The Challenge
A mall management company operating premium food courts across 5 locations with 40+ F&B tenants on revenue share (15-25%) was struggling with:
- Monthly revenue targets frequently missed (achieved only 7 out of 12 months)
- No visibility into tenant performance until month-end
- Reactive problem-solving - issues discovered too late
- Tenant disputes over footfall attribution and revenue calculations
- Underperforming tenants dragging down overall food court revenue
- No data to support conversations with tenants about performance
The Solution
1. Real-Time Revenue Dashboard
Integrated POS systems from all 40+ tenants into one unified dashboard:
- Live revenue tracking by tenant, location, day-part, and day
- Compare actual vs projected revenue in real-time
- Automated revenue share calculations with full transparency
- Tenant-specific dashboards showing their performance vs peers
2. Predictive Analytics & Early Warnings
Built ML models to forecast monthly revenue and identify risks early:
- Predict end-of-month revenue by day 7 with 85% accuracy
- Automatically flag at-risk months 3 weeks in advance
- Identify underperforming tenants, cuisines, or day-parts
- Benchmark each tenant against category averages
3. Corrective Action Playbook
Created data-driven intervention strategies executed when risks detected:
- Tenant Interventions: Share performance data, suggest menu changes, offer marketing support
- Marketing Campaigns: Targeted promotions for underperforming cuisines or day-parts
- Event Planning: Organize food festivals when overall footfall is predicted to be low
- Tenant Mix Optimization: Data-backed decisions on tenant renewals and new signings
4. Trend Analysis & Insights
Analyzed patterns to optimize operations:
- Identified peak vs off-peak hours by location and day
- Discovered that weekend vs weekday revenue split varied dramatically by mall
- Found that 20% of tenants were driving 60% of revenue
- Spotted seasonal patterns and planned promotions accordingly
The Results
Targets Achieved
All monthly revenue targets met in the year after implementation
Revenue Growth
Year-over-year growth through better tenant management and optimization
Early Warning
Average time to identify and intervene on at-risk months
Revenue Disputes
Complete transparency eliminated all tenant revenue calculation disputes
Real Example: Mid-Month Intervention
Scenario: March 2024
Day 7: System predicted March would miss target by 12% based on current trajectory
Root Cause Analysis: Chinese and North Indian cuisine categories 25% below forecast; weekend lunch significantly underperforming
Actions Taken:
- Met with 5 underperforming tenants, shared benchmarking data
- Launched "Weekend Lunch Festival" with 15% discount from participating tenants
- Increased digital marketing spend for food court
- Two tenants adjusted menus based on competitor analysis
Result: Month closed at 102% of target vs predicted 88%
Client Testimonial
"Before PhyloAI, we only knew we missed our target on the 1st of next month. Now we know on Day 7 if we're on track or not. That 3-week head start changed everything. We can intervene with tenants early, run targeted campaigns, and actually hit our numbers. The tenant dashboards also improved relationships - they can see their performance vs category averages, so conversations about improvement are data-driven, not finger-pointing." - VP of Food Court Operations