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

Sandra Habil (), Sara El-Deeb () and Noha El-Bassiouny ()
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Sandra Habil: German University in Cairo
Sara El-Deeb: German University in Cairo
Noha El-Bassiouny: German University in Cairo

Chapter Chapter 30 in The Palgrave Handbook of Interactive Marketing, 2023, pp 683-704 from Springer

Abstract: 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.

Keywords: Artificial intelligence (AI); Interactive marketing; Recommendations systems (RS); Online behavioral targeting (OBT); Retargeting; Personalization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-14961-0_30

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