The Future of Pricing Has Arrived; It’s Just Not Evenly Distributed Yet

If you are a student of the future — either out of curiosity or because conditions in your market demand it — you are familiar with the paraphrased version of the headline for this article (“the future has arrived…). The author is William Gibson, the well-known speculative fiction writer. The point he was making, to his non-fiction readers, is that the future in technology tends to show itself with the dominance of a few players that effectively raise the stakes for all competitors. The stakes are raised because to compete in that market will require that the company acquire the new talents, investment and experience of the forerunners. That proves to be impossible for most competitors, so instead of building they acquire platforms that meet the requirements. Once a majority of competitors buy a worthy platform, the rest of the pack is already behind. It’s not an enviable place to be.

So, who are the few at the head of the food retail pack? Off the top of my head, I’d say Walmart, Amazon, Target are all defining the future of retail pricing. And why? Because they have successfully embraced AI as the core technology for selling the right product, at the right price, in store or online platform to get a specific customer to shop. That leaves thousands of retailers behind the starting line today at extreme disadvantage. This is the time that a technology vendor or two will come along hoping to democratize or re-distribute the future, and level the playing field once more. 

A Thumbnail History of Pricing in Retail

I say “once more” because this isn’t the first disruption in technology-driven retail pricing. Our company has taken time to sketch a thumbnail history. At each step of the way, we sought to understand the requirement for the “then-near” future. First were the era of pioneers, in the 2000s, who ushered in an era of predictive price analytics. Among many other conditions to use the technology, there were siloed modules that were used independently to do the predictive work. There were rules or optimization, but not both. And demand analytics came quarterly, an infinite time to wait in fast-moving retail markets. Next, in the 2010s, came the prescriptive era for pricing, marked by modules that could offer independent decisions, where rules could conflict with optimization, and where demand analytics came weekly, closer to the speed of retail analytics. Then, speeding up to the current era — led by the examples of Walmart, Amazon, Target and the company I joined which have employed AI-driven automation to free pricing managers from countless tedious tasks so they can focus on strategy and execution of business-building solutions. The time has arrived for AI and machine learning (ML that gets smarter and smarter about the shopper).