What is marketing mix modeling, and can it help your business?

How a company markets itself is arguably one of — if not the most — important factors in its success (or failure). Despite its importance, marketing is typically allotted a razor-thin slice of the overall budget, leaving little room for experimentation. As a marketer, you need to double-down on the activities that boost revenue and cut the ones that don’t.

The best way to do that is to use historical data, but that’s not always easy to analyze or decipher. Luckily, there is a way to leverage numbers to back up successes, formulate future strategies, and even explain failures with marketing mix modeling.

What is marketing mix modeling?

Marketing mix modeling (MMM) is a method of measuring the impact of different marketing activities on your company’s bottom line using various methods of statistical analysis. You’ll also see it called media mix modeling, but we’ll mostly refer to these models as MMMs for simplicity.

Some key terminology is helpful before we dive deeper:

• Marketing strategy: The overarching approach a marketing team plans to take in order to accomplish their goals. If marketing is a puzzle, the strategy is the big picture—think the one on the outside of the box.
• Marketing tactics: Marketing activities, such as platform-specific campaigns, that are leveraged for a strategy. These are the individual pieces of the puzzle.
• Marketing mix: The range of platform, channel, or outlet types used for a brand’s marketing efforts.
• Marketing variables: The tools marketers use to promote a brand, which we’ll cover more below.
• Marketing spend: The amount of money a marketing team spends on activities. A marketing budget may include allocations for resources and tools, but spend refers specifically to the money that goes toward implementing all of their tactics. Keep in mind that spend is the lever we can pull to find the right marketing mix to achieve that big picture we define in our strategy.

It’s normal for marketers to promote brands across varying channels to capture the attention of their target audiences wherever is most appropriate. While this increases the likelihood that potential customers will see and interact with the brand, it makes analyzing those efforts messy.

All the tools marketers use break down into the the four Ps of marketing:

1. Product: Branding, packaging, and services.
2. Price: Discounts, offer price, and credit policy.
3. Place: Market, distribution, and channels.
4. Promotion: Sales promotion, publicity, and advertisement.

There are a lot of elements, and it can be challenging to understand what they’re doing for your business. A marketing mix model can help marketers and businesses understand this landscape better by clarifying how much revenue is driven by marketing and which channels or campaigns in a marketing budget are supporting your bottom line most efficiently over time.

Having all of this data gives the marketer a clear picture of the effects of their marketing tactics, including the sales and ROI resulting from each of their marketing efforts.

Marketing mix modeling vs attribution modeling

So, what’s the difference, right? To keep it short, MMMs use statistics to understand the downstream impact of marketing spend on overall revenue (or other desired KPIs like app downloads, customer acquisitions, etc). Attribution modeling, on the other hand, traditionally looks at each order a business receives, and tries to determine “where” that order came from.

There are several shortcomings to attribution modeling, like how most attribution models are too naive to be useful or how platforms that self-report attribution are usually too generous with themselves, but that deserves its own article. One major difference between these two methods is that some marketing mix models try to quantify the effects of immeasurable marketing drivers, like TV, radio, and newspapers.

Having one of these channels in your marketing mix is a challenge, but not necessarily one you can or should avoid. Collecting information about a digital marketing campaign is easy because you can count interactions even if the customer data you collect is limited. But for marketing managers that have target audiences who consume more TV and radio content, it makes sense to leverage an MMM to better understand the efficacy of these traditionally non-digital channels.

We’re not saying all MMMs are perfect, either. There are different types of MMMs out there, but at Prescient we’ve learned to adapt MMM methodologies to better explain the effectiveness of a unique marketing ecosystem because we believe your marketing should match your brand and your audience.

How marketing mix modeling works

Ultimately, media mix modeling uses statistical analysis to make sense of marketing data. For marketers, it’s often more important to understand what most marketing mix models can and can’t do for a business than it is to understand the math behind how they work, but we’ll cover them at a high level here.

To go a little deeper, there are independent and dependent variables within your marketing data. Independent variables are marketing inputs that stand on their own; they’re what you control—like spend, copy, or creative. Think of your media mix. The array of marketing channels utilized in a campaign is independent. But the channels you choose will affect things like sales, market share, and CAC, so these are dependent.

We use MMMs to try to understand the relationship between these two types of variables. After all, if we have a clearer idea of how certain channels in our marketing mix affect CAC, it’s easier to increase the value of future campaigns with some strategic planning.

There are two types of relationships that MMMs can identify:

• A linear relationship: This means the dependent variable increases with the independent variable and decreases when it decreases. The visibility of your digital ads likely increases when you increase spend and decreases when you cut it, for example.
• A non-linear relationship: These two variables don’t move directly together. One example of this is spend and branded keywords. Spending more on a branded keyword search doesn’t necessarily mean more people are going to search, but it casts a wider net when your brand visibility work is driving searches that a keyword search campaign can capture.

There are some well-known linear relationships in marketing. For example, sales promotions like coupon codes and heavy discounts will drive more incremental sales.

Marketing mix modeling example

Let’s say a DTC company is selling widgets and they’re using a wide marketing mix to promote their products and improve their brand perception that includes Google ads, Facebook ads, TikTok ads, print ads, and podcast ads.

This company may choose to analyze their marketing activity using a marketing mix model on their historical data to contextualize their marketing performance (what’s good for what they’re spending? what’s bad?) and find ways to achieve a better business outcome, like selling more widgets.

If there’s enough historical data to work with, this model will reveal what the company can expect to happen if they change their marketing activity. The marketing team can then use this information for strategic planning and budget optimization. They may decide, for example, to double their TikTok spend because they have reasonable confidence that doing so will increase their base sales.

Even if there’s no room to increase marketing budgets, these models can help reveal which campaigns to turn off in order to increase the most effective ones.

Getting the most value from an MMM

You can easily see where this gets complicated. Increasing media spend isn’t necessarily an effective strategy if sales go up but your average order value (AOV) and overall return on ad spend (ROAS) go down. Using an MMM to support key business outcomes often comes down to evaluating campaign effectiveness, not pure sales numbers. Data scientists who build and understand these models can help tease out those nuanced insights, as can dashboards like Prescient that are powered by MMMs but tailored to helping marketing teams put these insights into a business context.

Pros and cons of marketing mix modeling

Marketing mix models aren’t perfect, but they do have several benefits, including:

1. Allocate budget wisely: Since the marketing mix model reveals the most impactful parts of a campaign, companies can plan and budget their marketing activities on these powerful channels. That means you don’t waste any money on the least profitable ones.
2. Avoid saturation: There’s a saturation point to specific channels, beyond which there’s no return on spending. This model can better identify this saturation point and limit spending within the optimal range for the highest ROI.
3. Increased ROI: This is the main reason why people use the marketing mix model. Everyone wants to scale profits and ROI. An MMM can deliver what is essentially a roadmap to ideal planning and budget allocation of your marketing strategy.

But there are also limitations.

1. Require interpretation: Although marketing mix models give you information about which channels have the best return on spending, they don’t explain why. It’s up to you to figure out the why if needed, and it’s possible to make incorrect assumptions about the data, potentially leading to a less-than-ideal marketing mix. Understanding your buyers’ behavior is essential, and these models won’t fully help you do that.
2. Take significant time: Marketing mix modeling is also time-intensive. The data needs to be extremely clean for the model to work accurately, requiring manual work upfront. Small to mid-sized organizations may also have difficulty getting marketing mix models implemented because of the cost and time investment. That’s where leveraging a plug-and-play MMM solution, like Prescient AI, can help smaller teams quantify their wins like big teams.

The marketing mix model can be powerful when used correctly. Just like every other analytical model, it has its limitations. It is, however, the most powerful tool out there for identifying profitable marketing channels so you can double down on what’s working for your company—and stop spinning your wheels with a marketing mix that doesn’t support your bottom line.

Using Prescient for marketing mix modeling

Between salaries for data scientists and the time investment into developing an MMM, it doesn’t make sense for most companies to create these internally. That’s where Prescient comes in.

Marketing mix modeling is our specialty, and we hired a team with deep expertise in this area so that DTC ecommerce companies could use our dashboard instead of incurring the cost of creating their own. Our models are also trained on client data in as little as 48 hours. That means you’re looking at diagnostics within days that can help you move toward a target business outcome or uncover what drove incremental sales so you can replicate the success.

We also include an analysis of halo effects, which can help you understand your brand equity. Halo effects captures the full effect of your awareness campaigns, not just the sales that happen directly from them. That allows marketers to better quantify the value of brand equity initiatives by showing more fully how they impact sales.

We’re a fit for omnichannel brands who are looking to confidently scale their marketing spend. Let’s get started.

Marketing mix modeling FAQs

What is marketing mix modeling?

Marketing mix modeling or media mix modeling, shortened to MMM, is a type of statistical analysis used in marketing to evaluate the impact of advertising campaigns and marketing channels on a company’s sales. An MMM can also forecast the future impact of marketing tactics and help determine the optimal allocation of marketing budget across different channels and campaigns.

What is a marketing mix?

A marketing mix is the entire range of marketing activities an organization uses in its marketing strategy. These marketing tactics can include social media campaigns, in-store marketing, direct mail marketing campaigns, magazine advertising, television advertising, paid search, and even sponsored content. The marketing mix of any company will be affected by its marketing budgets, target audience, national or regional level status, among other variables. Ideally, a marketing mix has only the marketing strategies that show the greatest marketing ROI and support key business outcomes.

Is marketing mix a model?

While there is a statistical analysis technique called marketing mix modeling, a marketing mix is the entire range of marketing activities an organization uses in its marketing strategy. Marketing mix modeling can help marketers quantify the effect of different tactics in their marketing mix on the company’s sales.

What is a media mix modeling example?

Let’s say an automotive company wants to understand how their promotional activities affect sales revenue. They could use an in-house data team or a platform like Prescient using market mix modeling to parse aggregated data with statistical analysis from all of their marketing campaigns to uncover the advertising effectiveness of their marketing and promotional activities.

That can help them answer questions like: Did our marketing spend on paid search result in more car sales? Done right, marketing mix modeling provides recommendations a business can use for media mix optimization (where will they spend) and marketing investment decisions (how much will they spend).