What is MMM Marketing and How It Works

You may be surprised to learn that tracking customers and developing a marketing strategy used to be much easier. There were only several channels to choose from.

With the transition to online, modern consumers glide across dozens of media, from various messengers to review websites. Sometimes even the most sophisticated software cannot track their moves. Especially given the recent data privacy regulations that have significantly limited user data collection.

That’s why marketing mix modeling seems a way out. It enables marketers to analyze large data volumes to detect which channel brings the maximum value. This marketing tactic relies on statistical analysis to show how various factors impact conversions.

Would you like to learn more about the marketing mix models? Read how it works and when to use these tactics.

What is Media Mix Modeling?

Marketing mix modeling (also MMM or media mix modeling) is an analytical approach that measures the impact of many dependent and independent variables on the outcome. Simply put, it’s an automated analysis of large data volumes that detects how various marketing and advertising campaigns contribute to conversions.

These models are particularly effective for companies that combine multiple marketing channels. It gives marketing managers a top-down view of promotion activities, helping to focus on the most effective ones in the future. They get accurate data on how the investment may impact sales and other conversions.


Imagine you run an online store that sells clothes. You show ads, publish content, and promote your website across many media, but aren’t sure which of them makes the most significant contribution to total sales. Thus, you decide to use marketing mix modeling for comprehensive analysis and strategic planning.

You have historical data on sales and ad spend for each channel collected for several years. The aggregated data allows you to run a multivariate test on multiple points in time. The results will show what overall sales to expect after changing the media spend. It gives you reliable information to evaluate your current efforts and plan how to achieve incremental sales in the future.

How Does It Work?

The analysis for marketing mix models uses multi-linear regression analysis to detect a relationship between a dependent variable (e.g., base sales) and independent variables (e.g., marketing spend across channels). For example, you can find out how launching an advanced email strategy affects overall sales.

The creation of a mix model happens in four stages:

Stage 1. Data collection

Media mix modeling requires aggregated historical data on marketing activities, non-marketing sources, and external factors. Therefore, to build a MMM, you must gather the necessary information first. Besides, you need to aggregate data from all sources beforehand.

Stage 2. Modeling

Most MMM software functions similarly, using multi-linear regression analysis. You will need to set a dependent variable or business outcome you want to check. In most cases, the dependent variable is sales. Note that MMM is the most effective for analyzing online channels.

Stage 3. Analysis

Once you have designed a model, you can run an analysis to get actionable insights. Evaluate the impact of each channel on the business outcome you try to explain. This way, you can rank campaigns based on their efficiency. The more sales the activity generates, the more effective it is.

Stage 4. Optimization

The final phase of media mix modeling is using the results. If the data shows that your current media mix can be improved, modify your mix. Simulation exercises or a What-if Analysis can help to predict the outcome. These approaches allow you to select the perfect tactics to achieve the revenue you expect.

Note that you can combine media mix modeling with other analytics approaches. While the MMM tactics allow you to evaluate overall marketing effectiveness, customer profiling tools are effective for making each customer experience personal.

Why is It Important?

Market mix modeling lets you get the most out of the historical data accumulated for years. While many modern tools focus on tracking customer behavior in real-time (learn more about it here), media mix modeling allows you to estimate your marketing efforts on a large scale. It’s another measurement strategy perfectly suitable for occasional comprehensive analysis. Check some other reasons to run media mix modeling below:

Data-driven decision-making

The MMM approach relies on processing a large scope of data. If the data you feed in is accurate, the results are highly reliable. You can use this information to evaluate your current advertising effectiveness and make data-based decisions regarding the future. Thanks to this, your campaigns are more likely to increase sales and meet your business goals.

Perfect for post-cookie marketing

Brands have been using cookies to track website visitors for years. But with Google’s efforts to gradually phase out third-party cookies in Chrome browsers, you may no longer be able to use them. Under these conditions, companies need alternative ways to evaluate marketing effectiveness. Since media mix modeling doesn’t require collecting cookies, you may use it as a substitute. 
Ideally, you should combine media mix models with more customer-focused software like Verfacto. First, media mix modeling will let you see the marketing overview to detect the most sales-generating channels. Then, with Verfacto, you can run personalized advertising campaigns across the best-performing media.

ROI analysis

Media mix modeling gives you a quick summary of your media spend. You can detect which marketing investment can generate the best return to optimize your budget accordingly. It’s a very convenient way to double-check your investment strategy before implementing it.

Forecasting and planning

You no longer need a crystal ball to tell the future with media mix modeling. You can predict your spending based on the company’s data. While you won’t get an exact figure, the output is enough to estimate what budget allocation you need roughly. It helps you plan expenses and allocate the necessary resources to fund marketing activities in the months ahead.

More relevant pricing

Would you like to know whether lower prices result in incremental sales? Run media mix modeling before changing the tag. It may turn out that updated pricing will result in nothing but lower revenue.

Data processing can help you predict how your market share will respond to the new pricing structure. You will be able to choose the best rates, avoiding the trial-and-error method.

Keeping user data private

You don’t need personal information to do media mix modeling. Aggregated data on marketing channels, expenses, and performance allows you to run the analysis. Thus, market mix modeling enables you to stay regulatory compliant. Moreover, it can be the primary way to analyze your marketing if you cannot gather personal customer data for any reason.

What is the MMM Ratio?

MMM ratio is an indicator used to evaluate multi-channel campaigns. Regardless of the industry you work in, an MMM ratio is based on three essential components:

  • Marketing channels: The selection of media you choose to spread the word about your product or service.

  • Money spent on each media: Your investment in promoting your product or service through each channel you use.

  • Campaign results: The sales volume you achieved compared to the base sales you had before launching the marketing campaign.


Media mix modeling is definitely worth becoming one of your core analytical approaches, but it’s not all-powerful. While market mix modeling is highly effective for analyzing and planning marketing in general, it won’t provide detailed data. So, you must consider this and other limitations before building an MMM model.

Results highly depend on data quality

The data you utilize for analysis plays a decisive role and may fail to match reality. Therefore, you must standardize and validate historical data before using it for market mix modeling. It may take a lot of time if you have gathered data from multiple disparate sources, and the information is unstructured.

Cannot measure the long-term impact of marketing

The MMM method analyzes marketing budgets based on efficiency and short-term effects. You cannot measure long-term progress with it. It may be an issue since long-term marketing effects are about strengthening brand equity and brand perception. Brand equity is your brand’s worth, which is crucial for business growth.

Regional or national-level analysis may be less accurate

Large-scale MMM research combines data from multiple companies that may focus on different demographic groups. It distorts the result and doesn’t allow you to get a realistic picture of broad trends.

Correlation vs. causation fallacy

Media mix modeling shows correlation, not causation. Hence, you cannot always set a cause-and-effect relationship between several variables. It complicates the marketing analysis and may lead to the wrong understanding of an MMM model output.

No relationships between channels

While you can measure the effectiveness of advertising or marketing channels, you cannot detect how they impact each other. For example, magazine advertising may not generate conversions directly, but it benefits your brand equity and drives more people to other media. The lack of such details may prevent you from seeing the real picture behind the numbers of multi-channel marketing.

Real-time changes aren’t taken into account

Market mix modeling utilizes previously collected data and cannot provide insight into what’s happening. Hence, if something unexpected occurs, this approach is ineffective. For example, with the outbreak of COVID-19, consumers’ purchase and behavioral patterns have rapidly changed. It has made real-time monitoring with frequently updated sales forecasts more relevant than MMM.

No focus on customer experience

With media mix modeling, you cannot analyze marketing at the consumer level. You get overall sales and marketing data, but not information on how your customers feel while navigating the channels. The aggregate information is valuable to get a bird’s-eye view of your marketing campaigns, yet you cannot use it for personalized marketing, which is highly effective.

Infrequent reports

Media mix modeling relies on historical data that doesn’t change often. Thus, you won’t be able to run an MMM analysis every month or so. Twice a year is the optimum frequency for using this approach. Continuous analytics requires the use of other techniques.

Not suitable for new products

In launching a new product, you know little about your customers’ response. Since there is no data on how people respond to related marketing, you have no marketing inputs for MMM models. You will get enough inputs only after the product remains on the market for a while and generates historical data on sales trends.

Data-Driven Attribution vs Marketing Mix Modeling

Marketing mix modeling and data-driven attribution are often compared as two popular marketing analytics approaches. Yet, each provides a unique insight for campaign optimization. Let’s clarify the difference.

DDA is a multi-touch attribution model that tracks large volumes of consumer data with machine learning. This model shows engagement across multiple touchpoints to help marketers evaluate conversion at a more granular level. DDA scores each unique touchpoint across digital channels based on its impact on driving consumers through the sales funnel.

This information lets marketers see which touch points across media bring the most engagement. It allows them to understand the relationship between channels and their contribution to ROI for better media allocation.

MMM shines when you need to understand external factors that influence your current campaign or build an initial marketing strategy. It’s suitable for sales forecasts and high-level measurements. On the other hand, data-driven attribution gives a closer look at each channel. It detects which touch points across channels generate the best results in terms of customer engagement. Besides, the DDA approach relies on the most recent data on customers’ interactions (e.g., impressions, clicks, etc.), giving you more agility in your choices.

Another significant difference is that data-driven attribution is more difficult to carry out manually. It leverages complex algorithms to assign an accurate value to each marketing touchpoint. Thus, you will need an advanced digital analytics platform to run it.

5 Tips on How to Choose a Good Tool

The described models are just statistical approaches you will probably use as a part of a multi-functional marketing analytics software. Even though you can build such models manually, it would be very time-consuming. Besides, with manual data modeling, the risk of human mistakes is too high to use it.

Therefore, when choosing a media mix modeling tool, you must pay attention to additional features and follow some other tips.

1. Choose solutions that combine several approaches

Choosing software for market mix modeling, prefer tools that provide the maximum benefits from collected data. It will give you several alternative views on your marketing tactics and may help predict the results.

2. Collect data on customers in addition to large-scale analytics

The lack of insight into real-time customer behavior is the main drawback of MMM approaches. Thus, you must use a tool that supplements media mix modeling results with more detailed analytics. Consider services that offer customer profiling, user behavior tracking, advanced customer segmentation, and trigger-based marketing apart from channel-level analytics.

3. Gather both historical and real-time data

Historical data allows you to see how marketing efficiency changes over different periods and make data-driven predictions. But you also need to track what’s happening at the moment. So a perfect tool collects different data types.

4. Protect user privacy

Mind GDPR and other regulations limiting user data collection, and choose MMM software that ensures privacy. Always check how the selected tool collects and handles user information to ensure compliance.

5. Mind integrations

To get the most out of analytical tools, you must connect them with marketing and CRM solutions. It will allow you to build data-driven campaigns with a high automation level. You won’t have to switch between integrated tools since they will automatically exchange the necessary data and trigger personalized marketing interactions.

The 3 Best Tools for Ecommerce

While some tools offer media mix modeling as the main approach, others leverage it as one of the analytical methods. The choice depends on what you need, an all-in-one platform or a purely MMM tool. Here are several options to consider for ecommerce:


Maximus is a platform for marketing mix modeling and advanced analytics. It’s a web app that analyzes historical activities to calculate their contribution to ROI/sales and predict future outcomes. It also supports A/B testing to compare two versions of a campaign.

Mass Analytics

This MMM software suite has tools that allow users to run MMM analysis independently. You can use it for automating data preparation and processing. It also generates models to identify sales drivers, run simulations, and optimize budgets.


Verfacto is a platform with a wider range of features than the listed alternatives. Thus, you can achieve better sales and ecommerce results with it. Verfacto generates statistics on your marketing achievements across media, but it’s highly customer-focused. You will learn the customer lifetime value of every lead and get a real-time profile with a full history. It also ensures top-notch user privacy, as no cookies are used.


Marketing mix modeling is far from dead and can considerably benefit your marketers. It’s a quick and reliable way to evaluate your previous campaigns for optimizing your current sales, pricing models, or marketing budget.
Fortunately, if you don’t know how to create a model or have no time, you can use analytics software. Tools like Verfacto combine multiple data modeling approaches to provide both top-down and detailed analytical insights.

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