What is media mix modeling (MMM) in marketing?

Media mix modeling (MMM) is a statistical method that shows how each media channel, such as TV, digital, or print, impacts your business outcomes (sales or revenue). MMM uses aggregated historical data and regression modeling to show the incremental impact of each channel. 

Media mix modeling also accounts for external factors like seasonality, market trends, and competitive activity, providing a more holistic and privacy-safe view of media performance, without ever needing user-level tracking data. 

Marketing mix modeling vs media mix modeling

Media mix modeling and marketing mix modeling sound similar, but they serve very different purposes. Use the table below to understand the difference between marketing mix modeling and media mix modeling.

Aspect	Marketing mix modeling	Media mix modeling
Focus	All marketing mix variables (4 Ps: product, price, place, promotion)	Media channels only, for example, TV, digital, print, OOH, paid social, and more.
Purpose	Understand how all marketing and business activities affect sales or growth	Optimize media spend and channel allocation
Users	CMOs, strategy teams, and finance leaders	Media buyers, performance marketers, and digital teams
Data input	Media and pricing data, promotional calendars, product changes, household penetration, and distribution coverage	Historical media spend, impressions, reach, external factors like seasonality and competition
Typical outcome	Holistic understanding of marketing drivers, pricing impact, and cross-functional trade-offs	ROI by media channel, budget reallocation guidance, scenario planning
Use cases	Strategic planning, brand vs. performance impact, pricing strategy evaluation	Budget optimization, marketing channel strategy, and marketing forecasting
Granularity	Broad and cross-functional	Channel-level

Get our marketing mix modeling playbook to read more.

How does mixed media modeling work?

Mixed media modeling employs a multi-linear Bayesian approach to find the relationship between media spend and business outcomes. 

Thanks to predictive analytics, your team can make data-driven decisions to guide your media mix and optimize your spending.

The standard approach to media mix modeling includes these four main steps:

Media mix modeling step	Process
1. Data collection	Media mix software collects and preprocesses extensive datasets, including ad spend and impressions, across various channels. The model incorporates external factors like seasonality, economic conditions, and competitor activity to control for non-marketing influences. It also gathers data on your business outcomes, such as sales, leads, or other relevant metrics you aim to optimize.
2. Modeling data	Statistical models, often using linear or Bayesian regression, estimate the contribution of each media channel to the outcome. The model accounts for lag effects, saturation, and interactions between channels to reflect real-world marketing dynamics.
3. Data analysis	The system attributes a portion of the business outcome to each media channel to determine ROI and reveal the most effective channels. Simulate different scenarios, such as changing ad spend, to predict the potential impact on sales forecasts and future outcomes based on planned marketing activities and historical trends.
4. Channel mix optimization	Based on the analysis, your team optimizes your media mix by reallocating budgets to the most effective channels, adjusting targeting, and fine-tuning your media planning strategy to maximize ROI and achieve KPIs.

Why you need MMM in marketing 

Let’s review the top benefits MMM in advertising and marketing provides:

• Identification of efficient media channels: Modeling media helps uncover how different marketing channels interact, helping refine overall marketing effectiveness.
• Improved ad spend optimization: Media mix modeling tools offer a holistic view of campaigns and market trends, helping you identify what’s working. Revealing the key drivers behind conversions allows you to optimize ad spend and allocate budgets more effectively for future campaigns.
• Enhanced audience targeting: The model allows you to leverage scenario-based marketing planning. You can test the impact of adjusting ad spend or targeting various audience segments to increase KPIs. The process also makes targeting more effective by providing more accurate user data input.
• Adaptability to market changes: The MMM data allows you to quickly adapt to changing consumer behavior and market conditions. Continuously updating with new data ensures your marketing strategy remains relevant and effective over time.
• Budget justification: Modeling media mix in advertising can help you demonstrate the impact of media investments to your finance team, providing concrete data to support marketing budget allocation decisions and showcase the ROI of various channels.

Read more: What do marketing mix models show advertisers?

Media mix modeling example

Imagine you’re investing in an email marketing campaign and want to assess its impact on customer acquisition. 

Email marketing is a part of your digital marketing media mix. But connecting email campaigns to specific customer actions can be challenging, as factors like social media promotions and seasonal sales influence customer behavior, making it difficult to isolate the direct effect of your campaign on acquisition. 

In this case, mixed media modeling would give you a more comprehensive view by collecting data on email marketing spend, customer demographics, and overall revenue from the past year, so you can understand how these elements interact. 

Suppose the media planning software indicates that email marketing and targeting specific customer segments positively impact customer acquisition. In that case, you can optimize your future campaigns by increasing investment in email marketing and focusing on the most responsive segments. 

Here’s an example of how the media mix modeling would look in an MMM tool:

As a result, you would be able to:

• Increase investment in effective channels
• Target the most responsive customer segments
• Enhance overall campaign organization

How to build a media mix model

You need to own your MMM to move past the complexities of traditional media mix models. Follow these eight steps when building a media mix model: 

1. Define clear objectives and identify marketing KPIs that align with your goals, such as sales, ROI, and market share. 
2. Gather data, around 12-18 months’ worth, including marketing spend across channels, sales figures, and external factors like economic trends. 
3. Select an appropriate statistical model, such as a Bayesian marketing mix model, and build it using the identified variables, accounting for potential lag effects.
4. Validate and test-calibrate the MMM with a subset of your data to ensure accuracy 
5. Run the model to analyze the impact of different marketing channels, generating actionable insights on channel effectiveness and interactions. 
6. Optimize your marketing plan, adjust budget allocations, and make tactical changes to maximize ROI.
7. Continuously monitor the model’s performance, updating it regularly with new data to reflect changes in market dynamics. 
8. Communicate the findings and recommended actions to stakeholders, ensuring collaboration across departments for informed decision-making.

Keen’s MMM software helps you analyze your media mix effectively using AI. Our platform handles large datasets, including media spending, consumer behavior, and market outcomes, to deliver actionable insights into your media initiatives. 

How media mix directs marketing efforts  

The insights from modeling advertising address key questions that help optimize your marketing efforts, including:

Where should my ad spend go?

By analyzing historical data and current performance trends, MMM advises on the optimal allocation of your ad budget. The model recommends adjusting spending across various channels to meet future marketing goals and enhance overall performance.

What is the impact of external factors on my company’s revenue?

Media mix modeling software evaluates causality and how external elements, such as economic conditions and competitive actions, affect your revenue. Understanding these impacts allows you to anticipate market shifts and adapt your marketing strategy accordingly.

What’s the ROI of my media channel mix?

Media mix analysis quantifies the ROI for each media channel, helping you identify which channels perform best and where to allocate resources to boost profitability.

Read more: Demonstrate your marketing ROI

How is media contributing to my ROI?

MMM assesses the contribution of different media types, such as earned (public relations and word-of-mouth), owned (your website and social media), and paid media (advertising), to your overall ROI. This comprehensive view helps you understand how these media types interact and how to enhance media mix optimization for better results. 

Enhance your media mix analysis with Keen

Keen’s AI-powered MMM platform connects marketing and finance to help optimize budgets, demonstrate ROI, and drive profitable growth—all in real time. Our platform allows marketers to link investment data, run models without data scientists, and simulate plans for achieving their goals. 

Partner with Keen to enhance your media mix models. Begin your free trial today.