And while the conversational commerce company, which helps consumers buy just about anything via text, uses the latest technology, it’s based on a nearly antique version of customer service that’s ready for a digital-age resurgence.
Fleiss, who leads Jetblack as chief executive officer and cofounder, traced retail history back to the mom-and-pop shops consumers used to rely on. They had a limited selection, but the shopkeeper could be a trusted resource when it came to recommendations.
As national retailers such as Sears and Woolworth, and later mass retailers, including Walmart and Target, established themselves, product selection grew and stores moved closer to consumers, but those personalized recommendations from the local shopkeeper were lost.
“More of the work was put on the consumer,” Fleiss said. “People in those stores don’t know about you as an individual as much.”
That was only amplified in the “Age of Amazon,” where e-commerce has made an endless array of products available at one’s fingertips.
“All of a sudden, I spent three hours searching for the perfect area rug, it’s not a great use of time,” Fleiss said. “It’s stripped a lot of the joy of shopping. Shopping has become very transactional, it’s a glorified search bar. That’s also made it less trusted. If you are going to a glorified search bar…it’s less likely that you’re going to trust the results. I think all that’s going to change and I think we’re actually at the brink…that’s going to change with conversational commerce.”
Users pay Jetblack $50 a month to be that shopkeeper who can help make recommendations and check items off the to-do list via the back and forth of text messages.
Fleiss said the average customer of the one-and-a-half-year-old business spends $1,500 a month.
Jetblack uses both human and algorithms to take a simple but far-reaching request, like “find me a baby stroller,” and instantly whittle down the thousands of options online to just three.
That requires a lot of work on the back end and a lot of data on the consumers, for instance how old their children are. Fleiss said the process is getting better and more automated all the time as the machine learning system handles more requests, enabling better, more personalized recommendations at scale.
Along the way, Fleiss said she’s trying to get closer to consumers and to make their lives easier and shopping more efficient while seeking to money on the back end (aside from the monthly fee to shoppers).
“The key thing is you want to have a trusted relationship,” she said. “At the end of the day, my premise is, let’s reduce the clutter. The promise I want to make to consumers is that we’re trying to get you the best price out there.”