In 2025, TikTok was suddenly shut down in the US. Companies that relied heavily on the platform for sales were in chaos and scrambled to find alternatives. With marketers spending over $18 billion in ads on Tiktok, the shut down wasn’t just a minor disruption—it was a wake-up call for everyone.
If your business depended on the social media platform, what would you have done? Where would your next marketing dollar go? Not exactly an easy question to answer.
This is where modern marketing mix modeling (MMM) comes in. Traditional MMM can’t keep up with the pace of digital changes. By the time you get your first report from consultants, trends have changed, or worse, your best-performing platform has disappeared.
In this guide, you’ll learn exactly how MMM marketing works and how you can implement it to improve your marketing performance.
Key highlights:
- Marketing mix modeling is a mathematical method that uses statistical analysis to accurately quantify the incremental contribution of different marketing efforts and channels to sales.
- You can choose from different MMM approaches—linear regression, time series, Bayesian, machine learning-based, and causal inference-based.
- The best MMM platforms, like Keen, are able to combine different approaches to give you the most refined and trustworthy results.
- Using a trusted MMM platform is more cost-effective and data-rich than building your own, which requires significant marketing investment and expertise.
What is marketing mix modeling (MMM)?
Marketing mix modeling (MMM) is a statistical technique for understanding how and which marketing channels drive sales. It quantifies the impact of different channels—paid media, organic efforts, promotions, traditional media, and external factors—to optimize marketing spend.
Read more: What do marketing mix models show advertisers?
Benefits of marketing mix modeling
Marketing mix modeling solves most problems modern marketers face — be it the disappearance of cookies or evaluating their presence on multiple channels. MMM’s key benefits are:
1. Non-reliance on user-level tracking
Traditional attribution models, such as multi-touch attribution (MTA), are losing significance in a cookieless world. Marketers need an effective way to measure marketing effectiveness—without relying on third-party data.
MMM advertising is the solution — it differs from MTA because it uses aggregated data rather than individual user tracking. What’s more, you can implement it cross-channel, meaning it works for digital, TV, out-of-home (OOH), and even offline promotions. is the best
2. Accounting for marketing incrementality
MMM separates correlation from causality in marketing. Just because ad spend increased during a high-sales period doesn’t mean it caused the lift. This isolation shows the true incrementality of each channel, helping you focus on what truly drives growth.
3. Long-term vs. short-term marketing campaign impact analysis
Some channel initiatives (for example, brand awareness campaigns) drive long-term growth, while others (for example, paid search) deliver immediate conversions. MMM helps you balance short-term revenue with long-term brand investment.
Key marketing mix modeling components: How does MMM work?
MMM models have three important components:
- Input data: The data you feed the model
- Statistical modeling: The computation of the input data
- Output data: The information you receive from the model
Here’s a breakdown of the three:
1. Data collected to build your MMM platform
MMM is fundamentally helping you lead data-driven marketing. So, the more accurate and comprehensive your data, the better the model. But not all data is created equal—quality, granularity, and consistency matter:
- Data quality: Pull data from multiple sources to avoid oversimplifying reality. The types of data you should collect are discussed later in this blog.
- Data granularity: More granular data allows your MMM model to detect nuanced patterns, like which channels are driving incremental growth versus those that just appear effective due to correlation.
- Data consistency: MMM relies on historical data patterns to forecast future performance. If your data is messy, missing, or inconsistent, your model’s accuracy will suffer. Standardize data formats across platforms, clean and verify data before it enters the model, and fill gaps where data might be missing.
2. Statistical models to compute data
Statistical models, part of MMM, analyze your data to find relationships between marketing spend and sales. The most common approach is regression analysis, which quantifies the effect of each marketing channel while controlling for external variables (more on approaches later).
In the past, the traditional MMM method was to manually compute and analyze the data. However, now that you have access to marketing mix modeling platforms, you can own your MMM, getting you to the next step in minutes.
3. Insights to optimize your marketing mix
After your model completes the statistical analysis, you get an overview of your channel performance. The output will help you understand:
- Channel effectiveness: Which marketing channels and activities contribute the most to sales?
- Calculation of ROI: How much return does each channel generate per dollar spent?
- Marketing channel mix: Where should you increase or decrease spending? Not all channels will give you the same returns. Once you understand how effective a channel is and its ROI, adjust your marketing mix accordingly.
Understanding different marketing mix modeling approaches
MMM isn’t one-size-fits-all. Some MMM techniques are simple and easy to explain, while others use AI for deeper insights. Below are the most common MMM methods and when to use them.
- Traditional linear regression MMM: This is the most basic MMM approach. It uses multiple regression analysis to estimate the impact of each marketing channel on sales. It assumes a linear relationship—meaning: if you double your ad spend, it predicts a proportional increase in revenue.
- Time-series MMM: This model builds on linear regression but accounts for delayed marketing effects. It uses adstock modeling to measure how long an ad’s impact lasts. For example, time series MMM adjusts for the lag between a TV ad and its effect built over time.
- Bayesian MMM: Instead of giving a single, fixed result, Bayesian MMM generates a range of possible outcomes based on past data. The model continuously updates its predictions as new data comes in.
- Machine learning-based MMM: It uses AI techniques like neural networks and gradient boosting to find complex patterns and has the ability to detect nonlinear relationships and cross-channel interactions. Plus, the model continuously improves with incoming data, so your insights always stay updated.
- Causal inference-based MMM: The model uses control groups, counterfactual analysis, and experiments to isolate the real impact of marketing spend. It removes external noise, ensuring the results show what actually drives sales.
Read more: Why Bayesian marketing mix is superior to traditional approaches?
Which MMM approach should you use?
The best MMM approach depends on your goals, data availability, and marketing complexity. For example:
- If you need a simple, explainable model, start with traditional regression or time-series MMM.
- If you have complex marketing interactions, Bayesian or machine learning-based MMM will improve accuracy.
- If you need to prove true incrementality, causal analytics, and ROI, causal inference-based MMM is the best choice.
| Marketing mix modeling approach | Best for | Limitations | Real-world example |
| Traditional linear regression MMM | Companies with structured historical data but no complex marketing interactions. | – Assumes a linear relationship between spend and sales, which may not always hold. – Does not account for diminishing returns (at some point, spending more won’t increase revenue). – Struggles with delayed impact from brand marketing efforts. | A small business using MMM for quarterly budget reviews to decide whether to invest more in TV or digital ads. |
| Time-series MMM | Businesses that experience delayed purchase behavior (for example, automotive and insurance). | – Requires more advanced modeling expertise. – Still assumes a relatively simple relationship between spend and sales. – Works best when paired with other modeling techniques. | A CPG brand measuring whether brand-building TV ads lead to increased retail sales months later. |
| Bayesian MMM | Businesses with inconsistent, noisy, or limited data (for example, fast-moving consumer goods). | – More complex and requires specialized analytics expertise. – Harder to explain to non-technical stakeholders. – Slower to run than simple regression models. | A subscription service using Bayesian MMM to estimate how much of its growth comes from ads vs. organic referrals. |
| Machine learning-based MMM | Companies with highly complex marketing strategies across multiple channels. | – Less transparent than traditional models—harder to interpret why certain decisions were made. – Requires large datasets and strong computing power. – More challenging to deploy and maintain without data science expertise. | A global ecommerce brand using ML-based MMM to optimize ad spend across Google, Facebook, TikTok, and TV in real time. |
| Causal inference-based MMM | Businesses that need definitive proof of marketing effectiveness (not just correlations). | – Requires robust data infrastructure and clean experimental designs. – Needs specialized data science expertise or an advanced MMM platform. | A telecom company using causal MMM to prove whether its influencer campaigns truly drive new subscriptions or just steal demand from other channels. |
5-step process for a smooth marketing mix modeling implementation
According to Keen’s 2024 survey report, 91% of marketers say that advanced analytics platforms contributed to their campaign effectiveness. So, if you’ve decided to invest in one and bring MMM in-house, the following five steps will cover your implementation roadblocks:
Step 1: Define the strategic purpose of MMM
Before purchasing the new marketing software, you need clear alignment on why your company needs MMM. Without this clarity, change management will be painful, and the platform risks becoming an underutilized tool.
Key questions to align on internally
- What specific problems will MMM solve? Demand planning? Channel optimization? Proving return on investment (ROI) to finance?
- Who will use MMM insights? Is this for media buyers, CMOs, finance teams, or all of the above?
- How will MMM fit into current workflows? Will it replace existing attribution models, or will it complement them?
Step 2: Secure executive buy-in and stakeholder alignment
Marketing mix modeling implementation isn’t just a marketing project—it impacts finance, analytics, and even operations. So, you need cross-functional teams on the same page.
Key stakeholders to involve
- CMO and marketing leadership: To align MMM with business goals.
- Finance team: To get buy-in on using MMM insights for financial planning.
- Media and performance marketing teams: To ensure they integrate MMM into budget allocation.
- Data and analytics teams: To manage data collection and modeling validation.
Step 3: Prepare your data infrastructure before onboarding a MMM platform
Even the best MMM platform can’t function without high-quality data. You need to centralize, clean, and structure your data sources before you integrate any platform.
Data sources to prepare
To get the insights as accurate as possible, you need:
- Marketing spend data: Ad spend across TV, digital, print, social, OOH, influencer marketing, promotions, and more.
- Sales data: Revenue, conversions, or any key business outcome you want to measure. Note that this sales data should correspond to the time period of your marketing spend.
- Control variables: External factors like seasonality, economic conditions, competitor activity, or pricing changes.
Step 4: Choose the right MMM software based on business needs
Not all MMM tools are built the same. Some offer basic regression models, while others (like Keen) use causal inference and machine learning for deeper insights.
What to look for in an MMM platform
- Automated data integration: Can it easily connect to your marketing and sales data?
- Causal modeling capabilities: Does it separate correlation from incrementality? Can it factor in the halo effect?
- Scenario-based marketing planning tools: Can you forecast the impact of budget changes before making them?
- Unified marketing measurement: Can it evaluate both offline (TV, OOH) and online (paid search, social, programmatic)?
Keep reading: 3 key factors missing from your marketing mix model
Step 5: Train teams and integrate marketing mix modeling into decision-making
Investing in an MMM platform is pointless if teams don’t use the insights. Marketers are often resistant to change—especially if they’re used to digital attribution models and reliant on their marketing intuition.
How to drive adoption across teams
- Conduct workshops: Train marketing teams on how to interpret MMM insights.
- Embed marketing mix modeling into workflows: Make it part of annual planning and budget allocation meetings.
- Create MMM champions: Assign internal advocates to help drive adoption within teams.
- Prove the value of marketing mix models: Start with small calibration tests before using MMM for full-scale budget shifts.
Read more: Why marketing mix modeling is trending
Make smarter decisions with Keen’s MMM platform
We have seen brands make millions in ROI by shifting budgets based on MMM insights. Understanding how the model works and choosing a trustworthy MMM platform is the key to getting there.
Enter Keen’s AI-powered MMM platform.
You’ll hear a lot of open-source MMM noise and how you can get results for “free.” But “free” lacks decades of research data and our proprietary Marketing Elasticity Engine (MEE). Our platform leverages not only the Bayesian approach but also advanced machine learning methods that take causality and other external factors into consideration when computing.
The result? Real-time marketing measurement and media planning insights.
Request a demo today to see the difference marketing mix modeling can make for you
