What is Media Mix Modeling (MMM)?

A History & Modern Application with Northbeam

In the ever-evolving landscape of marketing, the ability to measure the impact of various marketing activities is crucial. One of the most sophisticated and iconic approaches to measuring this impact is through Marketing Mix Modeling (MMM); which is more commonly referred to today as Media Mix Modeling. This method has proven its worth over decades, adapting to the changes in media consumption and technological advancements. This blog post dives into the history of MMM, explores its fundamental principles, and examines how modern companies like Northbeam are utilizing MMM to optimize marketing strategies in today's digital age.

The Origins of Marketing Mix Modeling

MMM has been a part of the marketing arsenal for more than 70 years. However, MMM began to gain significant traction in the 1960s when companies like Kraft Foods pioneered the use of this analytical method to launch products such as Jell-O. During this era, marketers had limited but rapidly-growing channels like television and magazines, and MMM allowed them to analyze the effectiveness of varying advertising levels across different regions and times of the year.

Fun Fact: Neil Borden, a famed professor of advertising at the Harvard Graduate School of Business Administration from the 1920’s to the 1960’s, popularized the term "marketing mix" in 1949 and laid the foundational theory underlying MMM. He, along with colleague James Culliton, came upon this term through describing marketing executives as “mixers of ingredients”—adjusting components like product, price, place, and promotion [the 4Ps] to meet customer demands. This concept evolved into MMM, which systematically quantifies the impact of these elements on sales and revenue.

How Does Media Mix Modeling Work?

At its core, MMM is an analytical approach that uses statistical methods to estimate the effectiveness of different marketing activities. By examining historical data, MMM isolates the impact of various marketing efforts on overall business performance. This model operates on two primary components:

Inputs

MMM considers a broad set of inputs, including:

• Marketing variables: Such as advertising spend, promotions, and product distributions.
• Environmental factors: Like economic conditions, seasonality, and competitive actions.
• Data quality: Ensuring the inputs are reliable and detailed enough to offer actionable insights.

Outputs

The outputs of an MMM are predictions and evaluations of key performance indicators (KPIs), typically focusing on metrics like sales volume or revenue. These outputs help marketers understand the return on investment (ROI) of different marketing strategies and guide future spending decisions.

Model Philosophy

MMM traditionally used linear regression models which assume that each variable has an independent and constant impact. However, modern MMM approaches often employ more sophisticated techniques, such as machine learning and AI, to capture the complex and dynamic interactions between variables.

Practical Applications of MMM

Media Mix Modeling can be applied to various strategic marketing activities:

1. Scorecards

These are retrospective analyses that help marketers understand what worked and what didn’t. They typically evaluate the ROI of past investments and are used to plan future marketing strategies.

2. Forecasting

MMM is used to simulate different marketing spend scenarios to predict their potential impacts on business outcomes. This helps in budget allocation and financial planning, answering questions like "What could be the impact if we increase digital spending?" This is where most marketers seeking “incrementality” would use MMM.

3. Optimization

Beyond forecasting, MMM helps in making real-time adjustments to marketing strategies, ensuring that budgets are allocated to the most effective channels. This involves understanding the law of diminishing returns and optimizing for long-term growth and profitability. This is where marketers will look at things like “cost curves”: visualizations of diminishing returns (and forecasted results) based on specific levels of spend.

MMM+ with Northbeam

Today, companies like Northbeam are revolutionizing MMM by integrating advanced technologies such as AI and machine learning. Northbeam’s MMM+ tool exemplifies modern MMM practices, offering features that are both innovative and practical:

• Real-Time Data Analysis: Unlike traditional MMM that often relied on outdated data, Northbeam’s tool analyzes weekly data for more timely insights.
• Demystifying Spend: Moving beyond the traditional understanding of where to place your dollars, the Northbeam MMM+ solution provides the ideal budget blend across all channels to maximize results for any and all KPIs you choose.  
• Customizable Outputs: Northbeam’s MMM+ allows companies to focus on specific KPIs that align with their business goals, providing tailored budget recommendations based on these targets.

The Complementary Role of MTA

In addition to MMM, Northbeam recognizes the importance of Multi-Touch Attribution (MTA) in understanding the digital customer journey. While MMM provides a macro-level view of marketing effectiveness, MTA offers granular insights into the performance of individual campaigns and channels. Together, they enable a holistic view of marketing performance, guiding strategic decisions that optimize both online and offline channels.

Conclusion

Media Mix Modeling has come a long way from its early days with Kraft Foods and magazine catalogs. Its evolution has mirrored the complexity of the marketing world, growing from simple analytical tools to sophisticated models that incorporate real-time data and advanced statistical techniques. As companies like Northbeam continue to push the boundaries of what's possible with MMM, marketers are equipped better than ever to make informed decisions that drive business success in the digital age. Whether you are assessing the impact of historical marketing efforts or planning future strategies, MMM remains an indispensable tool in the marketer's toolkit.

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