You ever search for a food product online or at the grocery store but can’t find that exact something that perfectly matches your taste, dietary or nutritional preferences? You’re not alone. One of the big reasons searching for food products can be so frustrating is they are often bucketed under data categories that are holdovers from existing category management systems built fifteen or twenty years ago.
A new startup called Foodspace wants to eliminate this annoying experience by helping the CPG and food retail industry update their old-school category management systems with technology that makes sure that every conceivable product attribute a consumer may be searching for is documented and assigned to products headed to a physical or digital shelf.
The Boston-based startup plans on doing that by using machine vision technology that analyzes scanned images of new product packaging introduced by CPG manufacturers and uses AI to synthesize and assign attributes based on its understanding of the product packaging and label data. The attributes go beyond the typical high-level product categories such as organic or gluten free, and factors in things such as sensory preferences (creamy, grainy, etc) and consumer taste and lifestyle archetypes. All told, Foodspace’s system can assign nearly three thousand different attributes to a product.
The end result should be faster, more personalized searches for consumers. If, for example, a person who likes cheese, loves spicy food, and has a gluten allergy heads to the deli section of an online grocery store, they shouldn’t have to drill down five categories deep within the deli category to find that gluten-free habanero cheddar. With Foodspace’s AI-powered synthesis and matching of different attributes, a consumer finds a product match much faster, perhaps almost immediately, depending on the understanding the online grocer has about the shopper.
Of course, this move towards more granular, highly-consumer centric data is something that CPG and retail industries recognize is important, but have been slow to evolve away from because of the huge magnitude of switching towards systems that have thousands of product attributes. The Food Industry Association (which goes under the acronym FMI), has been working on a new framework called Shopper Centric Retailing that would update product information in the more detailed way, and this week at FMI’s annual midwinter meeting, the industry consultant who developed Shopper Centric Retailing framework, Winston Weber, announced Foodspace as a “premier” strategic solution partner to help food product companies transition their products to the new format.
In short, Weber sees Foodspace’s technology as an enabling platform to help food brands migrate to the future.
Foodspace’s technology is “helping translate products in the online space, to the benefit of brands, retailers and the end consumers,” said Weber CEO and namesake Win Weber in the press release. “Their technology is the conduit for which the Shopper-Centric Retailing business model can optimize consumer satisfaction.”
As I thought about better product data that could personalize my food product searches, I started to wonder if this could help usher in the personalized food profile concept that I’ve been thinking about ever since I heard Mike Lee talk about the idea at Smart Kitchen Summit in 2017.
Foodspace CEO Ayo Oshinaike thinks so. “The universal data set that enables that is not there,” Oshinaike told me via Zoom. “That’s the piece that’s in the middle that Foodspace is trying to solve with the breaking down of the information accuracy and how we’re able to relate products to consumers.”
