AI enhances efficiency by automating labor-intensive processes and allows continuous adaptation of marketing messages.
• Traditional methods built brand awareness but lacked the level of personalization achievable with AI.
• AI-driven strategies offer potential improvements in product recommendations and prediction of consumer needs.
• AI adoption involves substantial investments in technology and skilled expertise.