How a Startup Is Helping Retailers by Using Digital Tests in Physical Spaces

How a Startup Is Helping Retailers by Using Digital Tests in Physical Spaces

Pranshu Maheshwari (left) and Yash Kothari of Prayas Analytics.

Image credit: Adam Friedberg
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Y Combinator’s Kat Mañalac remembers well the day last year when she met Prayas Analytics founders Pranshu Maheshwari and Yash Kothari. Mañalac was visiting the Wharton School in Philadelphia as part of YC’s spring campus tour, where the accelerator scouts investment opportunities and offers winners a flat $120,000 for a 7 percent stake in their business.

“They had handmade T-shirts on,” she recalls. “Yash’s shirt said ‘I [heart] YC’ and Pranshu’s said ‘I [heart] Traction.’”

Those shirts got to the heart of the duo’s pitch, functioning as a live A/B test, a common practice in the digital space in which users are served various search outcomes from identical queries, website designs and calls to action in order to see which generate the most sales. For the record, Mañalac preferred Kothari’s shirt. She also liked the idea for Prayas Analytics, which led to YC’s investment in the two college seniors, who have since graduated and set up shop in New York City.

The appeal? Prayas’ A/B testing isn’t for the web but for physical retail spaces. Sure, there have been similar solutions, but they typically involve a ton of gear and expense, Mañalac says. “Prayas’ idea works by analyzing security camera footage, and that low barrier to entry is more broadly attractive to all kinds of retailers.”

Kothari explains that Prayas’ innovation is in using its software to move the online technique of A/B testing into physical spaces where previously it wasn’t feasible. “We take thousands of images and study movement. The system understands the objects and the movement as patterns, and we’re counting engagement moments.” He says a chain like J.Crew may have a theory of engagement, one that puts pricier items at the back of its stores, but Prayas “can test that dynamic in multiple locations with identical displays.”

Data from those locations can reveal high-engagement interactions or dead zones; retailers can then use that information to alter fixtures and layouts and control traffic patterns accordingly.

“The hardest part for clients is that we’re testing their intuition -- one honed over years of merchandising experience -- with before-and-after results, and often their intuition is wrong,” Kothari says. “Hunches and gut are important, but the real data is what helps you refine a hunch into sales.”

The concept was a perfect fit for New York, he adds. “We can engage with retail shoppers and with our customers here. This is about a high-touch relationship, and that’s the lifeblood of this city.”

Edition: June 2017

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