Overview
This collection brings together practical marketing analytics works focused on measuring marketing effectiveness and customer value. Rather than presenting individual models in isolation, these analyses show how different modeling assumptions influence business decisions—from assigning credit across customer journeys, to measuring channel effectiveness, to estimating long-term customer value.
The emphasis is on understanding why models disagree, what assumptions drive those differences, and how to interpret results responsibly.
Multi-Touch Attribution (MTA)
Understanding how attribution assumptions influence marketing decisions across customer journeys.
- When Attribution Models Disagree: How Different Methods Lead to Different Strategies (Article) (Code)
Media Mix Modeling (MMM)
Understanding how marketing assumptions such as adstock, diminishing returns, seasonality, and trend affect channel interpretation and budget allocation.
Customer Lifetime Value (LTV)
Understanding customer value through cohorts, retention, survival adjustment, and incremental value rather than relying on a single lifetime value estimate.
How to Navigate
If you’re new to marketing analytics:
- Start with Multi-Touch Attribution to understand customer journey measurement.
- Continue with Media Mix Modeling to learn how channel effectiveness depends on modeling assumptions.
- Finish with Lifetime Value to connect acquisition decisions with long-term customer value.
Why This Matters
Marketing decisions depend on how we measure success.
These notebooks demonstrate:
- how attribution methods allocate conversion credit,
- how realistic marketing assumptions affect media mix models,
- how customer value evolves over time,
- why different modeling choices can lead to different business strategies.
Together, they illustrate that marketing analytics is not just about building models—it is about making better decisions under uncertainty.