5 min read
Mixed Integer Programming for MMM Budget Optimization
Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
Articles & Insights
Deep technical analysis and strategic insights across data science, analytics, and business intelligence
1 min read
Part 3 | Hands-On Implementation: Building a Causal MMM from Scratch
A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset
16 min read
Practical optimizer scenarios in MMM: A step-by-step guide
Walk through real-world MMM optimization scenarios using Mixed Integer Programming. See how to discretize response curves, formulate constraints, and solve practical reallocation problems with actual numbers.
8 min read
Part 2 | Beyond Attribution: How Causal Inference complements Modern MMM
Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing
8 min read
Part 1 | Inside Google Meridian: Causal MMM at scale
How Google's Meridian brings causal inference to marketing mix modeling at global scale
8 min read
Marginal ROI vs ROI: Why efficiency beats revenue
Understanding why modern marketing optimization focuses on efficiency, not volume. Learn how marginal ROI transforms budget allocation and why the slope matters more than the height.
7 min read
Double Jeopardy in marketing: Why market leaders win twice
Understanding the mathematical law that explains why large brands not only have more buyers but also enjoy higher loyalty—and how it reshapes marketing strategy
10 min read
Michaelis-Menten equations in marketing mix modeling
Discover how the Michaelis-Menten equation models immediate diminishing returns in marketing. Learn when to use it instead of Hill functions and why it's the standard for direct response channels.
8 min read
The role of the optimizer in MMM
Understanding how MMM optimizers reallocate marketing spend across channels to maximize efficiency. Learn the step-by-step process from response curves to optimal budget allocation.
5 min read
Understanding Diminishing Returns: The Five Zones of Marketing Efficiency
Master the economics of marketing spend through the five zones of efficiency, from minimum threshold to market saturation. Learn when to scale, optimize, or reallocate budget for maximum ROI.
8 min read
Understanding Hill functions in marketing mix modeling
A comprehensive guide to saturation curves and their application in media optimization. Learn why Hill functions can be concave or convex and how they're used in Google Meridian.
7 min read
S-Curve vs C-Curve Response Models: When to Use Each
Exploring the mathematical foundations and practical applications of different response curves in marketing effectiveness measurement.
12 min read
Causal Identification, Uplift, and Contribution: The Three Pillars of Marketing Mix Modeling
Understanding the critical distinctions between causal identification, uplift, and contribution—and why conflating them leads to bad marketing decisions
4 min read
How to roll out a marketing mix model program : A practical implementation guide
A structured breakdown of deploying an MMM solution, covering project initiation, data foundation, modeling, and operationalization based on real-world implementation patterns.
3 min read
Interactive Adstock Calculator: Model Marketing Carryover Effects
Explore geometric adstock decay with our interactive calculator. Model how your marketing spend creates lasting impact through carryover effects.
9 min read
Probabilistic Forecasting in Travel Retail - Part 2: Gradient Boosting Quantile Regression
Deep dive into gradient boosting trees with quantile regression for robust uncertainty estimation in travel retail forecasting.
6 min read
Probabilistic Forecasting in Travel Retail - Part 1: Foundations
How to build robust forecasting models that account for uncertainty, seasonality, and external shocks in the travel retail sector.
10 min read
The market share paradox: Why industry leaders sometimes lose money
A critical analysis of the market share-profitability relationship through Bourantas & Mandes' dynamic model, revealing when bigger isn't always better
3 min read
Conjoint Analysis for Measuring Customer Willingness to Pay
A practical guide to using conjoint analysis for understanding product preferences and pricing optimization
6 min read
From lifecycle limits to markdown rules: The product constraints behind retail profit optimization
Why retailers can't escape markdown pricing—a deep dive into the lifecycle, inventory, and pricing constraints that shape modern retail optimization strategies
7 min read
Concavity and Network Flow in Profit Optimization: How Diminishing Returns Enable Efficient Solutions
Understanding why retail revenue functions are concave, how this property enables convex optimization, and the network flow interpretation that powers modern markdown pricing algorithms
5 min read
Inside the profit optimization problem: From price uncertainty to prescriptive decision-making
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty
6 min read
Why price elasticity is the most difficult parameter to estimate in retail analytics
A deep dive into why measuring price elasticity remains the biggest challenge in demand modeling, from data scarcity and endogeneity to seasonal confounding—and how modern techniques address these challenges
4 min read
Why a Hierarchical Modeling approach is essential in retail demand estimation
How hierarchical modeling solves the challenge of sparse data in retail by pooling statistical strength across products, enabling reliable demand parameter estimation even for low-volume SKUs
6 min read
The predictive demand model: Bridging historical data and markdown optimization
How predictive demand models serve as the critical bridge between historical sales data and prescriptive markdown optimization, providing both demand estimates and uncertainty quantification
5 min read
Why demand models must be monotonic in price: A deep dive into model choice for markdown optimization
Understanding why prescriptive pricing models must respect the law of demand, and how this fundamental constraint shapes our choice between GLMs and machine learning approaches
9 min read
Supply, Demand, and Price Elasticity: Lessons from the Fulton Fish Market
Analyzing 150 years of NYC's fish market data to understand how weather shocks reveal true price elasticity and market dynamics using causal inference
4 min read
Optimizing Vitamin Product Pricing Through Discrete Choice Modeling
How we leveraged Bayesian analysis and mixed-integer programming to optimize pricing strategy, resulting in revenue increase
3 min read
Implementing automated credit decisioning: A Data Science Approach
How we built an automated credit decisioning system that reduced decision time by 90% while improving accuracy by 25% using gradient boosted trees