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How graphs help marketers get super slick on user data

Author

Listed:
  • Eifrem, Emil

    (Chief Executive Officer and Co-founder, Neo4j, UK)

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.

Suggested Citation

  • Eifrem, Emil, 2020. "How graphs help marketers get super slick on user data," Applied Marketing Analytics: The Peer-Reviewed Journal, Henry Stewart Publications, vol. 5(4), pages 347-353, May.
    • Handle: RePEc:aza:ama000:y:2020:v:5:i:4:p:347-353
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    More about this item

    Keywords

    graph databases; connected data; recommendations 2.0; personalisation;
    All these keywords.

    JEL classification:

    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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