A.I. vs Predictive Analytics

A.I. vs Predictive Analytics

Last year our CEO, Gary Saarenvirta, contributed an article titled “Comparison of Traditional Predictive Analytics Tools Versus Artificial Intelligence-Based Solutions” to the Platt Retail Institute’s Journal of Retail Analytics, which published it in Q2 2017’s issue. Contact us and we’ll send you a copy. In this day and age, of radical disruption and revolutionary changes in retail, predictive analytics models are not really up to the task of looking at a retailers’ data holistically and supporting profitable decision making. A predictive analytics tool can look at a specific set of defined data inputs and a single-purpose model, and support some kind of decision. It may lead to higher sales of a product or increased lift on a promotional effort, but quite often actually fails at making recommendations and supporting decisions that drive store-wide profits. This is because the models aren’t designed to look at the various ‘ripple’ effects such as cross-category cannibalization and forward-buying that occur across the entire retailer’s product assortment anytime a decision is made in the areas of pricing, promotions, and inventory forecasting. They simply do not have the capability to process that much data. The software that powers what we know as “traditional” predictive analytics and as, one would expect, the computing capability is tied to (and limited by) of technology from 25, 30 years ago. Now, consider A.I.-based technologies when it comes to retail analytics. The A.I. approach is based on processing all the available data and interrelationships between every single product, simulating every single decision around pricing, promotions and inventory forecasting, as well as any aspects of merchandise planning, i.e., decisions around what...