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AI-Based Recommendation Systems: The Ultimate Solution for Market Prediction and Targeting

In: The Palgrave Handbook of Interactive Marketing

Author

Listed:
  • Sandra Habil

    (German University in Cairo)

  • Sara El-Deeb

    (German University in Cairo)

  • Noha El-Bassiouny

    (German University in Cairo)

Abstract

With the advancements of non-stop technologies in the retail sector, the relationship between consumers and retailers has become interactive. AI-driven systems have opened the door for retailers to understand consumers’ needs and predict their future behaviors. Through the lens of personalized recommendation systems and retargeted ads, this chapter explores the role of these AI-driven systems in creating value for consumers and allowing retailers to gain a competitive advantage. Empirically, the current chapter conducts a case study on the pioneer e-commerce platform Amazon to showcase how consumers' and businesses' relationships can be enhanced by AI-driven systems outcomes. In this sense, the findings theoretically contribute to the interactive marketing field by revealing a new method for creating value through approaching recommendation systems and retargeting to closely connect the marketers with the consumers. Practically, the findings show that these systems can help consumers avoid online information overload by providing informative, relevant, and accurate content. On the other hand, these systems help retailers increase their sales and also, consumer loyalty and satisfaction, and allow them to develop new products by predicting consumers’ behaviors.

Suggested Citation

  • Sandra Habil & Sara El-Deeb & Noha El-Bassiouny, 2023. "AI-Based Recommendation Systems: The Ultimate Solution for Market Prediction and Targeting," Springer Books, in: Cheng Lu Wang (ed.), The Palgrave Handbook of Interactive Marketing, chapter 0, pages 683-704, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-14961-0_30
    DOI: 10.1007/978-3-031-14961-0_30
    as

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