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Marketing Mix Modeling in Excel Case Study & Prep
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 5h 11m | Size: 2.36 GB
Measure what your marketing is really worth - build a complete MMM and budget simulator in Excel
What you'll learn
• Explain what marketing mix modeling measures, and how it differs from attribution and experiments
• Assemble and quality-check a modelling dataset from spend, price, promotion and external data
• Run exploratory analysis that produces testable modelling hypotheses rather than decoration
• Fit and interpret a multiple regression in Excel using the ToolPak, LINEST and Solver
• Diagnose a model properly: fit, significance, residuals, collinearity and holdout validation
• Apply adstock to capture advertising carryover, and estimate the decay rate from your own data
• Model diminishing returns with saturation curves, and read them for budget decisions
• Fit adstock and saturation jointly with Solver, using constraints that keep results credible
• Chart adstock, response curves and marginal return curves so they change a decision
• Convert coefficients into contribution, ROI and marginal ROI without the classic errors
Requirements
• Comfortable working in Excel - formulas, cell references and charts
• No statistics background required; every concept is built up from scratch
• No programming required at any point in the course
• The Data Analysis ToolPak and Solver add-ins (both ship free with Excel)
Description
Marketing mix modeling is how serious marketing teams answer the question every budget depends on: what would have happened anyway, and what did our advertising actually create? This course teaches the method end to end, and you build every step yourself in Excel - no programming, no specialist software, no agency fee.
You start with the business problem and the shape of the data, move through exploratory analysis and regression, then add the two transformations that turn a plain regression into a real marketing mix model: adstock for advertising carryover and saturation curves for diminishing returns. From there you convert coefficients into contribution and return on investment, chart the response curves, and finish by building a working budget scenario simulator that a planning team can use every week.
Every lesson is built around decisions rather than theory. You will learn not only how to compute each number, but how to check whether it is trustworthy, how to explain it to finance and to channel owners, and where the method genuinely stops working.
The course ships with a complete Excel workbook holding three years of weekly data for a coffee brand. Everything downstream of the data tab is a live formula, so changing a decay rate recalculates the coefficients, the contribution, the response curves and the budget simulator in front of you. Six lessons walk through that workbook cell by cell, including one that rebuilds the optimiser from scratch so you can see exactly what Solver is doing. Because the dataset is simulated from known parameters, an answer key lets you check what your model recovered against the truth - and see, honestly, which parameters the data simply cannot identify.
The closing sections take you beyond the spreadsheet. You will learn why every modern open-source MMM is Bayesian, what Google Meridian, PyMC-Marketing and Meta Robyn each optimise for, and which one suits your data and your team. You will learn the causal inference that underpins all of it - confounders, counterfactuals, geo experiments and lift testing - and finish with the adjacent disciplines a full analytics function needs: price elasticity, forecasting, customer lifetime value and retail media measurement.
The final section is the one most courses leave out: how this work is actually done. Where modelling variables come from and why a project generates thousands of them, how to search that pool and what to test before you trust a result, and how to turn coefficients into a contribution chart and a due-to bridge that a client can act on. Also there is a section on interview preparation
Who this course is for
• Marketing analysts and data analysts who need to measure media effectiveness
• Marketing managers and planners who set or defend channel budgets
• Growth and performance marketers facing the loss of user-level tracking
• Consultants and agency strategists who advise clients on media investment
• Students and career changers moving into marketing analytics
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