Predictive Targeting
Segmentation & Response Models
Restaurant Precision Marketing
Built a Random Forest classifier to identify the top 19% highest-probability
responders from a premium restaurant's customer base — transforming a -78%
average ROI into a profitable campaign by targeting 424 customers instead of
all 2,240, while achieving a 45% expected response rate.
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Customer Strategy
Attribution & Offer Design
Starbucks Attribution Framework
Course case analysis using Starbucks Rewards simulation data. Built an SQL
attribution pipeline and RFM segmentation to estimate a 15–20%
conversion-per-dollar improvement over mass targeting.
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