The future of retail continues trying grim, as more brick and mortar stores shut their doors. North American retailers have declared 8,558 store closures up to now this year, with total North American store closures expected to hit 12,000 by the top of 2019, rumored Coresight analysis on Friday
“If retailers want to stay open in the existing stores that they are operating in, my recommendation to them is to ask: Are they understanding the changing habits of those customers, and how they’re shopping with them, in those locations?” Winsor said.
“To survive in the tough, tough retail market, you have to start to turn your business, and make predictions, based on learning from your historical data,” he added. “It’s all about learning from your historical data.”
While the web and automation are responsible for these closures, similar technology might be the answer for physical store locations, mentioned Paul Winsor, chief of retail at DataRobot.
After being within the retail industry for around 30 years, Winsor stated that computing (AI) and machine learning are tools retailers should use to induce ahead—and to remain open.
“Data driven retail is not new. Technology has been around to help companies understand their business from a data perspective before,” Winsor said. “The data just hasn’t been as individual and accurate, as the way that machine learning can help you do that.”
To make predictions in the past, retailers would merely look into daily and weekly transactional knowledge and draw conclusions from that, Winsor mentioned.
As technology evolved and convenience took priority, online stores became the first search. Since technology took over the looking expertise, it also took over the manner retailers draw conclusions and predictions regarding their services. If retailers refuse to advance and adapt to an evolving retail infrastructure, they’ll inevitably be left behind.
The three ways AI helps retailers
“With AI, we’re dealing with machines that can simulate intelligent behavior or imitate intelligent human behavior, i.e. sense, reason, act and adapt,” said Brian Solis, principal analyst at Altimeter. “One of the most popular ways leading brands are using AI today is through machine learning.”
“The difference is that with machine learning, systems can recognize patterns from clean data sets, and with proper management, learn from that data to assess and even predict outcomes and improve performance over time,” Solis added. “This helps retailers learn how to personalize engagement, offers and next best action, as well as guide product and service development.”
Understanding the customer
“We want to be more convenient in the way that we shop and we want to be, we want more convenience, and we want to shop across multiple channels,” Winsor said. “We know, as consumers ourselves, that we are constantly changing our habits and therefore what machine learning and AI is doing in this space.”
“The really impactful part is around forecasting,” Winsor noted. “We are now seeing retailers using AI and automated machine learning to operate their demand forecasting to understand the actual quantity needed today based on the demand from the customers.”
“It’s going to really increase your accuracy because you’re taking in, you’re learning from the past and you’re predicting what that quantity needs to be in the future,” Winsor said. “Operational efficiency is absolutely key, because we’re talking about an industry that is operating its business on very low operating margins.”
Roadmap to AI in Retail
“The more promising and realistic future scenarios include screens, connected dressing rooms, and virtual racks that are tailored to me based on my personal, data-defined persona,” Solis said. “It only shares things I would consider based on previous history, and also coming trends, aligned with individual preferences. You could play that scenario out in a multitude of retail sectors, i.e. automotive, appliances, etc.”