Store Performance Partners With @Quividi

Adrian J Cotterill, Editor-in-Chief

Relatively new startup Store Performance has partnered with Quividi to provide in-store insight to its fast food, coffee and convenience chain customers.

Tim Butler, Director Store Performance told us “Quividi is the gold standard in audience demographics measurement, we are excited to add this solution to SAM and our SAM AI project. Allowing hospitality operators to deeply understand their footfall with actionable insights will help these business as they re-open their doors.”

In hospitality outlets, many customer behaviours have changed during the pandemic. As we emerge from the pandemic they will surely change again. Understanding this behavioural change will give companies a competitive advantage.

Quividi measures in real-time the footfall as well as the age and gender of your customers and their attention time to a point of interest. Combined with in-store sales, it offers a potent source of insight into your customers to increase conversion. Store Analysis Machine (SAM) will use the solution to offer store measurement and contextual promotion to digital menus, kiosk and drive thru.

Quividi is also participating in SAM AI, a ground-breaking, scaled analysis solution for retail that is co-funded by InnovateUK. Store Performance is looking for participants in the customer discovery phase of this project, details of the project and an application form can be found here.

Laetitia Lim, Quividi’s CEO said “Turning audience insight into real-time business decisions is key to brick&mortar, which needs to have the same reactivity as e-commerce. With Quividi’s audience & content intelligence platform now integrated with Store Analysis Machine, brands and retailers can plan and deliver targeted campaigns to the right audience and measure and optimize performance in real-time”.

As hospitality tackles the challenges of the ‘New Normal, Store Performance and Quividi can help improve understanding, insight and promotion that reflects changing trading patterns at scale.

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