When Boggi Milano's leadership team faced declining Sunday traffic at their flagship store in 2018, they discovered something surprising: the root cause wasn't marketing or merchandising. It was staff unfamiliarity with the store layout affecting customer conversions. This insight, uncovered through advanced in-store analytics, marked the beginning of a data-driven transformation that would lead to a 40% increase in shopper yield over the next five years.

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The Challenge Of Scaling Without Visibility

Operating 235 stores across 61 countries presents unique operational challenges. For Boggi Milano, the Italian fashion brand renowned for its contemporary menswear, which combines sustainable materials with cutting-edge design, the lack of unified performance visibility was becoming a growth bottleneck.

The company faced three critical blind spots that many multi-location retailers know too well. First, they couldn't effectively allocate labor across their store network. Some locations were overstaffed during slow periods while others struggled during peak times. Second, identifying which stores had high traffic but low conversion rates required manual analysis that was both time-consuming and often inaccurate. Finally, without visibility into the number of shoppers per labor hour, optimizing staff productivity remained a matter of guesswork rather than a strategy.

Marco Benasedo, Chief Information Officer at Boggi Milano, recognized that these challenges demanded more than traditional retail metrics. The company needed comprehensive, real-time insights that could translate complex shopper behavior into actionable operational decisions.

Beyond Basic Traffic Counting: The Power Of Integrated Analytics

RetailNext's platform, anchored by its Aurora sensor technology, offered something fundamentally different from basic people-counting systems. The Aurora combines multiple data capture capabilities within a single IoT device, including HD video, WiFi, and Bluetooth detection. But hardware alone doesn't solve business problems; it's the intelligence layer that transforms raw data into strategic insights.

The platform's deep learning AI processes behavioral patterns to provide a nuanced understanding of shopper journeys. Rather than simply counting entries and exits, the system analyzes dwell time, path patterns, and engagement zones. This granular visibility enables retailers to understand not just how many people enter their stores, but how effectively those visits convert to sales.

For Boggi Milano, this meant moving from reactive to predictive operations. The platform's algorithmic forecasting uses historical patterns to project traffic in 15-minute increments, enabling proactive staff scheduling. Conditional formatting and intuitive dashboards ensure that store managers can quickly identify opportunities without getting lost in data complexity.

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Solving The Labor Allocation Puzzle

One of Boggi Milano's most pressing challenges involved determining appropriate full-time equivalent (FTE) staffing for each of its stores. They needed to complete this task within just one week. Traditional approaches would have required extensive manual observation and estimation, likely resulting in significant inaccuracies.

Using RetailNext's Traffic 3.0 analytics, the team took a data-driven approach. They analyzed visit duration patterns, finding customers typically spent 17-18 minutes in-store. Combined with their determination that each sales associate could effectively serve 3.5 customers per labor hour, they established precise staffing requirements.

But Boggi Milano went further than simple averages. Recognizing that each store has unique characteristics, they customized the analysis by location. A flagship store in Milan's fashion district operates differently from a location in a suburban mall. This granular approach to labor optimization delivered remarkable results: a 15% reduction in headcount since 2018 while simultaneously improving customer service quality.

The company also established clear performance expectations based on their data insights. Sales associates now understand that roughly half their time should focus on direct selling activities, with the remainder dedicated to essential non-selling tasks like visual merchandising and inventory management. The data revealed that effective associates generate approximately one transaction every two hours. This benchmark helps managers coach performance and recognize excellence.

Simplifying Success Through Strategic KPI Selection

Many retailers fall into the trap of tracking too many metrics, creating confusion rather than clarity. Boggi Milano initially struggled with this challenge, overwhelming store teams with numerous KPIs that often sent conflicting signals about priorities.

Through careful analysis of their RetailNext data, the company made a strategic decision to focus primarily on Shopper Yield. This composite metric is calculated by multiplying conversion rate by average transaction value. This single KPI elegantly balances the two critical components of retail success.

The Performance Quadrant visualization became a powerful tool for store managers and area leaders. This simple report plots each location's performance on two axes: conversion rate and average transaction value. Stores appearing in different quadrants receive targeted coaching. High-conversion, low-ticket locations focus on building basket size. High-ticket, low-conversion stores work on engaging more browsers.

This focused approach prevents the common pitfall of optimizing one metric at the expense of another. When teams push too hard on conversion rates, they might rush customers and reduce transaction values. Conversely, focusing exclusively on large sales can mean missing opportunities with multiple customers. Shopper Yield keeps both factors in balance.

The company also shifted from daily to hourly performance analysis, revealing patterns invisible in aggregated data. A store might show acceptable daily conversion, but hourly analysis could reveal missed opportunities during lunch rushes or weekend peaks.

Measurable Impact: From Insights To Results

The five-year partnership between Boggi Milano and RetailNext has delivered quantifiable business improvements that validate the investment in advanced analytics:

• 40% increase in Shopper Yield: The focused KPI strategy drove consistent improvements in both conversion and transaction value

• 5-point increase in Conversion Rate: Better staff allocation and training based on traffic patterns improved customer engagement

• 15% increase in Average Ticket: Understanding customer behavior enabled more effective merchandising and sales strategies

• 15% reduction in labor costs: Optimized scheduling reduced payroll expenses while maintaining service quality

These results demonstrate that retail analytics isn't about replacing human judgment with algorithms. It's about empowering teams with accurate information to make better decisions. When store managers understand exactly when customers arrive, how they move through the space, and what drives conversion, they can create experiences that benefit both shoppers and the bottom line.

The Future Of Retail Intelligence

Boggi Milano's success illustrates a fundamental shift in retail operations. As Benasedo notes, the ability to transform complex analytics into actionable insights streamlines decision-making and enables rapid response to changing conditions. What once required weeks of manual analysis now happens in real-time.

The investment in reliable analytics technology like RetailNext's Aurora sensor platform represents more than operational improvement. It's about building competitive advantage through customer understanding. In an era where online retailers have perfect visibility into every click and scroll, physical retailers need equivalent intelligence about their spaces.

For retailers considering similar investments, Boggi Milano's journey offers valuable lessons. Start with clear business challenges rather than chasing technology for its own sake. Focus on metrics that drive balanced performance rather than overwhelming teams with data. Most importantly, recognize that the goal isn't just measurement. It's creating better experiences for both customers and associates.

As the retail industry continues evolving, the gap between data-driven and intuition-based operations will only widen. Boggi Milano's transformation demonstrates that with the right technology partner and strategic focus, retailers can illuminate opportunities hidden in their operational data, driving sustainable growth while enhancing the shopping experience.

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