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Understanding Marketing Mix Modeling: What Marketers Should Know


Data, am I right? No, really. Data and information are everything these days. We’re no strangers to that idea thanks to marketing analytics.

There’s not an earthly second that goes by where someone or something isn’t collecting or analyzing data and information. It drives nearly every decision that businesses make and even helps customers decide what they want to spend their hard-earned money on.

Marketing Mix Modeling (MMM) is a traditional modeling technique that’s re-entering marketers’ toolbelts. In a way, it faded into the background for a while when third-party cookies and other tracking methods were the standard, but with privacy concerns on the rise and thus an increasing difficulty in measuring marketing success, it’s making a comeback.

But how does it work and why should you be employing this measurement model for your marketing strategy?

How Marketing Mix Modeling Works

Marketing Mix Modeling is a way of measuring the effectiveness of marketing efforts and advertising campaigns and how they each impact progress toward a goal. For marketers, that goal is often to drive conversions, but MMM can measure for other variables, too (more on that later). Whatever you’re measuring for, this type of regression analysis helps marketers better plan future campaigns based on how a previous one performed.

If you’re newer to marketing or need a refresher on MMM, here’s a brief overview of how it works:

MMM is a quantitative, statistical technique used by marketers to measure the impact of various marketing activities on business outcomes — such as sales, revenue and brand awareness — using linear regression. It helps us understand how different elements of the marketing mix contribute to performance, helping guide optimization strategies and resource allocation for better results.

Dependent and Independent MMM Variables

With the marketing mix model, dependent variables and independent variables are key components that define the structure and objectives of the analysis. But what can you define for each?

Dependent Variables

Dependent variables are the outcomes or metrics you want your model to explain, predict or optimize for. They serve as the target metric for determining the effectiveness of marketing activities. Dependant variables include KPIs like:

  • Sales revenue.
  • Units sold.
  • Market share.
  • Brand equity metrics (e.g., awareness, consideration, or preference).
  • Customer acquisitions or leads.
  • Profit margins.

Independent Variables

On the other hand, an independent variable is a marketing input or driver that influences the dependent variable. This can include marketing activities and even non-marketing factors. Examples of marketing inputs include:

  • Ad spend by marketing channel.
  • Promotions.
  • Sponsorships or events.
  • Content marketing efforts.

And non-marketing variables could be things like:

  • Pricing.
  • Seasonality.
  • Economic factors.
  • Competitive activity.

If you’re like me and need examples to help explain somewhat complex-sounding concepts like the marketing mix model, don’t worry, that’s coming next.

A Made-Up Example of the MM Model In Action

Consider this: A detergent company that sells eco-friendly laundry detergent wants to measure the marketing ROI of efforts across TV ads, digital ads and in-store promotions.

So, they define their data inputs, which include a dependent variable (what they want to predict) and independent variables (influencing factors for the dependent variable):

  • Dependent variable: Weekly sales revenue.
  • Independent variables: TV advertising spend (X1), digital ad spend (X2), in-store promotion costs (X3) and seasonal index (X4), which is a dummy variable for holidays.

Now, I’m no mathematician, so I’ve asked ChatGPT for help developing a regression equation for this scenario. But first, let’s assume the following (in thousands):

  • X0 = 50: This represents baseline weekly sales as if each independent variable were zero.
  • X1 = 1.5 (for every $1,000 spent on TV ads, sales increase by $1,500).
  • X2 = 2.0 (for every $1,000 spent on digital ads, sales increase by $2,000).
  • X3 = 1.2 (for every $1,000 spent on in-store promotions, sales increase by $1,200).
  • X4 = 5.0 (sales increase by $5,000 during holiday weeks).

The equation becomes:

  • Sales = 50 + 1.5 (10) + 2.0 (20) + 1.2 (5) + 5 (1)
  • Sales = 50 + 15 + 40 + 6 + 5 = 116

Sales for this week are predicted to be $116,000, based on the chosen marketing activities and seasonality effects.

Marketing Mix Modeling vs. Media Mix Modeling

Marketing and media are often used interchangeably when referring to this statistical model, but here are some slight but important distinctions to make.

Marketing Mix Modeling is broad, covering all marketing actions and external factors across different marketing channels. It helps businesses determine marketing ROI and appropriate budget allocations across all marketing levers.

The media mix model, on the other hand, is a bit narrower in scope, focusing only on media-related activities. This includes TV, radio, digital ads, social media, print and others.

If you ever have trouble discerning the difference, just know that it’s in the name: marketing mix (all marketing); media mix (media only).

Strategies and Techniques for Marketing Mix Modeling

Like any form of data analysis, better results depend on the quality of the data you start with, which leads me to a few pieces of advice:

  1. Collect Granular Data: Before you start creating your model, gather detailed data on marketing activities (e.g., ad spend, promotions, clicks) and external factors (e.g., seasonality, economic indicators). The better the data, the more accurate the results.
  2. Lag Variables: Marketing actions often have a delayed impact. Account for this in your model to improve clarity (e.g., TV ads might influence sales over weeks).
  3. Diminishing Returns Modeling: In marketing, it’s all about spending money to make money, so, include variables that reflect diminishing returns from increased marketing spend.
  4. Attribution Modeling: Use MMM insights alongside multi-touch attribution models for a more holistic understanding of the relationships between variables.
  5. Recalibration: Update the model periodically with new data for continuous improvement.

Concepts Within Marketing Mix Modeling

A ‘newer’ concept to the MMM is something called Bayesian Marketing Mix Modeling and it differs from the traditional in a few ways. It’s a more advanced approach that incorporates Bayesian statistical principles. Simply put, it allows marketers to use prior knowledge or beliefs about the effects of marketing activities, combined with observed data, to estimate the impact of various marketing inputs on business outcomes.

This method provides a probabilistic framework to achieve a more nuanced view of consumer behavior by producing distributions of potential outcomes, which aids in understanding the uncertainty of the results.

Now that open-source initiatives and tools are more widely available, the Bayesian approach is gaining popularity. PyMC-Marketing is one such example. Without tools like these, Bayesian Marketing Mix Modeling might be too complex for organizations to handle on their own, as it demands in-depth knowledge of Bayesian statistics and places quite heavy computational requirements on businesses. Someone has to feed those algorithms and models ample high-quality data!

Mix Up Something Terrific

Marketing Mix Modeling is a powerful tool for marketers aiming to measure and improve the effectiveness of a marketing campaign and future campaigns. Providing data-driven insights not only enhances accountability but also ensures that marketing resources are allocated in the most impactful way.

Note: This article was originally published on contentmarketing.ai.