The Science of Retail: Applying Machine Learning to Drive Revenue

By Troy Parent

To effectively transform a retail enterprise from data rich to data-led, retailers must make their data actionable. They must turn a data swamp into a smoothly running data stream. This requires clear goal setting, data normalization and seamless bidirectional communications between core systems across the enterprise. Once these basic requirements are met, retailers can effectively 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.

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Troy Parent Author's page Troy Parent has over 18 years of software development experience. In his current role at Oracle, he oversees the development for the Retail Science and Analytic Platform, Retail User Experience and the Retail Application Platform.