The world has gone digital, and so has marketing. With more companies selling online, nearly every customer touchpoint is now measurable. This digital shift has been profoundly disruptive, not just for industries at large but especially for marketing: how companies grow, advertise, and measure what works has fundamentally changed.
A Look Back: Simplicity That Worked
Procter & Gamble pioneered demographic and econometric modeling in the 1970s, laying the groundwork for modern marketing mix analysis.
In the past, think the 60s and 70s, there was much less data. But marketers still found ways to measure impact. One of the most elegant methods was Marketing Mix Modeling (MMM), a regression-based approach that plotted media spend against sales to understand whether advertising efforts drove incremental revenue. Despite its simplicity, it worked, and it worked well. Because it was driven by real-world outcomes, not platform metrics.
More Data, More Doubt?
Today, everything is trackable. But are we actually measuring better? Modern marketing teams often rely on platforms like Google Analytics or the Meta Pixel for attribution. These platforms offer useful insights for day-to-day campaign optimization and channel performance. They can help track user journeys, flag friction points, and attribute conversions within their ecosystems.
But they are not designed to provide the full picture. They work within closed systems, and their models are often shaped to favor their own ad inventory. For basic insights, they're practical and powerful. But when it comes to deeper questions—like quarterly effectiveness or year-on-year ROI—you need more robust, independent tools. You need to go beyond the dashboard and do the homework.
The Problem of Over-Certainty
Google’s keynotes reflect a shift to AI-powered, data-driven attribution—replacing older models like linear or time decay. Useful for Google Ads, but limited beyond.
The abundance of digital data and tracking tools has created the illusion that marketing performance can be measured with pinpoint accuracy. With dashboards packed with metrics—click-through rates, impressions, view-through conversions—it’s easy to believe we have complete clarity. But in reality, much of this data is fragmented, biased toward the platform that reports it, and detached from actual business outcomes. This over-reliance on surface-level metrics leads to a false sense of precision. Marketers may feel confident optimizing what’s visible, but risk losing sight of what truly drives incremental growth.
Large, sophisticated companies know this. That’s why many of them are returning to or evolving traditional methods like MMM. These models can work independently of biased analytics platforms. They allow companies to isolate variables, test regions, stagger spend, and map results without relying solely on platform-reported conversions. It's slower. It's messier. But it's more honest.
How Marketing Mix Modeling Works
Marketing Mix Modeling (MMM) is a statistical technique used to estimate the impact of various marketing tactics on sales outcomes over time. Unlike platform-specific attribution tools, MMM operates at an aggregate level, relying on historical data rather than user-level tracking. It’s especially useful for answering high-level business questions like, “Which channels actually drive growth?” or “How should we reallocate our budget next quarter?”
The Fundamentals
At its core, MMM uses regression analysis to correlate marketing inputs (e.g., media spend, promotions) with business outputs (typically sales or profit). The model attempts to isolate the contribution of each input while controlling for external variables like seasonality, pricing, and macroeconomic trends.
Key Steps in the Process
- Data Collection – Gather time-series data on media spend, sales, pricing, promotions, competitor activity, and other relevant factors.
- Data Cleaning & Normalization – Address missing values, outliers, and inconsistent formats. Normalize across different channels or currencies if needed.
- Modeling – Apply regression techniques to estimate the relationship between variables. This may involve basic linear regression or more advanced techniques like Bayesian modeling or machine learning.
- Validation – Test model accuracy using out-of-sample data. Adjust for overfitting and ensure the model can generalize.
- Scenario Planning – Use the model to run simulations: What if TV spend is reduced by 20%? What happens if digital spend increases next quarter?
Types of MMM Approaches
- Classical Regression Models – Traditional linear or logistic regression used by many legacy CPG firms.
- Bayesian MMM – Introduces prior knowledge and confidence intervals; useful for uncertainty estimation.
- Machine Learning MMM – Leverages random forests or gradient boosting for flexibility, though interpretability may suffer.
- Open-source Models – Tools like Meta’s Robyn or Google’s LightweightMMM have made advanced MMM more accessible and modular.
Done well, MMM gives marketers a clear, cross-channel view of what’s truly working—independent of platform bias and attribution gimmicks.
Charting a Path Toward MMM Adoption
At Evergrow, we believe that Marketing Mix Modeling shouldn’t be limited to large enterprises with deep analytics teams. Anyone can begin the journey by gradually increasing their comfort with data and analysis. Here’s a practical pathway to adopt MMM thinking, no matter your starting point:
1. Start with Two Metrics
If you know nothing about modeling, begin by tracking just two things: ad spend and customer demand. Demand could be website form submissions, purchases, or leads. Choose a grain—daily, weekly, or monthly—and plot these two metrics on a simple line chart. Look for patterns. Are they correlated? Do sales follow spend with a lag? This is your entry point into causal thinking without needing equations.
2. Build in Spreadsheets
Once comfortable, bring your data into Excel or Google Sheets. Learn functions like CORREL, LINEST, or explore ANOVA for variance testing. Build a basic template that can ingest your channel spend and output directional relationships. The analysis becomes more precise but still intuitive. You’ll understand which levers are working, and which aren't.
3. Move to Scripts
For those ready to automate and scale, scripting allows for flexible, repeatable modeling. A simple Python script can take a CSV file, clean the data, run regressions, and output either a new CSV or console results. You can use libraries like pandas, numpy, statsmodels, or even build a Jupyter Notebook for live exploration. Key stages: ingestion, cleaning, modeling, and export. Decide where you want the output—CSV, notebook, report. R is also great for this, especially with packages like lm() or caret.
4. Test Mature Toolkits
Once you're comfortable with your own analysis, explore open-source MMM frameworks like Meta's Robyn or Google's LightweightMMM. These offer robust, battle-tested methods that integrate prior knowledge, simulate scenarios, and work across larger datasets. You don’t need to build from scratch—much of this already exists.
5. Build or Integrate a Platform
The final stage is operationalizing MMM. This could mean building a lightweight dashboard internally or plugging into tools that let you run regular models across your channels. The goal is to make MMM a living system that supports planning and post-campaign evaluation continuously.
Whether you’re just getting started or already exploring modeling, the path to MMM mastery is more accessible than ever.
Proof It Works
Brands like Wargaming and Dollar Shave Club have proven that taking control of attribution—whether through in-house systems or brutally clear metrics—can lead to breakout growth.
Final Thought
So here’s our challenge to you: Stop assuming dashboards tell the full story. Attribution isn’t about perfection. It’s about clarity, honesty, and asking better questions.
Let’s make marketing smarter, together.