Solution Brief AI Machine Vision Luxury Retail

Solution Brief
AI Machine Vision
Luxury Retail

Solution Brief AI Machine Vision Luxury Retail

WonderStore Creates Smart Brand Detection for Deeper Customer Insights Real-time customer insights with brand recognition powered by the Intel® Distribution of OpenVINO™ toolkit “Intel® DevCloud for the Edge allows us to choose our hardware configuration to optimize training, enabling us to create vertical CV models for each customer’s needs.” —Reinier van Kleij, CTO, ...WonderStore % 16 improvement in shop- window 1 conversion for existing customers Responsive retail for a relevant customer experience Staying competitive in fashion retail requires outstanding customer experience for demanding consumer segments. Understanding customers—what they’re wearing and how they interact with the retail environment—is key to increasing buy conversion. Now, WonderStore, powered by Intel® technologies, allows retailers to collect more data than ever about their customers, with a 16 percent improvement 1 in shop-window conversion for existing customers. The new WonderStore Brand Detector, optimized with the Intel® Distribution of OpenVINO™ toolkit and Intel® DevCloud for the Edge, identifies the type and brand of clothing being worn by shoppers. The solution automates brand identification, using visual sensors directed at shop windows, entrances, and store shelves. Using WonderStore Brand Detector, luxury retailers can better understand what customers are wearing and where they spend time in the store, then offer them relevant recommendations based on existing preferences. Challenges: Brand identification training and visual recognition frame rates Identifying clothing type and brand on a consistent basis offers several challenges for retailers seeking an automated solution. The first is that customers may be wearing any of a large number of clothing brands, and it is often difficult to obtain catalog content images. Even when these images can be sourced, they must be regularly updated as new products are released. Visual identification represents a compute challenge: high frame rates improve the accuracy of automated sentiment and clothing brand analysis, but maintaining them can create a high computational burden. In addition to in-store challenges, developers face a time-consuming process to first create effective deep learning prototype models, then optimize those models for maximum performance. However, developing and iterating quickly on these models can offer a significant competitive advantage for retailers, who can use them to gain deeper insights about each customer in or around their storefront. Solution: Optimized brand recognition for improved store performance WonderStore gathers data from cameras located throughout the store, in addition to those in shop windows. Using computer vision algorithms and artificial intelligence trained on models from the Intel Distribution of OpenVINO toolkit, customer data is analyzed, aggregated, and anonymized before being sent to the cloud. Solution Brief | WonderStore Creates Smart Brand Detection for Deeper Customer Insights In real time, the results of this analysis are used to power a range of smart shopping experiences in store, as well as communicate data with sales associates and retail marketing teams. This enables rapid A/B testing of shop windows or in-store displays, as well as development of visitor personas to increase revenue and improve targeting. WonderStore enables streaming content, personalized by visitor segment, to be pushed to store displays. This creates a dynamic, interactive space that adapts to fit target customers. With sentiment analysis and dwell-time monitoring, stores can better understand which displays are making customers happy—and which brands are being worn by the customers who are responding best. Using advanced computer vision technology, WonderStore can use a previously submitted series of opt-in selfies of a Read the full Solution Brief AI Machine Vision Luxury Retail.