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Introduction

At the National Retail Federation’s 2017 Big Show held in New York City from January 15–17, machine learning, or predictive artificial intelligence, took center stage. By building and automating precise data models, retailers can proactively serve customers, schedule field service and customer support, rapidly search inventory, conduct risk analysis, forecast prices, improve efficiency in the production process, tightly manage the supply chain, and more.

Highlights

  • With consumer expectations growing around how brands must meet their needs with accuracy and timeliness, simply targeting consumers based on basic demographics or transactional data won’t suffice. This will fall short in achieving deep efficiency gains or creating the robust experiences customers have come to expect.
  • Machine learning will become ubiquitous as retailers realize it’s the most precise way to predict customer behavior and purchasing habits over time.

Features and Benefits

  • Examines the level of advanced analytics needed to unlock the value of the information necessary to solve a desired issue or achieve a specific goal.
  • Looks at how machine learning will become ubiquitous as retailers realize it's the most precise way to predict customer behavior and purchasing habits over time.

Key questions answered

  • What's more critical to a specific business – a machine learning generalist or specialist?
  • What are some examples of how retailers are using machine learning in their enterprises?

Table of contents

Ovum view

  • Summary
  • Machine learning's varied uses
  • Machine learning isn't a panacea. Start small.
  • Machine learning will become ubiquitous

Appendix

  • Further reading
  • Author

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