Retailers Turn to AI to Combat Burnout, Decrease Merchandiser Workloads
Customer expectations for personalization in the shopping experience have never been higher, competition between retailers has never been greater, and the threat of a potential recession means merchandisers are being asked to do more with smaller teams.
What do any of those challenges have to do with artificial intelligence?
As retailers look at getting more done and freeing up their staff to focus on high-value tasks, interest is growing in the use of AI to handle the mundane tasks that take up too much of a merchandiser’s time — like fixing typos on e-commerce websites and researching underperforming product categories. Retailers are also using AI to flag when new products show signs of being more popular than expected, so merchandisers can act quickly and notch strategic wins.
According to a report by Constructor, an AI-power search and discovery provider for retailers, 40% of small e-commerce teams spend more than 30 hours a week on manual activities, and 57% of e-commerce teams spend more than 20 hours a week on manual activities. With smaller teams generally having a harder time with measurement and impact performance compared to larger teams, due to a more limited tech stack, retailers should be searching for AI solutions that can decrease the workload for their teams, says Constructor CEO and Co-Founder Eli Finkelshteyn.
“Right now merchandisers are often forced to work in the dark. They’re asked to create displays of products around a site, creating rules to slot them in position, but don’t get any feedback on how those rules are performing. That’s a broken system,” Finkelshteyn says. “AI has advanced to the point where we can show merchandisers the rules that are performing successfully and those that aren’t and may need another look.”
In surveying 100 ecommerce merchandising employees, Constructor’s team found that small teams are more likely to spend time on repetitive manual tasks, they’re less likely to find their product discovery efforts to be effective, and they’re less likely to know where to look for data that helps guide business decisions. All of this points to these teams drowning in e-commerce data — as the majority of businesses are today — and not having a measurable, tangible way to use data to improve the customer experience. Finkelshteyn says this is where investing in AI can help take the most drudgery off their plates.
“With as much customer data as the average e-commerce company has, it’s not possible or pragmatic to depend solely on human analysis and manual work to optimize each individual’s search results and category pages,” Finkelshteyn says. “There are millions, if not billions, of inputs every day, and it’s time to lean on machine learning designed with e-commerce in mind to start to tackle this data and use it to improve the customer experience.”
Constructor’s survey found that retail technology is helping to amplify merchandiser effectiveness by reducing burnout, driving results, and visualizing outcomes. Leveraging those outcomes is also a critical milestone for career advancement. With 94% of merchandisers reporting that they have a positive view of AI as a technology for e-commerce, getting merchandisers on board with using new technology should not be a challenge.
Finkelshteyn says an AI-based system that’s actually built for e-commerce can be a merchandiser’s trusted co-pilot. Many of the most basic, mundane work that merchandisers are used to doing — like fixing typos and synonyms — is simply not a good use of their time.
“These are really smart people who know their brands better than just about anyone. They have better things to do. The AI should be able to take care of the basics,” Finkelshteyn says. “But more importantly, there is a large class of problems merchandisers work on that are like finding a needle in a haystack, and AI can help merchandisers find those needles.”