Brick and mortar stores are closing left and right, but artificial intelligence may be able to keep them alive.
David Karandish, founder and CEO of Jane.ai, discusses the next step for machine learning and AI through chatbots and natural language processing.
The future of retail continues looking grim, as more brick and mortar stores close their doors. US retailers have announced 8,558 store closures so far this year, with total US store closures predicted to hit 12,000 by the end of 2019, reported Coresight Research on Friday.
While the internet and automation are typically to blame for these closures, the same technology could actually be the solution for physical store locations, said Paul Winsor, general manager of retail at DataRobot.
SEE: Special report: Data, AI, IoT: The future of retail (free PDF) (TechRepublic)
“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.”
After being in the retail industry for more than 30 years, Winsor said that artificial intelligence (AI) and machine learning are tools retailers must use to get ahead—and to stay open.
How businesses stayed open in the past
“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 simply look at daily and weekly transactional data and draw conclusions from that, Winsor said.
As technology evolved and convenience took priority, online stores became the primary way to shop. Since technology took over the shopping experience, it also took over the way retailers draw conclusions and predictions about their services. If retailers refuse to advance and adapt to an evolving retail infrastructure, they will 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.”
1. Understanding the customer
Machine learning helps retailers understand their customers and predict future behaviors, Winsor said.
“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.”
Not only will this increase accuracy, but it increases operational efficiency, saving both time and money for the organization.
“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.”
3. Streamlining product supply and development
Machine learning and AI can play a significant role in determining a retailer’s supply and development plans.
Some questions machine learning can answer, according to Winsor, include: Are retailers selling the right products today based on the customer’s demands and expectations? And are they priced at the right level? And are they the right products in the right assortment in the right stores—in the right location?
Examples of stores using AI
Many big name retailers have successfully implemented AI—out of both necessity and desire to stay ahead of the competition.
Solis outlined the following examples of how major retailers are currently using AI:
In April, Walmart unveiled its store of the future, called the Intelligent Retail Lab (IRL). Instead of using intelligent technology to track items and purchases, Walmart’s system is tracking inventor levels to alert staff when shelves need to be restocked or if fresh items have sat too long and need to be pulled.
In another example, Walgreens uses data from its antiviral prescriptions to track the spread of the flu. Doing so helps customers see the flu levels in their area and also helps Walgreens manage stock across its 8,000 stores.
Using IBM Watson cognitive computing tech, North Face personalizes product matching based on real-time customer input on where they’re going and when and how they plan to spend their time there.
Neiman Marcus uses intelligent visual search in its Snap. Find. Shop. app that allows customers to input pictures of their favorite things and then search inventory to match similar items.
Future of AI in retail
The future of retail is more automated and more individualized, Solis said. “Consumer choice will become less chaotic and stressful.”
“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.”
For more, check out How big data and AI help online retailers compete in the digital era on TechRepublic.