"Since we started as an e-commerce brand, we are used to having a lot of data at our hands and making decisions. We are really excited to use RetailNext in our studios to use more data behind our decisions."
Director of Studios, Bucketfeet
BuckFeet is a footwear brand whose mission is to connect people through art. They were founded in 2011 in Chicago, and every shoe they make is designed by a unique artist. BuckFeet has worked with over 20,000 artists from over 100 different countries. Every day they work to connect people through art and providing customers with products that help them express themselves. Their goal is to have shoppers leave their studio with a good understanding of the BuckFeet brand; It’s more than just shopping for shoes, it’s an artistic experience.
BuckFeet wanted to expand into brick and mortar to learn more about their customers and artists by connecting with them on a local and personal level. By integrating into neighborhoods, they can have their local artists display their artwork, come to events to share their stories, and interact with customers.
Since Buckfeet started out as an e-commerce brand, they were used to having a lot of data to help make decisions. Now with retail stores, they needed to have the same data rigor to their offline business as their online e-commerce.
With RetailNext in the BuckFeet studios, they are able to have more data behind the decisions. They use analytics in the stores in multiple different ways. For example, they are testing to understand more thoroughly what's the right size space, where different types of in-store features should be, what types of features are consumers reacting to, and where they are spending their time. As a result, BuckFeet is able to make many key changes to their in-store operations, and then use the analytics to back up their understanding of what works and what doesn’t work. As they take these learnings they are then able to apply those to future studios as they grow and expand their footprint.
Expand retail footprint from analytical results of existing stores
Data driven testing to learn what works