Improving Retail Space Utilization Through Footfall Analytics

Client Overview

A prominent retail real estate developer wanted to strengthen decision-making across its retail properties by gaining accurate visibility into store-level footfall, customer movement, and demographic patterns.

The objective was to move beyond manual observations and fragmented data, enabling leadership teams to make informed decisions on tenant placement, lease strategy, space utilization, and marketing performance.

Problem Statement

The client lacked a reliable system to track and analyze customer activity within individual retail shops.

Key challenges included:

• Inaccurate tracking of store-level footfall
• Limited visibility into customer demographics such as age and gender
• Absence of real-time data for tenant performance evaluation
• Difficulty assessing customer movement, engagement, and dwell time
• Limited analytics support for lease negotiations and marketing planning

For leadership, the challenge was not only about tracking visitor count. It was about converting customer movement data into actionable business intelligence for better asset performance.

Solution Provided

Decimal Point Analytics deployed a machine learning-based video analytics solution using CCTV feeds to track, classify, and analyze customer footfall across individual retail shops.

The solution included:

• Automated footfall tracking through ML-enabled video analytics
• Real-time dashboards for store-level visitor insights
• Heatmaps to identify high-engagement and low-engagement zones
• Path and dwell time analysis to understand customer movement
• Demographic classification covering male, female, and child categories
• Analytics support for tenant strategy, store performance evaluation, and marketing decisions

This gave the client a centralized, data-led view of customer behavior across retail spaces.

Outcome

The solution helped the client improve operational visibility and make faster, evidence-backed decisions across retail property management.

Business outcomes included:

• Improved marketing effectiveness through detailed footfall and demographic insights
• Optimized tenant placement based on customer movement and engagement
• Better space utilization across retail shops
• Reduced operational inefficiencies through automated tracking
• 40% faster decision-making in tenant and marketing strategies
• Improved cost control through data-backed space planning

Key Takeaway

Retail real estate decisions cannot depend only on assumptions or periodic manual checks. With AI-powered footfall analytics, real estate leaders can understand how customers move, engage, and interact within retail spaces, enabling stronger tenant strategy, better lease discussions, and improved property performance.

Looking to improve space utilization and retail performance with AI-powered analytics? Connect with Decimal Point Analytics to explore how video analytics can help turn customer movement into measurable business intelligence.