Feature
Artificial intelligence (AI) is altering nearly every aspect of the retail industry. AI is helping retailers work smarter, leaner and more accurately — from the way goods are presented in stores to supply chain moves and inventory planning. Here, we look at some case studies on how AI is being applied by brick-and-mortar stores:
Table of Contents
1. Levi Strauss
When you walk into a store looking for a new pair of jeans, you may have landed at the end of an AI-driven technical solution that allows you to choose the fit of pants — skinny, straight, slim, relaxed, tapered or flared — and find a pair, in your size, at a price you’re willing to pay in the store you’re in.
The clothing brand Levi Strauss uses vast quantities of data to sense demand, forecast where its products should be needed and have them there when a customer shows up looking for stylish clothes.
“Getting the right product to the right place in the right size and at the right price and quantity, while keeping up with the volume of demand and complexity of the channels is no small task,” according to a case study on the company’s AI engagement.
Levi Strauss worked with SAS to use analytics to view and analyze millions of consumer demand signals, so the retailer could create demand plans and a supply chain to target a single geography, including a neighborhood, that would seek a particular garment, color, style or size. The technology is still in progress, but when fully implemented, it should help the Levi Strauss predict future risk, demand and opportunities and have the products customers want on store shelves before they look for them.
Results
- Made data-driven decisions to increase growth and competitive advantage
- Created smarter demand plans and supply chains
- Customized wholesale and retail and wholesale availability
2. Sport Clips
Sport Clips is a national hair care franchise with thousands of stylists working in hundreds of locations across the U.S. The company tries to help its franchisees succeed with the critical function of hiring.
“People who become franchisees are trusting us with, in many cases, their life savings,” says Gordon Logan, founder and chairman, Sport Clips.
“The question we always receive from new franchisees is, ‘Where do I find the people I need to staff my store and deliver that championship haircut experience?’”
To help franchisees with this eternal challenge, Sports Clips tapped AI to build tools that help its many retail franchisees with staffing while improving employee productivity and engagement, according to a case study.
The company turned to IBM watsonx to create the AI solutions it needed to simplify and speed franchisees’ ability to increase the headcount of skilled stylists in their shops, while staying focused on the day-to-day work of running a salon.
The tool gave the salons a chat-style interface and automation for several key hiring tasks, such as creating new job listings, pushing job listings to social media, identifying qualified candidates and scheduling interviews.
Results
- Reduced three-hour hiring tasks to three minutes
- Helped increase staffing by 30%
- Provided access to 169 million job candidates and an AI-powered matching algorithm
3. SPAR ICS
SPAR ICS is the IT group that builds solutions for food retail, sports retail and shopping centers for the SPAR Austria Group. The team wanted to develop a solution that would help it tap the AI to get goods to customers more efficiently, according to a case study.
SPAR ICS turned to Microsoft to build AI tools that would use the company’s data to deliver groceries and other goods to the right stores faster and more accurately.
AI helped the IT team predict demand in specific locations, manage the supply chain to efficiently get those goods to the right retail outlets and create an app that helps customers connect with a store, plan purchases, check out and receive receipts.
“We use AI to analyze data on weather conditions, marketing campaigns, seasonality and numerous other factors to precisely predict the optimum quantities per shop,” says Elisabeth Blaickner, senior product lead, SPARC ICS.
The AI effort was successful for the retailer.
“With our new advanced data and analytics capabilities, we are able to accelerate time to market, proactively anticipate market demands and really drive innovation,” says Andreas Kranabitl, managing director, SPAR ICS.
Results
- Improved inventory prediction accuracy to over 90%
- Reduced unsold groceries to 1%
- Delivered fruit and vegetables to stores three days earlier
4. Ulta Beauty
Ulta Beauty worked with SAS to use AI to improve customer experience (CX) and personalize the way its stores and marketing teams put products in front of customers, according to a case study.
“The two areas we’re really leaning into — AI and machine learning — are anchored in a recommendation engine,” says Kelly Mahoney, VP of member marketing, Ulta Beauty.
“It’s an algorithm that’s proprietary to Ulta Beauty and allows us to bridge the physical and digital worlds.”
The recommendation engine allowed Ulta to target product suggestions, deals and campaigns to small, specific groups of customers. The AI determined what customers are likely to want and offered rewards, through Ulta Beauty Rewards, that could motivate customers to return to the store.
“We can get to a granular group of guests who we want to present something new to readily,” says Melissa Berscheid, senior director of member marketing and technology, Ulta Beauty.
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Results
- Increased customer loyalty, as 95% of sales came from returning customers
- Shifted product recommendations based on real data
- Reached customers with marketing messages in almost real-time
5. Kerry
Kerry is a global nutrition company that develops foods and flavors to food retailers around the world. Since food trends change fast. The company wanted to more quickly and effectively tap into consumer food trends to stay abreast of what consumers want, so it could better create and deliver food products.
“At the highest level, the challenge is simply that the food and beverage landscape is evolving and shifting faster than it ever has in the past,” says James Sandora, global VP for digital, Kerry.
“Our customers have to become extremely nimble in how they address consumer needs.”
Kerry turned to IBM Watson to build an insight tool, Kerry Trendspotter, that would allow it to quickly spot and forecast global food and beverage trends.
The tool crawled through social media content from consumers and food industry influencers, analyzed the data and predicted what customers may want. The tool identified and ranked the emerging patterns to predict the ingredients, flavors and foods that have the highest probabilities of trending.
“When our customers know what products consumers want and begin development earlier, those products hit the market at the peak of consumer demand and everyone wins,” Sandora says.
Results
- Helped reduce product concepts in product development from four to six weeks to five days
- Helped reduce product creation process from six to nine months to under two months
- Worked to deliver results of trend research to other food and beverage makers
