The Challenge
In today's competitive retail environment, providing exceptional customer service while managing labour costs presents a significant challenge. Many retailers rely on outdated staffing models based on historical sales data or manager intuition rather than actual customer behaviour. This approach frequently results in misaligned staffing, with too many associates in low-traffic areas while customers in busy departments leave without purchasing because they couldn't find assistance.
A major national retail chain with over 1,000 locations was experiencing these exact issues. Their internal analysis revealed that up to 20% of potential sales were lost because customers couldn't find assistance when making purchasing decisions. Meanwhile, labour costs- their second-largest expense after inventory - continued to rise as staff stood idle in less frequented departments. Store managers lacked visibility into real-time customer movement patterns, making it impossible to position associates where they were most needed throughout the day.
The retailer needed a solution to provide actionable insights into customer traffic patterns, optimise staff positioning in real time, and ultimately increase sales while reducing unnecessary labour costs.
The Staffcaster Solution
Sensitel addressed these challenges by implementing Staffcaster, a real-time, cloud-based service that leverages existing Wi-Fi networks (such as Aruba Networks) and additional store-based sensors to track customer movement and recommend optimal staffing levels. Unlike traditional staffing models, Staffcaster processes streaming data from mobile phones, GPS devices, smart meters, and other connected devices to provide dynamic, hour-by-hour staffing recommendations.
The implementation began with the integration of Staffcaster with the retailer's existing Aruba Wi-Fi infrastructure, requiring minimal additional hardware investment. Sensitel worked closely with store operations teams to map layouts and define key zones for customer traffic monitoring. Within weeks, the system collected and analysed customer movement data across all departments.
Staffcaster provided store managers with an intuitive dashboard featuring heat maps of store traffic patterns by zone. The dashboard also included automated recommendations on staff positioning to meet customer demand before sales opportunities were lost1. The system also delivered detailed analytics on average customer dwell times by department and shift, enabling managers to identify trends and optimise staffing schedules accordingly.
For unexpected surges in customer traffic, Staffcaster automated workflows by sending email and SMS alerts to on-call staff, enabling quick response to changing conditions1. Additionally, the system's zone affinity and cross-flow pattern analysis helped the retailer understand customer shopping journeys throughout the store, revealing that certain department combinations had low crossover rates, while others showed strong correlation.
Business Results
After implementing Staffcaster across its locations, the retailer achieved remarkable improvements in both customer service and operational efficiency:
Sales increased by 15% in departments that previously suffered from understaffing, directly attributable to improved customer assistance at the point of purchasing intent
Labour costs decreased by 12% overall while maintaining or improving service levels through more efficient staff allocation.
Customer satisfaction scores rose by 22% as measured through post-purchase surveys, with specific improvements in the "ease of finding assistance" metrics
Store managers reported spending 30% less time manually adjusting staff positioning, allowing them to focus on higher-value activities.
Cross-selling opportunities improved by 18% as staff became more available to suggest complementary products.
The retailer gained valuable insights for merchandising decisions based on previously unknown customer traffic patterns. In one instance, a newly developed store was redesigned so both floors can be visible to visitors at the same time and that increased flow of visitors.
The difference was clear for shoppers: no more hunting down an already-swamped sales associate. Instead, knowledgeable sales associates were positioned to help customers make purchases while creating cross-sell and upsell opportunities. For store managers, Staffcaster eliminated the guesswork from staff positioning, providing data-driven recommendations that maximised sales and labour efficiency. Since then, the retailer has expanded its use of Staffcaster to include inventory management optimisation. This ensures that stock replenishment aligns with customer traffic patterns, further enhancing the shopping experience and operational efficiency.