What is Marketing Mix Modeling?

November 30, 2023 | Chris Kervinen, Paul Arpikari, Carmen Bozga

What is Marketing Mix Modeling?

Why should you care about Marketing Mix Modeling (MMM)? To answer this, we need to look at how marketing has evolved. From traditional print and TV ads to the explosion of digital channels like social media and email marketing, the media landscape has changed dramatically. This diversity in channels enables unprecedented reach but also adds layers of complexity to marketing strategy. Understanding this evolution is key to appreciating why MMM is not just useful but essential in the modern marketing toolbox. Simultaneously, there is a growing trend towards prioritizing consumer privacy, particularly as the reliance on cookies and individual tracking is increasingly scrutinized and even phased out. Consumers are more aware and resistant to invasive tracking methods, prompting a shift towards aggregated, privacy-conscious approaches in understanding consumer behavior.

Marketing Mix Modeling and Marketing Mix

Before diving into marketing mix modeling, let's revisit the marketing mix - the foundation of any marketing strategy. It consists of:

  • Product: The goods or services you offer.
  • Place: The channels through which you sell, be it online platforms or physical stores.
  • Price: Your pricing strategy, which should reflect market demand, cost, and perceived value.
  • Promotion: The tactics used to increase awareness and sales, including advertising, sales promotions, and public relations.

Each element must be carefully considered and balanced to create a successful marketing strategy. MMM helps in optimizing this balance in a data-driven way.

What is Marketing Mix Modeling (MMM)?

Marketing Mix Modeling (MMM), sometimes also referred to as media mix modeling, is a technique used to analyze and understand the effectiveness of various marketing channels and strategies. It employs statistical analysis to discern how different marketing activities impact sales and profitability. This approach is particularly valuable in today's multi-channel marketing environment, where it's crucial to determine the effectiveness of each channel and strategy.

To effectively launch Marketing Mix Modeling (MMM), a substantial and diverse collection of data is crucial. This data provides insights into the company's historical actions and their outcomes, laying the groundwork for accurate analysis and strategic planning. Let's delve into the primary data sets essential for MMM.

Input Data:

  1. Online Media Data: This encompasses digital advertising efforts across various platforms. Metrics may include impressions, clicks, engagement rates, and spend across different campaigns and channels.
  2. Offline Media Data: This refers to traditional media channels such as TV, radio, print, and outdoor advertising. Data collected should include reach, frequency, duration of campaigns, and expenditure.
  3. Own Media Data: This involves data from owned channels like company websites, email marketing, and social media presence. Metrics can include website traffic, email open rates, and social media interactions.

External Variables:

  1. Weather: Weather conditions can significantly impact consumer behavior. Data on temperature, precipitation, and seasonal changes should be considered.
  2. Macroeconomic Data Sets: Economic indicators like unemployment rates, inflation, and consumer confidence indices are vital as they influence consumer spending and overall market conditions.
  3. COVID-19 (as of November 2023): The ongoing impacts of the COVID-19 pandemic on consumer behavior and market trends. This includes lockdowns, consumer sentiment changes, and shifts in online vs. offline shopping patterns.
  4. Other Time-Series Data Sets: Any additional data that the company believes affects sales, such as local events, holidays, or industry-specific trends.

Output Data:

  1. Sales Data: The core output for MMM is detailed sales data. It should be as granular as possible, ideally at the day-product (category)-location level. This granularity allows for a more nuanced understanding of how different factors affect sales across various dimensions.

Key aspects of MMM include:

  • Frequency: This refers to the regularity of data analysis and updates. Depending on the dynamics of the market and business needs, updates can be monthly, weekly, or even daily. This frequency is crucial for staying relevant and adapting marketing strategies in real-time.
  • Data Size: MMM emphasizes not just the volume of data but also its quality and specificity. Rich, detailed sales and marketing data are analyzed to gain deep insights into consumer behavior and market trends.
  • Modeling Dimensions: MMM goes beyond just sales figures; it also includes data on profit margins. This provides a more comprehensive view of marketing effectiveness, enabling businesses to see not just revenue uplifts but also impacts on profitability.
  • Access & Application: The insights gained from MMM are most effective when they're integrated across various departments. This cross-functional application helps build a cohesive and informed marketing strategy, leveraging insights for broader business benefits.

What is Bayesian Marketing Mix Modeling?

At Sellforte we use Bayesian marketing mix modeling (MMM), therefore, here is our simplest explanation:

Bayesian MMM is an advanced variant of MMM approach. It integrates Bayesian statistical methods, which allow for incorporating prior knowledge and assumptions into the analysis. This method is known for its flexibility and ability to handle complex, real-world data scenarios. Bayesian MMM is used by leading companies, including tech giants like Google, for its ability to provide more nuanced and contextually relevant insights into marketing performance

Imagine you own a small bakery, and you're trying to figure out the best way to advertise your new line of cupcakes. You've tried several marketing strategies in the past, like social media ads, email marketing, and local newspaper ads.

Traditional MMM Approach: Traditionally, with MMM, you would analyze past sales data and see how each marketing channel (social media, email, newspaper) correlated with your cupcake sales. This analysis helps you understand which channels were most effective.

Bayesian MMM Approach: Now, let's add the Bayesian element. Before you even start analyzing your current data, you already have some beliefs or assumptions based on your experience. For example, you might believe that social media ads are more effective for your younger customers, but newspaper ads are better for reaching an older demographic.

Bayesian MMM allows you to include these beliefs in your analysis. It's like starting your analysis with an educated guess which then gets updated and refined as you input real, current data from your marketing campaigns.

How It Works in Practice:

  1. Starting Point (Prior Beliefs): You begin with your initial belief (also known as 'prior') that social media ads are very effective for cupcake sales. Including priors allows you to get robust MMM results even with small data sets.
  2. Data Analysis: As your new marketing campaign runs, you collect data on how each channel is performing.
  3. Updating Beliefs (Bayesian Updating): The Bayesian method then combines your initial beliefs with the new data. If the data shows that social media ads are indeed driving a lot of cupcake sales, your belief is strengthened. However, if the data shows that newspaper ads are unexpectedly bringing in more sales, the Bayesian method adjusts your belief, placing more weight on the newspaper ads.
  4. Result: The end result is a more nuanced understanding of your marketing effectiveness. Instead of just looking at raw data, you're also considering your professional experience and adjusting your strategies based on a combination of both.

In essence, Bayesian MMM doesn't just tell you what worked in the past; it helps you make more informed predictions about what's likely to work in the future, considering both your past experiences and new data. This approach is especially useful in complex, ever-changing markets where relying solely on historical data might not be enough.

What is Marketing Mix Modeling SaaS?

Marketing Mix Modeling SaaS (Software as a Service) means providing Marketing Mix Modeling as a service through a software platform, which various companies can subscribe to and use. This model of delivering MMM solutions aligns with the broader trend of SaaS offerings across industries. Here's how it works:

The SaaS Model for MMM:

  • In this setup, a provider develops an MMM software solution and offers it as a service to various clients, like retailers, marketing agencies, telecommunications companies, etc.
  • Clients subscribe to the MMM software, often on a monthly or annual basis, and use the platform to conduct their marketing mix analyses.

Benefits of SaaS MMM:

  • Accessibility: Clients can access sophisticated MMM tools without the need for in-house development, making advanced analytics more accessible to a wider range of businesses.
  • Cost-Effectiveness: It removes the need for significant upfront investments in software development, making it a more cost-effective solution for companies, especially small and medium-sized businesses.
  • Scalability: The SaaS model allows companies to scale their use of MMM solutions as their business grows or their needs change.
  • Regular Updates: The service provider continually updates the software, ensuring that the latest methodologies and data processing capabilities are available to clients.

Application Across Industries:

  • Different industries can leverage SaaS MMM for their specific marketing needs. For example, a retailer might use it to analyze the effectiveness of online versus in-store promotions, while a marketing agency might use it to determine the ROI of different advertising channels for their clients.

Customization and Integration:

  • Many SaaS MMM solutions offer customization options to suit specific business needs and can integrate with other tools or data sources, such as CRM systems, sales data, or marketing platforms.

Data-Driven Insights:

  • Subscribers can input their marketing and sales data into the software, which then uses advanced analytics to provide insights on the effectiveness of different marketing strategies and channels, helping companies optimize their marketing mix for better ROI.

SaaS MMM is a way to democratize access to advanced marketing mix modeling tools. It allows companies of various sizes and from different sectors to benefit from data-driven marketing insights without needing to develop their own complex modeling systems, fitting well within the broader trend of cloud-based, subscription software services.

Who can profit the most from Marketing Mix Modeling?

Understanding who benefits the most from Marketing Mix Modeling can shed light on its practical applications and effectiveness across various sectors. Let’s explore how MMM proves advantageous for different industries:


Retailers, both in the physical and online spaces, are prime beneficiaries of MMM. By analyzing data on customer purchases, seasonal trends, and promotional effectiveness, retailers can optimize their product mix, pricing strategies, and promotional activities. For example, a large supermarket chain could use MMM to determine the most effective combination of in-store promotions and online advertising to drive sales for specific products or categories. This not only enhances customer experience but also maximizes profits and minimizes wasted marketing expenditures.

Marketing Agencies

Marketing agencies leverage MMM to deliver more effective campaigns for their clients. By understanding the impact of different marketing channels and strategies, agencies can provide more accurate recommendations, fine-tune campaigns in real-time, and demonstrate clear ROI to their clients. For instance, a digital marketing agency might use MMM to assess the effectiveness of various online campaigns across social media, search engines, and email marketing, enabling them to allocate budget more effectively and improve campaign performance.

Telecommunication Companies

Telecommunication companies operate in a highly competitive market where customer acquisition and retention are critical. MMM helps these companies understand which marketing strategies are most effective in attracting new subscribers and which loyalty programs are best at retaining existing ones. For example, a telecom company could use MMM to analyze the impact of different pricing strategies, advertising campaigns, and bundle offers on customer acquisition rates and average revenue per user (ARPU).

Other Industries

Beyond these industries, MMM finds relevance in sectors like automotive, finance, healthcare, and more. Each industry has unique marketing challenges that MMM can address, from optimizing media spends in automotive launches to determining the most effective channels for financial services marketing.

The Universal Benefit

The common thread across all these industries is the power of informed decision-making. MMM allows businesses to move away from guesswork and intuition, relying instead on data-driven insights. This leads to more efficient use of marketing budgets, improved customer targeting, and ultimately, enhanced business performance. As such, MMM is not just a tool but a transformative process that reshapes how marketing is approached across various sectors.

You can find various case studies from our clients here.

How to use Marketing Mix Modeling in your organization?

Applying MMM effectively in your business involves a series of steps and considerations to ensure that the insights it generates are actionable and lead to tangible improvements in marketing strategies and overall business performance.

1. Data Collection and Integration: The first step in applying MMM is gathering comprehensive data. This includes historical sales data, marketing expenditure across channels, customer demographic information, and any other relevant data like seasonal trends or economic indicators. It's crucial to ensure that this data is integrated effectively, providing a holistic view of your marketing activities and their outcomes.

2. Involving the Right People: Implementing MMM isn't just a task for the marketing team; it requires cross-departmental collaboration. Involving members from sales, merchandising, finance, and even product development ensures a more comprehensive application of MMM insights. This approach breaks down silos within the organization and encourages a unified strategy towards marketing and sales.

3. Advanced Analysis: With the data in place, the next step is analysis. This is where MMM software, whether in-house or a SaaS solution, comes into play. Utilizing advanced statistical methods, machine learning, and AI algorithms, the software analyzes the data to reveal insights on the effectiveness of different marketing strategies and channels.

4. Translating Insights into Strategy: The insights garnered from MMM need to be translated into actionable strategies. This could involve reallocating budgets to more effective channels, redefining target customer segments, adjusting pricing strategies, or refining promotional tactics. For instance, if MMM shows that social media advertising is more effective than expected, a company might increase its investment in this channel.

5. Real-World Application: Consider the example of Gigantti . By applying MMM, they discovered that their search engine traffic boosts were actually driven by TV advertisements, not just SEO or paid searches. This insight allowed them to reallocate their marketing budget more effectively, investing more in TV ads that were proven to drive traffic.

6. Continuous Optimization: MMM is not a one-time exercise. Regular updates and continuous analysis are necessary to adapt to changing market conditions, consumer behaviors, and the evolving media landscape. This ongoing process helps in keeping the marketing strategy relevant and effective.

7. Specific Applications: MMM can help in making specific marketing decisions like identifying star promotions that attract customers without hurting net profits, understanding how different media channels perform across customer segments, and determining the most loved products for each segment.

8. Forecasting and Future Planning: MMM also plays a crucial role in forecasting sales and profit margins based on the current media plan, allowing for future planning and budget allocation.

9. Making Everyday Decisions: Finally, the insights from MMM should be a part of everyday decision-making processes. They should inform daily marketing activities and long-term strategic planning, ensuring that every marketing dollar is spent effectively.

Conclusion and Key takeaways

In conclusion, Marketing Mix Modeling (MMM) stands as a pivotal tool in the modern marketer's arsenal. In an age where data is abundant and the marketing landscape is more complex than ever, MMM provides clarity and actionable insights. By systematically analyzing the effectiveness of different marketing strategies across various channels, MMM helps businesses optimize their marketing mix, improve ROI, and ultimately drive sustainable growth. The key to success with MMM lies in its thoughtful application – integrating data from multiple sources, involving diverse teams in the process, and consistently updating strategies based on the insights gained. When implemented effectively, MMM not only enhances marketing effectiveness but also contributes to a deeper understanding of customer behavior and market dynamics, empowering businesses to make informed, strategic decisions that resonate with their audience and align with their goals.

MMM Key Takeaways

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Marketing Mix Modeling

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