{k} Kadoa Icon
Kadoa for location data

Location Intelligence

Monitor store footprints, expansion patterns, and closure signals at scale.
Talk to us

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

Extract the web. Power your decisions.

Book a demoGet Started

From Our Blog

View all posts
Real-Time Tenant Profile Tracking with Kadoa cover image

Real-Time Tenant Profile Tracking with Kadoa

How investors use Kadoa to extract and monitor tenant directories across Simon Property Group's 300 premium shopping destinations.

19 February 2026
What is Web Scraping? Enterprise Use Cases for 2026 cover image

What is Web Scraping? Enterprise Use Cases for 2026

A comprehensive enterprise guide to web scraping in 2026. How to run it at scale, where AI helps, how to stop losing engineering hours to maintenance, and what separates platforms worth evaluating.

13 February 2026
How AI Is Changing Web Scraping in 2026 cover image

How AI Is Changing Web Scraping in 2026

Explore how AI is changing web scraping in 2026. From automation and data quality to compliance to scalability and real-world use cases.

26 January 2026
Other Kadoa use cases:
Web Scraping AutomationReal-Time MonitoringCompany DocumentsRetail IntelligenceJob Market DataETL for LLMsView all use cases
ContactStatusTermsPrivacyLinkedin