How to Create a Data-Driven Marketing Mix Model

Want to boost your marketing ROI by up to 50%? A Marketing Mix Model (MMM) can help you allocate budgets smarter, predict campaign outcomes, and measure what truly works. Here's how you can start:

• What is MMM? A statistical approach that separates natural business performance from marketing impact to evaluate ROI.
• Why use it? Businesses using MMM often see a 15–20% ROI improvement.
• Steps to build one:
  1. Gather and clean data (ad spend, sales, external factors like seasonality).
  2. Use techniques like regression or machine learning to analyze.
  3. Test and validate results with methods like hold-out forecasting.
  4. Apply insights to optimize budgets and strategies.

Tools to use: Python (Pymc-marketing, Meridian) or R (Robyn).

MMM isn't just about tracking past performance - it's about planning smarter for the future. Learn how to build and refine your model step-by-step below.

Marketing Mix Model Basics

Definition and Purpose

Marketing Mix Modeling (MMM) is a statistical approach that breaks down business performance into two key components: base drivers (natural performance) and incremental drivers (marketing impact). Brands rely on MMM to determine which initiatives contribute to ROI. This separation helps create accurate, data-driven marketing strategies.

Why Use Data Analysis

MMM stands out because it combines insights from both trackable and non-trackable channels. Interestingly, 41% of marketers report challenges with data collection and analysis, emphasizing the importance of comprehensive measurement tools.

"A strong data foundation is crucial to getting accurate and actionable results from your MMM. The quality and completeness of your data directly impact the model's ability to isolate the true impact of marketing activities, control for external factors, and provide accurate insights for optimization and forecasting." - Pieter van Groenendael, Senior Solutions Engineer at Supermetrics

MMM works hand-in-hand with tactical tools like Multi-Touch Attribution (MTA). While MTA focuses on detailed, channel-specific decisions, MMM provides a broader perspective crucial for strategic planning. Research shows that over 40% of leading consumer brands combine MMM with other methods, such as MTA and conversion-lift studies, showcasing the power of integrated analytics.

Once you understand these analytical benefits, the next step is gathering and preparing the right data.

Data Collection and Preparation

Required Data Types

To build a reliable marketing mix model, you need to gather the right data. This process revolves around three main categories:

Focus on detailed, granular data - daily or weekly data works best to capture subtle trends and performance shifts.

Data Cleanup Steps

Cleaning your data is essential to make it ready for analysis. Here's how you can refine raw data into something usable:

1. Standardize Formats Raw data often comes in inconsistent formats. For example, unify all dates into MM/DD/YYYY (for US standards), convert currencies to USD, and apply uniform naming conventions across channels.
2. Handle Missing Values Missing data can skew results. Address gaps by:
   • Removing incomplete records (if their absence won't affect outcomes significantly)
   • Using statistical techniques to estimate missing values
   • Aggregating data into larger time intervals (e.g., weekly) to minimize gaps
3. Transform Variables Use specific transformations to better represent marketing effects:
   • Adstock: Accounts for delayed impacts of marketing activities
   • Diminishing returns: Reflects saturation points where additional spend has less effect
   • Lag effects: Considers time delays between spending and results

To smooth out noise while preserving trends, aggregate daily data into weekly or monthly intervals. For instance, when analyzing TV ads, weekly data can better reflect campaign effects without daily fluctuations getting in the way.

Keep detailed records of all cleaning steps, transformations, and assumptions. This documentation ensures consistency, simplifies updates, and makes your model reproducible.

"A strong data foundation is crucial to getting accurate and actionable results from your MMM. The quality and completeness of your data directly impact the model's ability to isolate the true impact of marketing activities, control for external factors, and provide accurate insights for optimization and forecasting." - Pieter van Groenendael, Senior Solutions Engineer at Supermetrics

Finally, validate your cleaned data against the raw version to confirm accuracy. Clean, well-prepped data lays the groundwork for selecting the right statistical tools and building a reliable model.

Marketing Mix Modeling Workflow

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Methods and Tools

Start with well-prepared data, then choose statistical methods that align with the complexity of your marketing campaign.

Statistical Methods

Marketing Mix Modeling (MMM) uses techniques like regression, time series, Bayesian methods, and machine learning to analyze data and uncover insights.

• Regression Analysis: A classic MMM approach that examines the relationship between marketing spend and sales. It's effective for simple patterns but may overlook more intricate dynamics.
• Advanced Statistical Methods: For complex scenarios, these advanced techniques can be more effective:

After deciding on a method, use the right software to implement and refine your model.

Software Options

Python leads the pack for MMM, with 61% of major U.S. advertisers (those spending $500,000+ annually) preferring Python-based solutions. Its popularity stems from:

• A wide range of libraries (like Pandas, NumPy, and Scikit-learn)
• Easy integration with existing systems
• A strong, active user community

Popular MMM Tools:

When choosing tools, think about factors like integration capabilities, update speed, flexibility for new data channels, costs (including hidden ones like integration), and your team's skillset. For beginners, Python-based options like Pymc-marketing often provide a good balance of functionality and ease of use. Its extensive libraries and growing community make it a practical choice for building and maintaining models without breaking the bank.

"A strong data foundation is crucial to getting accurate and actionable results from your MMM. The quality and completeness of your data directly impact the model's ability to isolate the true impact of marketing activities, control for external factors, and provide accurate insights for optimization and forecasting." - Pieter van Groenendael, Senior Solutions Engineer at Supermetrics

Model Creation and Testing

Building the Model

To create your marketing mix model, start by setting clear goals and identifying variables that reflect both your marketing efforts (like channel spend and campaign timing) and external influences (such as seasonality and competitor activity). Begin with a straightforward model and slowly introduce more channels and conditions. This step-by-step approach lets you refine and improve your model as you go. Once the model is in place, it’s time to test how well it performs.

Testing for Accuracy

To ensure your model is reliable, use these three testing methods:

• Conversion Lift Tests: Conduct controlled experiments by adjusting spending - for example, increasing the budget for a specific channel temporarily - to measure its actual impact on ROI.
• Hold-out Forecasting: Train your model using part of your data, then compare its predictions to a reserved dataset to see how well it performs in real-world scenarios.
• Dynamic Spending Variations: Experiment with different budget allocations to check if the model's recommendations consistently match observed ROI changes.

"MMM looks to find the true incrementality of your marketing dollars but, because we're looking for causal inference, we must validate if our model is giving us the right information." - Marti Sanchez

Keep your model updated with fresh data regularly. This ensures it stays accurate and continues to support ROI-driven decisions.

Using Model Results

Reading the Results

To understand your marketing mix model, focus on key performance indicators (KPIs) and channel metrics that highlight the relationship between contributions and spending. According to McKinsey, reallocating budgets to better-performing channels can boost marketing ROI by 15–20%. These metrics can guide your decisions when running budget simulations.

Future Planning

Use the insights from your model to create scenario-based budget plans. These steps can help you apply your findings effectively:

• Evaluate Current Performance
  Review how your current channel mix is performing. For instance, a telecommunications company recently shifted its budget toward top-of-funnel activities and saw a 10% increase in lead growth without spending more.
• Forecast Outcomes
  Test different budget scenarios using your model. If your data shows social media outperforms traditional advertising, develop a plan to transition gradually while maintaining your market presence.
• Monitor Implementation
  Use AI-powered analytics to set up alerts for any underperforming areas, ensuring you can address issues quickly.

Tracking Success

As you refine your strategies, it's crucial to track performance and update your model regularly. A detailed dashboard can help you monitor key metrics across all channels:

Keeping an eye on these metrics will help you maintain the accuracy of your model and fine-tune your marketing mix.

"MMM looks to find the true incrementality of your marketing dollars but, because we're looking for causal inference, we must validate if our model is giving us the right information." - Marti Sanchez

Conclusion

Data-driven marketing mix modeling (MMM) plays a key role in refining strategies and improving ROI. Research shows that MMM can increase ROI by 20% and speed up decision-making for 59% of marketers.

To build an effective MMM, precise data management and ongoing updates are crucial. It requires reliable data, thorough validation, and regular adjustments to keep up with market changes. Andrea Atzori, a Digital Marketing Expert, emphasizes:

"Marketing mix modeling is emerging as a critical solution, offering data‑driven insights to optimize marketing strategies, allocate budgets effectively and achieve measurable results in 2025 and beyond"

Real-world examples highlight the impact of MMM. For instance, Kellogg's uses MMM to assess their efforts in advertising, promotions, and pricing. This helps them allocate resources strategically by analyzing past performance data. It also allows them to pinpoint which channels deliver the best returns before they start to decline.

To get the most out of MMM, it's important to monitor and refine your model regularly. By staying data-focused and keeping your model updated, you can make smarter investment decisions and achieve better business results. This approach underscores MMM's promise: making future investments smarter and more effective through measurable, data-backed insights.

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