Jul 21, 2024
Client
A retail store faced challenges in optimising store layouts, reducing energy costs, and understanding customer behaviour. Their goal was to use real-time data to enhance the shopping experience, boost sales, and improve operational efficiency.
Challenge
The store encountered several operational and strategic challenges:
Limited Foot Traffic Insights: Store managers lacked accurate data on customer flow, making it difficult to optimise layouts and product placements.
Energy Inefficiencies: HVAC and lighting systems ran continuously, regardless of occupancy, leading to high energy bills.
Ineffective Promotions: Inconsistent data on customer behaviour made it hard to assess the effectiveness of in-store promotions.
Missed Opportunities: Without real-time data, staff struggled to respond to peak traffic periods, resulting in missed sales opportunities.
Approach
We deployed a comprehensive sensor-driven solution to collect real-time data and provide actionable insights:
Occupancy Sensors - Installed at entrances and throughout the store to monitor foot traffic and customer flow patterns.
Environmental Sensors - Deployed to track temperature, humidity, and lighting conditions in real time.
Data Integration - Combined sensor data with POS systems to correlate foot traffic with sales performance.
AI-Driven Analytics - Built predictive models to forecast peak traffic times and recommend energy-saving strategies.
Dashboard Development - Created a user-friendly dashboard to visualise key metrics such as foot traffic, energy usage, and sales performance.
Solution
The sensor-driven solution delivered the following functionalities:
Sensors tracked the number of customers entering and exiting the store, as well as movement within specific aisles.
HVAC and lighting systems were automated to adjust based on real-time occupancy levels, reducing unnecessary energy usage.
Visualised customer flow to identify high-traffic areas and optimise product placement for maximum visibility.
Integrated sales and foot traffic data to assess the ROI of in-store promotions.
Notifications sent to staff during peak periods to allocate resources efficiently.
Impact
Enhanced Store Layout Efficiency:
Before: Product placements were based on assumptions and anecdotal feedback.
After: Data-driven heat-maps led to a 15% increase in sales of promoted items by optimising high-traffic zones.
Reduced Energy Costs:
Before: HVAC and lighting systems operated at full capacity during non-peak hours.
After: Automated adjustments based on occupancy reduced energy consumption by 25%, saving the client £500 per store monthly.
Improved Customer Experience
Before: Staff struggled to respond to peak times, leading to longer checkout lines.
After: Real-time occupancy data allowed better resource allocation, reducing wait times by 20%.
Increased Promotion Effectiveness:
Before: Limited data made it difficult to measure the success of in-store campaigns.
After: Correlating traffic patterns with sales data increased promotion ROI by 18%.
Scalable Insights:
The solution was rolled out to additional locations, providing centralised insights across all stores and enabling strategic decision-making at the corporate level.
