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How graphs help marketers get super slick on user data

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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.
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Keywords: connected data; graph databases; personalisation; recommendations 2.0

Document Type: Research Article

Affiliations: Chief Executive Officer and Co-founder, Neo4j

Publication date: January 1, 2020

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  • Applied Marketing Analytics is the major new professional journal publishing in-depth, peer-reviewed articles on all aspects of marketing analytics. Guided by an expert Editorial Board each quarterly 100-page issue - published both in print and online - features detailed, practical articles written by and for marketing analytics professionals on innovative thinking, strategies, techniques, software and applied research showing how major brands are collecting, interpreting and acting on marketing analytics, both around the world and across varied digital and non-digital marketing channels. Learn how to measure the effectiveness of your marketing initiatives more accurately, how this compares to your competitors, identify gaps in your marketing analytics program and what metrics that support sound marketing decision making - and add to the bottom line.
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