Media mix modeling (MMM) is a data-driven analysis that evaluates how internal factors like marketing spend and advertising campaigns and external factors such as economic conditions and market trends impact your marketing performance.
Whether you’re planning or allocating your marketing resources, MMM provides insights into how each variable impacts results individually and collectively.
By leveraging historical data, the model reveals how channels like TV, radio, online ads, social media, and print contribute to your bottom line. These insights allow you to optimize marketing spend, maximize ROI, and forecast future results.
How does media mix modeling work?
MMM uses statistical analysis to determine how marketing efforts impact business outcomes like sales.
The model employs a multi-linear Bayesian approach to link marketing inputs—such as spending on various channels—to results that might include app downloads or revenue. Thanks to predictive analytics, your team can make data-driven decisions to guide your marketing mix and optimize your spending.
The standard approach to MMM includes these four main steps:
| Based on the analysis, your team optimizes the media mix by reallocating budgets to the most effective channels, adjusting targeting, and fine-tuning your media planning strategy to maximize ROI and achieve . | Process |
| 1. Data collection | ML-powered MMM systems collect and preprocess extensive datasets, including ad spend and impressions, across various channels. The model considers external factors like seasonality, economic conditions, and competitor activity. It also gathers data on business outcomes, such as sales, leads, or other relevant metrics the business aims to optimize. |
| 2. Modeling | The Bayesian approach helps teams quantify the relationship between marketing inputs, such as ad spend and business outcomes. The system identifies how changes in spending or activity across different channels impact results and assigns numerical values that measure how marketing inputs affect business outcomes in each channel. |
| 3. Data analysis | The system attributes a portion of the business outcome to each marketing 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. Optimization | Based on the analysis, your team optimizes the 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 provides:
- Identification of channel synergies: The model helps uncover how different marketing channels interact, revealing synergies that can refine overall campaign effectiveness.
- Improved oversight and optimization: Media mix modeling software offers 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 targeting: The model allows marketers to test different campaign scenarios, such as 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 data provided allows you to quickly adapt to shifting market conditions and consumer behaviors. Continuously updating with new data ensures your marketing strategy remains relevant and effective over time.
- Budget justification: With MMM, you can demonstrate the impact of marketing investments to your finance team, providing concrete data to support budget allocation decisions and showcase the ROI of various channels.
Media mix modeling example
Imagine you’re investing in an email marketing campaign and want to assess its impact on customer acquisition.
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, media mix modeling software would provide 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.
If the media planning software indicates that email marketing and targeting specific customer segments positively impact customer acquisition, you can optimize your future campaigns by increasing investment in email marketing and focusing on the most responsive segments. 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
Follow these eight steps when building a media mix model:
- Define clear objectives and identify KPIs that align with your goals, such as sales, ROI, and market share
- Gather data, around 12-18 months’ worth, including marketing spend across channels, sales figures, and external factors like economic trends
- Select an appropriate statistical model, such as a Bayesian marketing mix model, and build it using the identified variables, accounting for potential lag effects
- Calibrate and validate the model with a subset of your data to ensure accuracy
- Run the model to analyze the impact of different marketing channels, generating actionable insights on channel effectiveness and interactions
- Optimize your marketing strategy, adjust budget allocations, and make tactical changes to maximize ROI
- Continuously monitor the model’s performance, updating it regularly with new data to reflect changes in market dynamics
- 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 media mix analysis 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 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 each marketing channel?
Media mix analysis quantifies the ROI for each marketing channel, helping you identify which channels perform best and where to allocate resources to boost profitability.
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.
