Vuori DTC Measurement Strategy Engine
Marketing Mix Modeling via zero-cost Python regression
Client Archetype
High-growth DTC apparel brand. Vuori operates across Google Search, Meta Ads, and Programmatic Display, scaling fast with increasing pressure to justify media investment beyond last-click attribution.
Core Challenge
Post-iOS 14 signal loss rendered click-attribution unreliable. With 30–40% of conversions dark to pixel-based tools, Vuori needed a structural approach to media measurement that didn't depend on cookies or app-level events.
Methodology
Data Engineering
Synthetic data generation: 104 weeks of omni-channel spend with built-in diminishing returns and macro noise. Weekly revenue, channel spend, and external variables (seasonality index, macro index) were modeled to reflect real-world media dynamics at scale.
Attribution Modeling
Log-log feature transformation, elasticity extraction, and marginal ROAS layer via Python regression engine. By transforming spend and revenue into log space, the model captures diminishing returns as a structural property (not a tuning parameter) and surfaces channel-level elasticities that hold under budget shifts.
Executive Automation
Media saturation curves isolate diminishing returns via an automated performance allocation matrix with mROAS triggers. Output is a decision-ready table: each channel's elasticity, average weekly spend, marginal ROAS at current investment, and a plain-language strategic action derived from threshold logic.
Executive Performance Matrix
| Channel | Elasticity | Avg Weekly Spend | mROAS | Strategic Action |
|---|---|---|---|---|
| Google Search | 0.0375 | $38,657 | $3.44 | Aggressive Scale |
| Meta Ads | 0.0378 | $59,688 | $2.24 | Maintain & Optimize |
| Programmatic | 0.0044 | $12,857 | $1.20 | Saturation Warning |
Key Insight
Google Search is significantly under-funded relative to its marginal return ($3.44 mROAS vs. a $3.00 scale threshold). Meta Ads is performing efficiently at current investment levels. Programmatic display has crossed into saturation. Each additional dollar is returning $1.20, suggesting budget should be capped and reallocated toward Search to maximize incremental revenue.
Explore the Full Model
The complete Python implementation is available in Google Colab, covering data generation, log-log regression, elasticity extraction, and the allocation matrix.
View Google Colab Notebook