The <b>Retail</b> AI Adoption Problem | RetailTechPodcast

The Retail AI Adoption Problem

In the early 2000's a new technology hit the retail market, called price optimization. Base price optimization is exactly what it sounds like: what should be the initial price of a product or what should the "Regular" price of a product be, for items that are longer lifecycle, with multiple replenishment phases? Base price has had the greatest adoption in grocery, at least in the US, mostly thanks to Walmart - not because Walmart adopted it, but because grocers who were competing with Walmart needed to take a more nuanced approach to price in order to survive Walmart's relentlessly low prices. The retailer generally sold ice for $1.27, and the price optimization had recommended jacking the price up to $2.99, way more than double the old price. The difference between price optimization and AI, at least around where it can be applied within merchandise planning, is that price optimization promised to make existing jobs easier - and created whole new pricing departments within the merchandising organization. In price optimization, base price made some traction in grocery, but it took a lot longer outside of that vertical, and it's only through extensive efforts of companies like First Insight, which exposes the "Why" behind its price recommendations, that adoption is coming. Promotion optimization is a whole other mess, which lags even base price - and adoption challenges there are not limited just to fashion. AI needs to learn the lessons of price optimization, and address them. Read more