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Retail Science: Applying Machine Learning to Drive Revenue

Troy Parent
Senior Director, Retail Science & Insights

I sat down with TotalRetail to discuss how to effectively transform a retail enterprise from data rich to data-led. As we know retailers must make their data actionable and they must turn a data swamp into an efficient and smoothly running data stream.

In order to do this, clear goal setting is necessary, including data normalization and seamless bidirectional communications between core systems across the enterprise. After these basic requirements are met, retailers can progress from the more basic areas of data analytics, to the most advanced:

  • Descriptive: What happened?
  • Predictive: What will happen?
  • Prescriptive: What shall we do about it?
  • Cognitive: What's the next step?

Each of these questions is important, but to different ends. As retailers embrace and evolve their approach to analytics, feeding good data into smart models, embedded retail science is delivering progressively better recommendations and forecasts.

What Does Retail Science Focus On


Click to Access the Full Article on TotalRetail

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