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Kadoa for retail data

Retail Inventory & Pricing Intelligence

Detect inventory buildups, clearance patterns, and promotional intensity before earnings announcements.
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Success story

Problem
A fundamental equity fund was tracking inventory and prices of major brands and retailers with costly and brittle in-house scrapers.
Kadoa Solution
  • Automated monitoring of inventory levels across retail websites
  • Detection of clearance sales and promotional intensity changes
  • Out-of-stock tracking as demand indicator
  • Historical pricing pattern analysis for seasonality
ROI
  • 90% reduction in scraper maintenance costs
  • 5x more SKUs tracked vs in-house solution
  • Zero engineering resources required
"We spent 80% of our time fixing broken scrapers instead of analyzing data. Kadoa gave us back our analysts' time while expanding coverage."
— Portfolio Manager, Consumer-Focused Fund
Sample workflow

Brand E-commerce

Direct-to-consumer sites

Outlet Sites

Clearance and discount channels

Track

Inventory levels & pricing

Analyze

Promotional depth & frequency

Research Platform

Direct model integration
Sample results
Ticker
LULU
Category
Women's Apparel
Inventory Level
High (85% in stock)
Avg Discount %
22%
Week/Week Change
+15% discounts
Signal
Elevated inventory
Ticker
NKE
Category
Seasonal Footwear
Inventory Level
Medium (62% in stock)
Avg Discount %
35%
Week/Week Change
+20% on sale
Signal
Early clearance
Ticker
HD
Category
Power Tools
Inventory Level
Low (15% in stock)
Avg Discount %
0%
Week/Week Change
-30% availability
Signal
Strong demand
Ticker
TGT
Category
Electronics
Inventory Level
High (78% in stock)
Avg Discount %
18%
Week/Week Change
+12% promos
Signal
Promotional push
Ticker
PTON
Category
Equipment
Inventory Level
Very High (95% in stock)
Avg Discount %
30%
Week/Week Change
New discounts
Signal
Inventory pressure
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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