Articles & Case Studies

A collection of frameworks, best practices, and real-world case studies on data.

Marketing Mix Modeling in 2026, Part 1: Foundations, Bayesian MMM, and Google Meridian
Part 1 of a practical MMM series: define the right economic outcome, build adstock and saturation response curves, handle uncertainty, and apply Google Meridian-era measurement principles.
Marketing Mix Modeling Google Meridian Bayesian Modeling Marketing Measurement
How to Triangulate MMM with Incrementality: A Practical Calibration Framework
A technical and executive guide to calibrating Marketing Mix Models with incrementality experiments, measuring whether the model improved, and deciding what to do when the evidence disagrees.
Marketing Mix Modeling Incrementality Geo Testing Causal Inference Bayesian Modeling Budget Allocation Model Calibration
Counterfactual & Geo Testing: The Gold Standard for Modern Marketing Measurement
A private-equity style playbook for building a counterfactual modelling capability and geo experiment engine that anchors strategic budget allocation with MMM, attribution, and incrementality.
Counterfactual Modeling Geo Testing Incrementality Marketing Mix Modeling Measurement Strategy Causal Inference Data Quality
The Ultimate Guide to A/B Testing Statistics: From Theory to Practice
Go beyond p-values. This deep dive covers everything from choosing the right statistical test for your metrics to calculating sample size with power analysis. A complete guide for data-driven professionals in e-commerce, marketing, and product.
ab testing statistics data science product analytics conversion rate optimization power analysis
Manual Predictions & Backpropagation in a 2–2–1 Neural Network (Sigmoid + MSE)
A full, step-by-step walkthrough that computes a 2–2–1 neural network by hand: forward pass, MSE loss, formal objective, dependency map, backpropagation derivations, and one complete weight update—ready-to-learn math with concrete numbers.
neural networks machine learning backpropagation algebra calculus deep learning tutorial
The Strategic Imperative of RAG: An Investment Framework for Unlocking Enterprise Knowledge
Generative AI is a transformative platform, but its true enterprise value is unlocked when grounded in proprietary data. This analyst report provides a strategic framework for investing in Retrieval-Augmented Generation (RAG), detailing the technology, a portfolio of high-ROI use cases, and a phased roadmap for implementation. RAG is not an experiment; it is a critical investment in building a durable competitive moat.
RAG Generative AI LLM AI Strategy Enterprise AI Vector Database Knowledge Management

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