How graphs help marketers get super slick on user data

Emil Eifrem
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Emil Eifrem: Chief Executive Officer and Co-founder, Neo4j, UK

Applied Marketing Analytics: The Peer-Reviewed Journal, 2020, vol. 5, issue 4, 347-353

Abstract: The paper discusses why and how brands need to embrace ‘Recommendations 2.0’ in the shape of highly-personalised data-driven digital brand experiences. Amazon has demonstrated the value of being able to predict what else customers might want to buy, by analysing online sales data. This is a lesson that any brand wishing to survive needs to learn — and apply. However, the retail, banking and services arena is getting increasingly competitive — and Recommendations ‘1.0’ does not suffice. The paper will argue that AI-based shopbot-styled recommendations, or ‘Recommendations 2.0’ is the approach now needed. Examples of intelligent recommendation technology across a wide range of industries will be considered, notably, Google Assistant’s eBay’s AI-based shopbot and augmented reality e-marketing agency Quander. The paper will conclude that to improve meaning and precision requires richer context, which is what AI-enriched applications such as chatbots or augmented reality e-marketing provide, and graph database technology is the way to make this available for the retailer and service provider.

Keywords: graph databases; connected data; recommendations 2.0; personalisation (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2020
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