Marketing Science &
Precision CRO
Bridging the gap between raw data and revenue. My methodology applies Bayesian statistics and machine learning to marketing optimization, transforming uncertainty into predictable growth.
TOTAL ROI GENERATED
$12.4M
ACCURACY THRESHOLD
98.2%
EXPERIMENTS RUN
450+
Selected Projects
Experimental results backed by rigorous statistical validation. Click any card to read the full case study.
Mobile App Retention Analysis
Developed a survival analysis model to identify the critical churn inflection points for a Fintech app with 2M+ users. Implemented a predictive notification engine that targeted users at 85% churn probability.
Methodology
- Kaplan-Meier estimator implementation
- Random Forest classifier for feature importance
- Automated A/B testing on nudge intervals
KPIs IMPROVED
E-commerce CRO Experiment
Multi-variate testing on checkout flow for a major retail brand. Optimized friction points using heatmaps and Bayesian inference.
KPIs IMPROVED
Predictive Customer LTV Model
Engineered a machine learning pipeline to predict 12-month Customer Lifetime Value at the point of first purchase with 89% accuracy. This allowed for immediate segmentation of high-value versus low-value acquisition cohorts.
ERROR RATE
0.041
COHORT SIZE
850k
KPIs IMPROVED
Bespoke Attribution Modeling
Moving beyond last-click. Built a custom Markov-chain attribution engine to account for the multi-touch reality of modern B2B SaaS journeys.
UNCOVERED SPEND WASTE
$450,000
ATTRIBUTION ACCURACY
94%
The Scientific Process
Evidence Synthesis
We don't start with intuition. We ingest multi-source data to define the baseline variance and identify statistical outliers.
Bayesian Prior Setup
Every experiment is contextualized with historical results to calculate the required sample size for significant results.
Validation Loops
Continuous monitoring of p-values and confidence intervals ensures we never call a winner prematurely.
// Bayesian A/B Test Validation
function calculatePosterior(alpha_prior, beta_prior, trials, successes) {
const alpha_post = alpha_prior + successes;
const beta_post = beta_prior + (trials - successes);
return { alpha: alpha_post, beta: beta_post };
}
// Sample Size Determination (Power Analysis)
const power = 0.8;
const alpha = 0.05;
const effect_size = 0.02;
const required_n = calculateSample(power, alpha, effect_size);
console.log(`Experiment Validation Status: PASS`);
console.log(`Statistical Significance: 99.4%`);
Need Evidence-Based Growth?
Stop guessing. Let's build a data infrastructure that provides the clarity you need to scale with confidence.