Unlock Supermart Optimization with Agrex.ai—transforming layouts, customer journeys, and revenue per square foot.
Executive Summary: The AI-Powered Advantage in Supermart Retail
The physical supermart floor is the last frontier of retail data. A leading national supermart chain recognized that its sales growth was plateauing due to reliance on traditional, static merchandising strategies and a lack of real-time insights into customer behavior.
This case study details how Agrex.ai’s Video Analytics platform transformed the retailer’s existing surveillance infrastructure into a dynamic business intelligence tool. By accurately mapping the anonymous customer journey and identifying true product engagement, the chain optimized product placement and space utilization, resulting in a significant, measurable uplift in sales and operational efficiency.
The Challenge: Overcoming "Retail Blind Spots"
Inefficient
Layout
High-cost floor space was being wasted in “dead zones” (areas with low foot traffic or product interaction), reducing the overall Revenue Per Square Foot (RPSF).
Sub-Optimal Merchandising
High-margin impulse items, though well-stocked, were underperforming because their placement relied on intuition rather than empirical data.
Customer
Friction
Congestion at key points (e.g., checkout, popular displays) led to frustration and missed sales opportunities (basket abandonment).
Inability to
A/B Test
There was no efficient, data-backed method to test new display concepts or layout changes across multiple stores.
The Agrex.ai Solution: Real-Time Shopper Intelligence
Agrex.ai implemented its AI-powered video analytics solution across the pilot locations, focusing on three core computer vision capabilities:
1. Heatmap and Dwell Time Analysis
Methodology: AI models process video feeds to generate Heatmaps, visually representing areas where customers spent the most (Hot Spots) and least (Cold Spots) time. The system also calculates (product catalogue-wise) zone-wise footfall counts and tracks average dwell time for every section and product category.
Insight Generated: Differentiates between mere pass-by traffic and genuine product engagement (i.e., stopping and interacting with a shelf), providing powerful data for physical store strategy:
Zone Performance Benchmarking: Provides an objective, quantifiable score for every department and large zone (e.g., fresh produce, home goods, packaged snacks) based on total footfall and average dwell time. This enables inter-zone comparison for strategic resource allocation.
- Dead Zone Identification: Accurately identifies and quantifies underperforming retail square footage (“cold spots”) where footfall is low and engagement is negligible, guiding decisions to re-merchandise or convert space for higher-value activities.
2. Conversion Funnel Analysis
- Methodology: Defines a multi-stage Customer Journey Funnel within the supermart, tracking anonymized shoppers to pinpoint where interest drops off before the checkout area:
Store Entry: Number of customers entering the supermart.
Interest Stage (2+ min dwell): Shoppers who spent more than 2 minutes within a specific zone.
Engagement Stage (10+ min dwell): Shoppers who spent more than 10 minutes, indicating deeper interaction.
Product Interaction: Customers who stopped at specific product displays or engaged with interactive elements.
Insight Generated: Precisely quantifies conversion rates for marketing and merchandising efforts at each stage, allowing the retailer to optimize product placement based on observed customer attention.
3. Queue Management
Methodology: Monitors the final transaction phase by tracking the conversion rate and efficiency at the Point of Sale (POS):
Reached POS (Point of Sale): Customers entering the checkout area.
Converted (Billed): Customers successfully completing a transaction.
Abandoned (Exited without Bill): Customers leaving the POS area or the store without a completed transaction due to perceived or actual long wait times.
Insight Generated: Provides real-time metrics on queue lengths and wait times, enabling managers to receive instant alerts and proactively staff checkout lanes to minimize customer friction and reduce basket abandonment.
Unlock Every Square Foot: See Your Supermart's True Potential, Live .
Implementation & Measurable Results
The retailer focused on optimizing two high-impact areas: the fresh produce section (a key traffic driver) and the seasonal promotional area (a key high-margin space).
Conclusion: Future-Proofing Retail with Agrex.ai
| Optimization Area | Challenge Identified | Data-Driven Action | Measurable Result |
|---|---|---|---|
|
Product Placement Impulse Items |
Seasonal high-margin display had a low 6% Conversion Rate due to placement in a high-speed transit zone. | Display was moved to a high-dwell zone, specifically the intersection of two major anchor aisles, as highlighted by Heatmaps. | 45% increase in the display's Conversion Rate in the first month. |
|
Space Utilization "Dead Zone" Recovery |
A specialized food aisle had a 25% lower footfall rate than the store average, wasting premium retail space. | A visually engaging digital signage display was placed at the aisle entrance to act as a traffic magnet, a strategy validated by Flow Analysis. | An 18% increase in the aisle's overall footfall, significantly improving its RPSF. |
|
Operational Efficiency Checkout Friction |
Average peak-hour wait time at checkouts was creating customer frustration and abandonment risk. | Real-time Queue Alerts were configured to notify managers when the line exceeded a 2-minute wait or 3 customers. | 52% reduction in average peak-hour wait time and a drop in overall transaction friction. |
See Your Store's Uplift
Agrex.ai uses anonymous video analytics. Our platform detects and tracks movement patterns and behaviors (like dwell time and flow) but never captures, stores, or processes any personally identifiable information (PII) such as faces. All data is processed in real-time and aggregated.
No. One of the platform’s key benefits is that it leverages your existing CCTV infrastructure. Agrex.ai is a software layer that connects to your current video feeds, transforming your security system into a powerful business intelligence tool, minimizing upfront hardware investment.
After the initial one-week calibration period, the Agrex.ai platform begins providing actionable data within 72 hours. Most retailers begin implementing data-driven changes and see measurable impacts (like the 18% increase in aisle footfall mentioned) within the first 30 days.
Our scalable, cloud-based solution is designed to deliver optimization results for any size of modern retail format, from smaller neighborhood stores to regional supermart and hypermart chains. The value is derived from the data, not the scale of your current operation.