PunHin Marketing Science applies synthetic control modeling, Bayesian econometric calibration, and non-parametric experimentation to isolate net-new commercial revenue.
Swipe or scroll horizontally across cards to see how synthetic control matching separates base organic sales from campaign lift.
Top-down econometrics are anchored using real experimental priors, removing historical last-touch distortions and enabling dynamic budget shifts.
Exposed campaign regions are matched against un-exposed control geographies. Baseline organic sales are isolated and subtracted to measure causal ad lift.
Context AI evaluates mixed-language search terms and regional terminology that traditional English-only analytics engines fail to classify.
Top-down econometrics are anchored using real experimental priors, removing historical last-touch distortions and enabling dynamic budget shifts.
Exposed campaign regions are matched against un-exposed control geographies. Baseline organic sales are isolated and subtracted to measure causal ad lift.
A rigorous econometric pipeline designed to deliver unambiguous commercial clarity.
We construct a mathematically optimized "synthetic twin" for exposed marketing regions using un-exposed control geographies. This isolates external macroeconomic noise and isolates true, causal multicultural ad lift.
Y_synthetic = Σ (w_i * Y_control_i) | Optimization: Min ||Y_exposed - Y_synthetic||
Standard attribution tools misclassify code-switched or in-language search intent. Context AI parses multilingual search query clusters in real time to capture high-intent demand that competitors overlook.
NLP_Parse(Query: "prestamos para negocio") → Intent_Tag: COMMERCIAL_LOAN_HIGH_INTENT
Top-down econometric models are calibrated continuously using bottom-up geo-experiment priors. This eliminates the lag of traditional annual MMMs and delivers real-time marginal ROAS curves.
P(θ | Data) ∝ P(Data | θ) * P(θ_experimental_priors)
GrowthOS calculates exact marginal ROAS saturation points for each channel. Ad spend is automatically capped before diminishing returns set in and redirected to higher-yield corridors.
Marginal_ROAS = d(Revenue) / d(AdSpend) | Trigger: Cap when mROAS < Target_Threshold
| SCIENTIFIC FEATURE | TRADITIONAL MMM & LAST-CLICK | PUNHIN MARKETING SCIENCE |
|---|---|---|
| Incrementality Measurement | Correlation-based last-touch attribution | Synthetic control geographic experiments |
| Multilingual Disambiguation | English-only keyword taxonomy assumptions | Real-time Context AI search processing |
| Model Calibration Cadence | Annual or bi-annual retrospective reviews | Continuous Bayesian MMM triangulation |
| Saturation & Spend Curves | Static channel budget allocations | Dynamic marginal ROAS curve capping |
| Ad Platform Integration | Manual reporting spreadsheets | Direct API feed into GrowthOS Steering |
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