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Evaluating store-level campaigns required identifying comparable control stores, but manual matching was slow, inconsistent, and difficult to scale. Without reliable benchmarks, campaign performance was hard to measure, reducing confidence in marketing investments and operational decisions.
Retail
Measuring Marketing Impact with AI-Powered Store Intelligence
Business Challenge
Our Approach
LatentView developed an AI-powered Store Match Engine that automatically identified statistically comparable control stores using store characteristics, historical performance, and trend similarity.
The solution established unbiased benchmarks for campaign evaluation, enabling accurate measurement of incremental impact while significantly accelerating store-level performance analysis and marketing decision-making.
Solution Highlights
- AI-powered control store matching
- Store similarity and performance analytics
- Campaign incrementality measurement
- Automated performance benchmarking
- Marketing effectiveness analytics
- AI-powered image tagging and metadata enrichment
- Automated product identification across lifestyle and catalog images
- Visual similarity recommendations for cross-sell and product discovery
- Scalable image processing with minimal manual intervention
- Reusable AI models across product categories
Key Highlights
Business Impact
Enabled data-driven allocation of $284M+ in marketing investments
90%
reduction in campaign evaluation turnaround time
Faster, more consistent measurement of store-level campaign performance
Improved confidence in marketing and operational decision-making
Incremental Revenue
$
0
M
improvement in conversion rate
0
%
increase in click-through rate
0
%
70% to 91%
Image tagging accuracy improved
Significant reduction in manual image labeling effort and maintenance costs
Client Profile
Client/Company
Leading Apparel Retailer
Industry
Retail
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