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Case Study: Computer Vision Application in Retail
Global Fragrance Brand Cuts Compliance Review Time by 90%
A global fragrance conglomerate that acquired several brands needed a better approach for monitoring whether major department stores were adhering to their planograms, i.e. merchandising properly as per their branding and promotional guidelines and strategies. Their current manual process of visually inspecting thousands of photographs of retailers’ display cases, counters and selling fixtures was time-consuming and labor-intensive. A third-party, contracted by the fragrance conglomerate to monitor compliance, shared the problem with Infolytx.
Infolytx presented a solution that combined its extensive expertise in Computer Vision with Machine and Deep Learning models that would compare photographs against the brand’s merchandising requirements. The solution would then provide a confidence level for whether a store’s display, presented in photographs, was compliant with brand guidelines. With appropriate guidance from the brand’s human trainers, the AI system continually improved at identifying non-compliant displays and placement of the brand’s testing bottles, promotional gift items and advertising banners.
With the new AI solution, the company replaced its manual visual inspection process, which took 10-15 minutes, with an automated approach that estimated compliance in less than one minute. With the time and efficiency gains the company could focus on better merchandising strategies to boost brand fragrance sales. The new process also allowed for more timely interventions prior to big promotional events and improved retailer interactions.