Up to 59% of major fashion retailers in the UK are already using some form of retail analytics stemming from facial recognition; for all stores, the figure oscillates around 25%. But the need stretches across industries, beyond retail – it includes players in tech, finance, casinos, law enforcement and high-traffic events.
While the main motivator for retailers using facial recognition-based analytics may vary (sometimes extending to loss prevention), benefits abound – the ability to gauge responses to product displays, to monitor traffic and dwell times. All of these add up to an increase in sales and a more granular view of the factors at play.
Analytics and customer profiling give you unprecedented insight into the habits and preferences of your target audience. A detailed demographic breakdown that includes gender and age allows you to see, for example, if there’s a surge of senior male customers on weekday afternoons or of young female customers on weekend mornings. This, in turn, allows you to adapt the size, skills and focus of your sales force while crafting product displays that cater to specific parts of your audience at specific times.
What’s more, by correlating age and gender metrics with receipt timestamps, you can draw conclusions about the kinds of products certain types of customers are more likely to buy, at what time of day and in what quantity. All in all, well used in-store metrics bring you advantages usually reserved for online sellers.
To maximize the power of in-store metrics and create a more efficient retail experience, get in touch with the VisageCloud team.
Let us explore together how VisageCloud can best work for your use case