5 min
ROI Is Not Enough. You Need Time-to-ROI.
Two initiatives can show the same ROI and still deserve very different decisions. The missing variable is time-to-ROI: how fast the initial investment is recovered.
Articles & Insights
Deep technical analysis and strategic insights across data science, analytics, and business intelligence
8 min
From Propensity Scores to Learning Systems: What Most CRM Teams Get Wrong
Most CRM organizations don't have a feedback loop. They have a broadcasting system. The shift from propensity to uplift modeling changes everything.
6 min
Your MMM Should Not Predict Revenue
Revenue is a derived quantity. Marketing influences demand, units sold, not price. Modeling revenue directly entangles two mechanisms and produces misleading attribution.
5 min
MIP for Marketing Budget Allocation: A Complete Guide
Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
16 min
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
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
Google Meridian: How Causal MMM Works at Enterprise Scale
How Google's Meridian brings causal inference to marketing mix modeling at global scale
8 min
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
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
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
MMM Budget Optimization: How to Allocate Marketing Spend
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
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
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
S-Curve vs C-Curve: Which Response Model Fits Your Marketing Channel?
Exploring the mathematical foundations and practical applications of different response curves in marketing effectiveness measurement.
12 min
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
8 min
What is Marketing Mix Modeling? A Complete Guide
A comprehensive introduction to Marketing Mix Modeling (MMM) — what it is, how it works, when to use it, and how it differs from attribution. Written for practitioners and decision-makers.
4 min
MMM Implementation Guide: From Data to Production in 5 Phases
A structured breakdown of deploying an MMM solution, covering project initiation, data foundation, modeling, and operationalization based on real-world implementation patterns.
3 min
Adstock Calculator: Model Marketing Carryover and Decay Rates
Explore geometric adstock decay with our interactive calculator. Model how your marketing spend creates lasting impact through carryover effects.
6 min
For Markdown modeling, predict sell-through rate and not sales volume
Sales volume mixes demand and inventory effects. Sell-through rate captures what actually matters: how fast inventory clears under pricing and context constraints.
5 min
Demand Planning des nouveaux produits : le plus grand angle mort du forecasting
Pourquoi le forecast de lancement produit est souvent un exercice politique plus qu'une estimation robuste, et comment le transformer en système probabiliste et explicable.
10 min
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
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
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
Conjoint Analysis for Measuring Customer Willingness to Pay
A practical guide to using conjoint analysis for understanding product preferences and pricing optimization
6 min
LLM Systems: Architecture First, Framework Second
LangChain, LangGraph, Claude ADK, Google ADK, PydanticAI — different names, different abstractions, same promise. This article cuts through the framework noise and identifies the five architectural properties that actually determine whether an LLM system survives production.
9 min
Pourquoi les projets d'IA agentique échouent: un problème d'operating model pas seulement de technologie
La plupart des échecs GenAI en entreprise ne viennent pas d'un mauvais modèle. Ils viennent d'un mauvais design de workflow, d'une absence de mandat et d'une architecture incapable de faire agir un système.
4 min
Why Smarter AI Does Not Automatically Mean Better Architecture
Modern LLMs are objectively better at nuanced language tasks than legacy systems. But when you zoom out from a single prompt in a playground to a production environment processing millions of requests, the paradigm shifts entirely. Here is why using an LLM as a universal parser and router is often the wrong architectural choice.
6 min
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
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
Markdown Pricing Optimization: Stochastic Programming for Retail
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty
6 min
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
Hierarchical Bayesian Modeling for 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
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
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
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
Optimizing Vitamin Product Pricing Through Discrete Choice Modeling
Using Bayesian demand modeling and mixed-integer programming to set optimal prices for a new product line — balancing revenue, cannibalization, and market share
3 min
Implementing automated credit decisioning: A Data Science Approach
Designing an automated credit decisioning system with gradient boosted trees — from manual review to real-time scoring in production
12 min
Why LLM-Only Parsing Breaks in Production — And What to Do Instead
LLMs can extract structured data from anything — until they cannot. This article documents the failure modes of LLM-only parsing in production pipelines, and presents a layered architecture where determinism comes first and the LLM is used only where it is structurally irreplaceable.
6 min
Stop Talking About Agentic Commerce. Start With Agentic RAG.
Most companies are not ready for autonomous commerce agents. The practical starting point is Agentic RAG: systems that retrieve business context, reason over it, and produce decision-ready outputs.
9 min
End-to-End Marketing Mix Model Architecture: From Data Pipeline to Optimization API
A comprehensive guide to deploying production-ready Marketing Mix Models—covering data ingestion, model training, cloud deployment, and real-time optimization APIs for budget allocation