By David Moran, Eversight
Artificial intelligence (AI) is here, and retailer leadership is faced with a rapidly adapting landscape in which to operate. As early as 2016, IDC predicted that by 2019, Artificial Intelligence Will Change How 25 percent of Merchants, Marketers, Planners, and Operators Work, Improving Productivity by 30 percent and KPIs by 10–20 percent. Has this come true? From our perspective, these estimates were simultaneously too ambitious with the regards to the pace of change, and not ambitious enough with regards to the value at stake.
What is AI? Simply put it’s the growing ability of machines to do work that previously required human intelligence. While there are lots of techniques that fall under the definition of AI, the buzz lately (and the major innovations) has been overwhelmingly driven by advances in Machine Learning algorithms, made possible by the growth of cloud computing to allow flexible processing power to rapidly scale up to tackle tasks that used to be too big or too hard for a computer to effectively manage. So, when people are talking about AI transforming retail, what they usually are referring to are advances in machine learning unlocking huge value.
Why is machine learning relevant to merchandising? If you think of the complexity a merchant is trying to manage it would be nearly impossible to spend time on all the things that one could possibly look into. Buying desks turn over fairly frequently, the assortment complexity is massive, competition is rapidly changing (especially in a world with digitally connected smartphones in-store offering immediate transparency to competitors stores and low friction online buying on the same mobile device), and there are all sorts of operational challenges between sales forecast accuracy and shelf management, etc. that it’s simply overwhelming for anyone to effectively manage.
Machines, on the other hand, are remarkably good at looking at lots of data and picking up patterns. We’re starting to see a shift in retailer software to more of a coaching-based user interface, to surface interesting problems to a merchant’s desk to help them to focus on the areas where a human is uniquely well suited to make judgment calls. We’re also starting to see AI automate routine tasks to free up time and eliminate boring work. As a result, the value opportunity, in our experience, far exceeds a 10-20 percent potential.
At the same time the pace of change has been inconsistent. There are some retailers who already use machine learning to determine their assortment, the position that items come up in search online, their pricing and promotions, etc. Recently Amazon was cited as supplementing critical retail merchandising decisions with AI, with significant results. On the other hand, there are still retailers who are struggling with the transition to the cloud, are working hard to adapt their culture to meet the pace of change, or simply don’t know where to get started. So, 25 percent of a merchants’ work changing due to Machine Learning by 2019, is likely overstated today.
At the same time, this is an inevitable future. We’re seeing the companies that are winning in this space are the ones who take a few, concrete, high impact areas like Pricing, Assortment, or Supply Chain accuracy, and make big bets in a focused way, as opposed to those who are grinding it out trying to fix everything from Master Data Accuracy to data platforms to systems integration all at once. As the wins come in, and the organization adapts accordingly, it becomes easier and easier to fund the transition of the overall infrastructure and attract the right people into the culture to drive the changes.
About The Author
David Moran is Co-founder and Chairman of Eversight and has spent his career in Consumer Goods and Retail, most recently as the Global VP of Sales-Revenue Management for Anheuser-Busch InBev nv/sa, the world’s largest brewer. Earlier, David was a leader in McKinsey & Company’s Consumer Pricing Practice where he developed strategies for global retailers and brands. David sits on the Grocery Manufacturers Association’s Sales Committee and Advisory Counsel. He has also been a guest lecturer for the Stanford Graduate School of Business and USC School of Business and his research interests include pricing, promotions, digitally connected commerce, and applied behavioral economics.