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Everything You Need to Know About Marketing Mix Modeling and How to Get Started

Marketing Mix Modeling (MMM) is a powerful measurement technique that quantifies the impact of various marketing inputs on sales or market share. Through careful analysis, including regression techniques, it pinpoints the effectiveness of each marketing channel in terms of Return on Investment (ROI). Not limited to traditional marketing, MMM is equally insightful for evaluating digital marketing efforts, providing a comprehensive view for modern marketers. 

Think of it as mapping a journey: you're trying to find the path from marketing spend (the starting point) to sales (the destination), and the model helps you understand how different routes (marketing channels) can get you there.

In this article, we will explore various elements and processes associated with understanding MMM, its advantages, tips to get started, and how MMM has helped real-life marketers and businesses.

What is Marketing Mix Modeling and How Does It Work?

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Unless you have a background in data science or mathematics, understanding the ins and outs of Marketing Mix Modeling might feel overwhelming. Luckily for us marketers, we can leave the perplexing technical elements to the experts. But, that being said, it can be helpful to have a basic understanding of how MMM works. Then, you can get the most out of the information it produces and turn it into useful insights, transforming both your offline and online marketing strategies. So, to gain some perspective, let’s take a look at the processes involved in MMM;

1. Data Collection and Modeling 

Aggregated data is the bedrock of MMM, as it fuels the statistical models that translate raw numbers into marketing wisdom. Typically, MMM requires data on media spending (the cost of various marketing efforts) and outcome data, such as sales or revenue. Other factors such as seasonality, market trends, and consumer behaviors should be considered. The “why” is clear: understanding how different elements of the marketing mix interact and influence sales allows for targeted, efficient strategy development.

How to Collect and Aggregate Data

Collecting and aggregating data for MMM is a critical step that can be a little nuanced, depending on the channels involved and the organization's analytical maturity. There are a couple of ways to source this type of data;

  • Internal Sources: If you have a marketing analytics team that maintains a marketing data warehouse, you're in a great position. If not, you may need to ask around different teams for assistance, which will take a bit longer but will be worth it in the long run.
  • Agency Sources: Obtaining data from your marketing agency is another viable option. While there are cases where an agency may resist sharing data (which is a big red flag), most agencies are cooperative. It's wise to seek automated and scalable solutions here. Whether it's an automatically updated Google Sheet, an automated data feed with your data science team, or even just an Excel file sent via email.
  • Directly from the Source: The best way is often straight from the advertising platforms themselves. All large ad platforms offer an API where you can obtain aggregated cost and impression data. This method ensures correct, daily updated data, leading to fresh models and insights. By now you may be thinking about how daunting maintaining API integrations can be, but by working with the better MMM SaaS vendors, this is delegated, allowing you to focus on applying the MMM insights to your marketing strategy instead.

When it comes to gathering offline marketing data, the process can be a little nuanced, depending on the channels. However, the goal remains the same: to ensure accurate, up-to-date information for your marketing mix modeling activities. 

  • Excel and CSV Files: Many offline marketing efforts involve data that is traditionally managed in Excel or CSV files. These can include data from TV campaigns, radio spots, print advertisements, and other non-digital channels. While gathering data from these sources might not be as automated as API integrations, it’s an essential step. 
  • Media Agencies: For TV and radio campaigns, businesses often work with media agencies to execute and manage these efforts. Often, these agencies can provide reports and detailed data on the reach, frequency, and costs associated with specific campaigns. 
  • Integrating Offline Data with MMM Vendors: If you are using a MMM vendor, such as Adtriba, for example, you can semi-automate the process of integrating offline data. Thus allowing you to input offline data alongside your online data manually. 

2. Algorithm Modeling

So, now that you have the data collected in a way that works best for your business, what’s next? Now it’s time for statistical modeling, and this is where Marketing Mix Modeling really comes to life. But, not to worry; as mentioned earlier, this is where the data experts come in too.

At its core, statistical modeling in MMM involves using techniques like time series regression analysis to identify relationships between marketing inputs and outcomes.

Time series regression, in particular, is a specialized statistical method that excels at handling data collected and recorded over time. Essentially, time series regression uses input variables (campaign spend) to predict target variables (such as revenue) while considering control variables (special events, product launches, etc.), thus allowing marketers to get even more precise with elements like budget allocation. 

Time series analysis includes accounting for trends, which represent a pattern in data that shows the movement of a series to relatively higher or lower values over a long period of time. In other words, a trend is observed when there is an increasing or decreasing slope in the time series. In addition, it considers seasonality, which refers to buying patterns due to times of the year, week, or even day.

For instance, this data might include sales data for today, yesterday, and the days before, resulting in thousands of data points. When time series regression is employed, MMM can provide a more complete and accurate picture of the impact of marketing activities on revenue during a specific period. 

The beauty of MMM's statistical modeling is its ability to make complex marketing landscapes understandable. It translates the vast and often confusing world of marketing data into clear insights and actionable strategies. MMM's approach to data collection and modeling offers a roadmap for marketing success, guiding businesses in their ongoing quest to connect with customers and grow. 

Now that we have an understanding of what MMM aims to achieve, let’s dive a little deeper into the equation where MMM gets its insights and how it works. 

The MMM Equation: Simplified 

marketing mix modeling equation

The algorithm is simplified in the above diagram, but let’s have a closer look at what each element represents;

  • Baseline Sales: Baseline sales represent the revenue that a business can expect to generate without any additional marketing efforts. These sales are often attributed to factors such as well-established brand equity. Brand equity is the result of strong brand awareness, positive brand associations, customer loyalty, and a reputation for high-quality products or services. However, baseline sales encompass more than just brand-related factors.

Seasonality plays a crucial role, as consumer buying patterns often fluctuate with the seasons. Additionally, current trends, external events, and even stock market dynamics can influence baseline sales figures. In essence, baseline sales signify the sales that would occur organically, regardless of any specific marketing strategies or initiatives. 

  • Sum of effects from marketing activities: This figure represents the cumulative impact of various marketing initiatives and campaigns. This figure takes into account the effects of both digital and offline marketing efforts. 

In the digital realm, it encompasses the impact of strategies like paid advertising, email marketing, and social media campaigns across platforms like TikTok, Meta, and YouTube.

In parallel, it also considers the influence of offline marketing activities, including traditional mediums like television advertising, direct mail campaigns, radio promotions, and out-of-home (OOH) advertising. 

  • Sum of effects from external factors: This figure encompasses the sum of the impact of external factors that extend beyond advertising efforts and include elements such as product innovations, competitors’ actions, price, placement, and broader macroeconomic conditions. They are often uncontrollable aspects that can significantly affect sales figures.
  • Total Sales: This figure includes the sum of baseline sales, the effects of marketing activities, and the impact of external factors - essentially, all of the above elements we just detailed. 

Total sales is the ultimate indicator of business performance, offering invaluable insights for strategic decision-making and resource allocation.

Hierarchical Effect 

Hierarchical effect diagram

The Marketing Mix Modeling algorithm employs a hierarchical approach to assist users in understanding the complex connections between external factors, marketing efforts, and their direct and indirect impacts on sales. 

For instance, let's consider a holiday season, such as Christmas. During this time, people often engage in gift shopping. Imagine a web user who discovers ASOS, the online fashion retailer, through a Google search and decides to purchase a pair of sneakers as a gift for a friend for Christmas.

Thus, the season (Christmas) impacted the user's thinking about buying gifts, which led to a Google Search, subsequently resulting in a conversion on ASOS.

Let’s consider another example, a person who sees an OOH ad for Ikea, such as a billboard, while on their way to work. Perhaps they also see the billboard on their way home. Then, later that evening, they think about the ad they saw and go to their phone to search for the Ikea website. This scenario highlights how upper funnel marketing techniques, such as OOH advertising, can influence a customer’s decision, resulting in a direct website visit, ultimately impacting a business's target variables.

Adstock Effect

This advanced algorithm doesn't stop at identifying the impact of marketing activities and external factors on sales; it goes a step further by comprehending how this impact evolves over time. This dynamic concept is referred to as the adstock effect, and it considers the effects of past marketing campaigns on current outcomes.

For example, if a previous advertising campaign generated interest in a product, its effects might continue to influence sales even after the campaign has ended. The adstock effect quantifies this influence, helping marketers make more informed decisions about their ongoing strategies.

In essence, the adstock effect ensures that marketing strategies are not only based on current data but also consider the lasting impact of past efforts, providing a more comprehensive view of marketing effectiveness.

3. Analysis

The analysis phase of MMM focuses on interpreting the complex relationship between marketing efforts and business outcomes such as sales or revenue. This is where marketers and advertisers will see real value, providing actionable insights that can drive decision-making. Here are just some ways MMM will assist in meeting your marketing goals:

  • Understanding the Incremental Effect: MMM allows businesses to pinpoint how individual marketing efforts contribute to incremental sales or other key performance indicators (KPIs). By isolating these effects, companies can understand which marketing inputs are delivering the best return on investment. Here is a glance at some of the KPIs that marketers can measure more effectively with MMM;
    • Cost Per Acquisition: This is a metric used in marketing to calculate the average cost incurred for acquiring a single customer through a specific marketing channel or campaign. It considers the total expenses associated with that particular marketing effort and divides it by the number of customers acquired solely through that channel.
    • Cost Per Incremental Acquisition (CPIA): This metric calculates the cost incurred to acquire each additional customer as a result of a specific marketing activity or campaign. It measures the direct cost associated with gaining new customers beyond the baseline acquisition rate. It is important to note that this metric does not include organic growth.
    • Marginal Cost Per Acquisition (MCPA): This is the cost associated with acquiring one additional unit of customer acquisition due to a specific marketing action. It represents the incremental cost of gaining a customer compared to the cost of acquiring customers without that specific action. 
    • Return On Ad Spend (ROAS): This metric evaluates the effectiveness of advertising efforts by quantifying the revenue generated relative to the costs spent on various campaigns. It offers a clear picture of the financial outcome of each advertising investment, aiding in strategic decision-making and resource allocation.
  • Visualizing the Impact: Again, the better MMM SaaS providers will have sophisticated visualization tools that can help to represent complex data in understandable ways. This makes it easier to grasp how different variables interact.
  • Identifying Trends and Anomalies: Through detailed analysis, trends, seasonal patterns, and anomalies can be detected, providing clues about underlying market dynamics.
  • Evaluating Marketing ROI: By analyzing how different marketing channels contribute to sales, businesses can understand the ROI of various marketing inputs, ensuring that marketing budgets are allocated where they will have the most impact. 
  • Customizing the Analysis: Depending on the business needs and industry context, custom analysis can be performed to focus on specific questions or challenges relevant to the organization's goals.

4. Activation

Once the analysis phase has uncovered key insights, the next step is to translate these findings into actionable strategies. This is the activation phase of MMM, and here's how it works:

  • Creating Simulations: Using the statistical models derived from the analysis, simulations can be run to predict how changes in marketing spend across different channels will impact business outcomes. This "what-if" analysis allows marketers to explore various scenarios and make informed decisions.
  • Optimizing Budget Allocation: Based on the insights from the simulations, marketing budgets can be reallocated to the most effective channels, taking into consideration factors such as saturation, seasonality, and trends. This optimization ensures that every dollar spent is targeted for maximum impact. 

In fact, some MMM platforms, such as Adtriba, offer media planning features. Businesses can set goals and adjust parameters like spending limits and desired conversions. The tool then generates recommendations, turning your vision into a quantifiable reality.

  • Monitoring and Adjusting: Activation is an ongoing process. Regular monitoring of performance against the optimized scenarios allows for continuous refinement and adjustment, ensuring that the marketing strategy remains aligned with changing market conditions and business goals.

So, now that we have some understanding of how MMM processes look, let’s look a little closer at why businesses should use it in the modern marketing landscape and how to get started.

Why Use Marketing Mix Modeling in the Modern Marketing Landscape?

  • Privacy Restrictions

In an era where privacy is a growing concern, MMM stands out for its privacy-friendly approach. Since it relies on aggregated data rather than detailed user-level tracking, it aligns with modern regulations such as GDPR. The absence of third-party cookies or any specific user tracking ensures that MMM doesn't suffer from restrictions that might hamper other analytical approaches.

  • Online or Offline

The relevance of MMM extends to businesses that operate solely in the digital domain. Digital-only marketers may wonder if MMM applies to them, especially given the unique nature of online channels. The truth is that MMM is not only applicable but often essential for digital marketers. Online platforms may sometimes behave like offline channels, with aggregated view-level data that doesn't allow user-level tracking. Without MMM to measure aggregate impact, these insights could go unnoticed.

  • Unique Optimization Insights

MMM provides unique optimization insights that other methods may lack. By learning how costs link to conversions and revenue, marketers can explore where to efficiently allocate budgets. Whether it's the next $10 or $1000, MMM guides the spending decisions, steering towards the most productive paths. This is particularly relevant in the context of incremental and marginal Cost-per-acquisition (CPA) and optimization strategies, as it shows marketers the paths that suggest the highest productivity and returns.

Getting Started with Marketing Mix Modeling

Implementing marketing mix modeling (MMM) in your business or organization is an exciting step toward unlocking a more refined and data-driven marketing approach. The process, though, may appear intricate at first glance. Here, we'll walk you through the essential elements of getting started with MMM, including some potential challenges that beginners might face.

Steps to Getting Started

  • Assessing Your Needs: Before diving into MMM, understanding the specific marketing questions and challenges your business faces is crucial. This initial assessment helps tailor the MMM approach to your unique context.
  • Identifying the Data Sources: As previously discussed, aggregated data is the foundation of MMM. Determine the sources of the necessary data, whether from internal teams, agencies, or directly from advertising platforms.
  • Choosing the Right Tools: Selecting the appropriate software or platforms for MMM is vital. Some vendors offer specialized MMM tools, while others may opt for custom solutions developed in-house.

Pro tip: For those looking to explore cutting-edge solutions in marketing mix modeling, consider checking out our MMM with AI solution, Adtriba Sphere. Quick and easy to implement, Adtriba’s MMM solution allows you to stay in line with privacy requirements, gain accurate granular insights, and optimize and plan with ease. Talk to us today to learn more.

Case Study: FREENOW

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To illustrate the power and potential of Marketing Mix Modeling, let’s take a look at a real-world application. Here’s a case study featuring one of our esteemed clients, FREENOW, who leveraged MMM to enhance their marketing strategy and achieve remarkable results. Their experience serves as a tangible testament to what can be accomplished when applying these advanced techniques and tools to modern marketing challenges. 

Gaining a Comprehensive Understanding

With Adtriba's always-on Marketing Mix Modeling (MMM) solution, FREENOW found the answers they were looking for. This sophisticated system provided a holistic and cross-platform view of the efficiency of all their marketing activities, breaking down barriers that had previously hindered their insight.

Cutting Costs and Optimizing Media Planning

The integration of Adtriba's solutions into FREENOW's marketing strategy allowed them to see a significant increase in efficiency. By having a clear view of the actual incrementality of their marketing channels, FREENOW was able to pinpoint precisely where their dollars were having the greatest impact. This detailed information guided them in optimizing their media planning, ensuring that every marketing effort was as effective and efficient as possible.

FREENOW's application of Adtriba's MMM solution transformed their marketing, resulting in smarter decisions and fine-tuned success. Their story stands as an inspiring example for businesses seeking to leverage the power of advanced MMM tools.


In this article, we've explored the privacy-friendly nature of MMM, its relevance to both online and offline channels, and its unique optimization insights. We've broken down the processes involved, from data collection to activation, and offered practical guidance on getting started with MMM, including a pro tip to check out Adtriba's AI-powered solution.

Through the real-world example of FREENOW, we've seen how MMM's advanced techniques can transform marketing strategies, leading to more efficient spending and optimized media planning.

In a constantly changing marketing landscape, MMM stands as a robust tool that can navigate complexity, pinpoint effectiveness, and ultimately drive a return on investment. Whether you're new to this method or looking to sharpen your existing approach, embracing MMM could be your next strategic move toward achieving remarkable marketing success.

Book a demo today and discover how Adtriba can aid your organization in seeing success through Marketing Mix Modeling! 


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