How Customer Fit Data Can Improve Product Development, Inventory Management and Sales

As retailers diversify their revenue streams and allocate inventory, merchandise planning grows more complex – and more critical to get right. Common practice is to look at historical purchasing data and use this to inform predictive modelling, but AI fit technology company 3DLook believes the best solution comes from actual customer body data.

“Poor fit is the number one reason for apparel returns,” said Whitney Cathcart, co-founder and Chief Strategy Officer at 3DLook. “If you understand what your customers look like and you can segment that down to planning and distribution, you would not only lower returns, but you would also be distributing products to the places that are closest to your customers.”

Many apparel and footwear companies offer size guides online, but these can be inaccurate or rely on shoppers to know their precise measurements. Individual styles can also differ within the same brand, making it complicated to provide consistent and trustworthy recommendations.

Then there’s the challenge of individual differences between customers. Existing purchasing data can provide insight into the most popular sizes at any specific retail location, but these may not be the perfect fit for each consumer. If a business isn’t serving its customers the exact right sizing for them, it may be vulnerable to lose future purchases should the shopper find a retailer that does offer a better match.

“With the proper understanding of how the customer base differs from region to region, you could properly distribute clothing of various sizes accordingly and re-align patterns and grading rules to better match the body measurement and shape data of your actual customers,” said Cathcart. “For example, you have more people, who live in Texas, who are tall and curvy, and customers in Florida, who tend to be short and curvy.”

In order to identify these nuances about a customer base, technology can be a useful – and user-friendly – tool. 3DLook guides shoppers through a quick scanning process, which entails taking two photos of their body from any smartphone, against any background. These photos will be permanently deleted by the 3DLook platform, once they have been processed to obtain the landmarks on the user’s body  and create their unique 3D model.

Through the collection of these models, which are then compared against specific product data, 3DLook is able to generate individual recommendations for each shopper. These are based on not just the consumer’s size, but also their specific shape and measurement dimensions. In this way, it mimics he recommendations of an in-store associate.

“When you go into a store, the sales associate comes up to you and sees you trying to buy something,” said Cathcart. “She knows the assortment, she can see your body type and ask what style you’re looking for. She has the knowledge of what customers with similar preferences have bought and she can actually help you find something that fits. Now that offline shopping is off-limits, people expect that same experience online.”

Understanding where to direct product within the distribution chain is critical to minimize inventory waste and inter-store shipping. But by using customer fit data and building it earlier into the decision-making process, at the product development phase, brands can improve customer satisfaction, build loyalty and maximize inventory.

Originally posted on Footwear News.