AI Shopping Center Analytics
The Problem
“Shopping center performance insights are slow and fragmented”
Organizations face these key challenges:
1
Data fragmentation across property management, leasing, sales reporting, foot traffic, and market datasets prevents a single source of truth
2
Tenant sales and traffic signals arrive late and are noisy, making it hard to detect underperformance or churn risk early
3
Tenant mix and co-tenancy decisions rely on manual analysis and intuition, limiting scenario testing and slowing execution
Impact When Solved
Portfolio-wide early warning system flags at-risk tenants 60–120 days sooner using sales/traffic/lease signalsAutomated tenant mix and rent optimization scenarios reduce leasing decision cycle time by 30–50%Standardized performance benchmarking across assets improves occupancy by 50–150 bps and lifts NOI by 1–3%