Marketing Data Science: MMM, Attribution & Measurement
Develop a practical understanding of marketing attribution and measurement from a data science perspective. This coaching package is designed for data scientists, analysts, and marketing analytics professionals who work with or want to deepen their knowledge of attribution, media effectiveness, and marketing measurement. Depending on your goals, we can work on: • Multi-Touch Attribution (MTA) concepts and modeling approaches • Marketing Mix Modeling (MMM) and incrementality • Choosing between spend, impressions, clicks, conversions, and other media inputs • Evaluating channel performance and ROAS • Understanding attribution bias and limitations of user-level tracking • Model validation, backtesting, and robustness checks • Reconciling MMM, MTA, platform-reported attribution, and business KPIs • Handling paid and non-digital channels such as TV, offline media, and brand activity • Forecasting and budget-allocation use cases • Communicating attribution results to marketing and business stakeholders • Reviewing real-world measurement frameworks, model outputs, or project designs Sessions are tailored to your current role, technical background, and business context. The goal is not just to explain attribution theory, but to help you reason through the trade-offs, assumptions, and implementation decisions that arise in real-world marketing measurement. This package is especially useful for professionals working in Marketing Data Science, Growth Analytics, Product Analytics, Performance Marketing, or Marketing Measurement. We can also use your own anonymized business problem, model design, or measurement setup as the basis for the sessions.
What's included
Sessions are highly practical and tailored to your current role, technical background, and business context. We start by identifying the measurement problem you are trying to solve, then work through the relevant methodology, assumptions, and trade-offs together. Rather than focusing only on theory, I use real-world examples and encourage clients to bring anonymized business problems, model designs, dashboards, or measurement frameworks to discuss. We can review existing approaches, challenge assumptions, compare alternatives, and turn the discussion into concrete next steps. My goal is to help you develop the reasoning needed to make better measurement decisions independently, not simply apply a predefined attribution model.
- A clear framework for choosing the right marketing measurement approach for your business problem — including when to use MMM, MTA, experiments, or a combination of methods.
- A structured way to evaluate attribution models, including assumptions, validation methods, bias, robustness, and common failure modes.
- A practical measurement plan for your own use case, covering KPIs, media inputs, model granularity, validation, and how results should inform decisions.
- A stronger understanding of how to reconcile MMM, MTA, platform-reported attribution, experiments, and business KPIs when they disagree.
- - A framework for communicating marketing measurement results, uncertainty, and trade-offs to technical and non-technical stakeholders. Me
Meet your expert
Carlos Andres A.
Real-World Data Science Coaching: Forecasting, Marketing, 14 Years Exp
I’m a statistician by profession and a programmer by hobby with a Master’s Degree in Business Analytics. With more than 14 years of professional experience my main interests are in the field of Data Science particularly in forecasting methods. I’m looking to improve my knowledge in advanced modelling tools like ensemble, deep learning. I’ve had more than 4 years of experience leading multidisciplinary teams in data science projects. I love working in projects where I can help and mentor other colleagues. I'm from Colombia and moved to Germany directly by getting a job at Flixbus so I also have experience in how to successfully move to a new Country with a job.