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Kadoa for location data

Location Intelligence

Monitor store footprints, expansion patterns, and closure signals at scale.
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Success story

Problem
A leading PE firm with a retail and restaurant portfolio was manually tracking store footprints quarterly through consultants.
Kadoa Solution
  • Automated daily monitoring of 10,000+ store locations
  • Early detection of temporary closures converting to permanent
  • Competitor expansion tracking in portfolio company territories
ROI
  • Faster detection of distress signals
  • 5X increased location coverage
  • 80% in-house engineering time saved
"Kadoa automates the location footprint tracking for our entire company universe. This intelligence is core to our team."
— Managing Director, PE Fund
Sample workflow

Store Locators

Company websites

Track

Monitor location changes daily

Detect

Identify expansions & closures

API Integration

Direct integration

Change Alerts

Closure & contraction warnings
Sample results
Portfolio Company
FastCasual Dining Co
Location Signal
Accelerating Closures
Locations Affected
12 stores (8% of footprint)
Geography
Midwest markets
Revenue at Risk
$45M ARR
Severity
Critical
Portfolio Company
Premium Fitness Chain
Location Signal
Competitor Encroachment
Locations Affected
23 locations at risk
Geography
California, Texas
Revenue at Risk
$67M ARR
Severity
High
Portfolio Company
Specialty Retail Brand
Location Signal
Rapid Expansion
Locations Affected
+18 new stores
Geography
Southeast region
Revenue at Risk
+$34M projected
Severity
Positive
Portfolio Company
Quick Service Restaurant
Location Signal
Format Shift Detected
Locations Affected
Converting 45 locations
Geography
Urban markets
Revenue at Risk
$120M transitioning
Severity
Medium
Portfolio Company
Home Services Network
Location Signal
Territory Abandonment
Locations Affected
Exiting 3 states
Geography
Pacific Northwest
Revenue at Risk
$28M ARR loss
Severity
High
Make Or Buy

Why top firms switched to Kadoa

Team & Budget Impact
In-House:
2 senior data engineers + ongoing maintenance
With Kadoa:
~40% lower operational cost
Setup
In-House:
Rule-based, manual coding, breaks frequently
With Kadoa:
Auto-generated
Data Quality
In-House:
Manual validation, constant quality issues
With Kadoa:
High quality out-of-the-box, automated data validation
Maintenance
In-House:
Was slow, fragile, and costly
With Kadoa:
Fully managed
Time to Dataset
In-House:
2 to 4 weeks per source
With Kadoa:
A few hours
Scalability
In-House:
Linear cost growth, engineering bottleneck
With Kadoa:
10x sources, same cost

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