Bayesian A/B Testing: A Better Way to Experiment
Moving beyond p-values to understand the probability of winning and expected loss using Bayesian inference.
Real Results
Case Studies
Real-world examples of how rigorous measurement, controlled experiments, and statistical analysis improved marketing performance across B2B SaaS and e‑commerce.
$2M+
Ad Spend Optimized
-35%
Avg CPA Reduction
3.2x
ROAS Improvement
Predicting Customer Lifetime Value (CLV) with Python
Using BG/NBD and Gamma-Gamma models to predict future customer value and identify high-value segments.
B2B SaaS
B2B SaaS Lowers Google Ads CPA by 35% with Better Measurement and Experimentation
Skeleton case study showing how the Google Ads optimization workflow can be applied to a B2B lead generation client.
35%
CPA Reduction
1.5x
Increase in Qualified Leads
E-Commerce
E-Commerce Brand Reduces CPA by 42% Through Systematic Creative Testing
How a DTC e-commerce brand improved Meta advertising performance through structured creative testing and proper measurement
42%
CPA Reduction
3.2x
ROAS Improvement
180%
Scale in Spend
Building a Marketing Mix Model (MMM) with Python
A step-by-step guide to building a basic Media Mix Model using Python, scikit-learn, and statsmodels to optimize marketing budget allocation.
Wondering what this could look like for your business?
Every engagement is different, but the same measurement and experimentation framework applies. Share a bit about your context and we'll explore together.