Boggi Milano is an Italian menswear brand with 235 stores across more than 61 countries, driven by innovation, technology, sustainability, and high-quality, high-performance materials. Its garments combine premium, often organic and recycled fabrics with style research aimed at a contemporary way of life.
The Challenge
In 2018, Boggi Milano set out to improve how it analyzed store performance and sought a partner with deep in-store analytics expertise. The team was unable to maximize and streamline labor, lacked a reliable way to identify stores with high traffic but low conversion, and had limited visibility into shoppers handled per labor hour.
The Solution
Boggi Milano partnered with RetailNext, whose platform is powered by the Aurora® all-in-one IoT sensor — featuring a wide field of view, HD color video, BLE, Wi-Fi, and beacon — and a predictive traffic model that forecasts counts in 15-minute increments. Rather than relying on fleet averages, the team analyzed visit duration and shoppers-per-labor-hour store by store to determine the right full-time-equivalent (FTE) staffing for each location, balancing selling and non-selling time.
The Results
Using Traffic Analytics to fine-tune labor allocation, Boggi Milano reduced headcount by 15% since 2018 while lowering payroll expense and increasing productivity. Over the same period the brand grew shopper yield by roughly 40%, lifted its conversion rate by about 5 points, and increased average ticket by around 15% — turning in-store analytics into a sustained performance engine.
