Marketing mix modeling (or MMM) is a powerful statistical analysis that uses sales and marketing data to estimate the impact of marketing activities on sales. It is employed by companies to measure marketing effectiveness and predict the impact of future efforts — most often on ad spend.
A definition of MMM
MMM is a highly resilient, data-driven statistical analysis that considers how various internal and external factors impact your marketing performance — be it sales or any other KPI.
In a modern multi-channel marketing campaign, you might employ broadcast advertising, Google Ads, paid and organic social media, public relations, outdoor transit advertising (bus stops, billboards, etc.), webinars, co-selling partnerships, promotions, and more.
Plus, those marketing channels can serve multiple purposes. For instance, your digital channels may be focused on lead generation, while upper funnel tactics maybe be aiming to build awareness and brand equity.
That’s a lot to keep track of! Plus, the more initiatives and complexity that you add to your campaign, the more robust it becomes and the more impressions it achieves.
And while lots of different channels and tactics can help you achieve multiple goals and reach a larger audience, they can also bring layered complexity. As you add different channels, you will quickly find it more difficult to determine which of those channels is contributing most to your goals. That's where advanced analysis like MMM can help.
An MMM analogy
To better illustrate this dynamic, let's imagine a professional soccer coach trying to determine which of the team's players is the real difference maker. The team features 11 starting players and a few substitutes. In any given game, one player may be involved in more critical plays (say, the star midfielder) and could possibly contribute to 25% of a team's win through goals and assists.
That player can't win by themself, though. Instead, they need the help of the entire team to defend, maintain possession, and create opportunities. So how much of a role do those other team members play?
It’s incredibly difficult to make this determination just by standing back and watching the game unfold. It’s an entire micro-ecosystem that feeds off itself. Here, attribution is very hard to define.
The same is true of your marketing and promotional activities. While performance marketing efforts across your digital channels may be easier to link to your conversion goal, your broadcast and print advertising played a role, too.
Identify KPI's
Let's jump back into the shoes of our soccer coach who (hypothetically) identifies a set of core KPIs that will be tracked for each player. This includes the number of passes, pass completion rate, assists, interceptions, goals, clearances, etc. This gives us an opportunity to employ an MMM-style approach.
Applying a marketing mix modeling approach to one single game might not give you that much valuable data. After all, it's a small sample size. In a marketing context, that would be like be trying to define performance attribution based on a single day's-worth of data.
However, if we view these KPIs across an entire season (or several months to a year in the case of a campaign), we can start to see valuable attribution insights rising to the surface. Those insights can help to shape the strategies for the next season or campaign.
What variables should I analyze for MMM?
The list of variables you can monitor with MMM is nearly limitless. However, we can group many of them together in a few categories.
- Calendar-based variables
- Media activities or marketing tactics
- External variables
- Internal variables
Calendar based
First, there are calendar-based variables of the market. Think of seasonal trends and major holidays that have an impact on your consumer’s buying patterns.
Media
Next, we have media activities, or marketing tactics. This category is a bit of a catch all for your advertising and outward marketing investments. It includes TV, print, outdoor, display, direct, search, social, etc. It can also include earned media mentions like those gained from your public relations efforts.
External factors
Third, we should consider external effects. This is a sort of “force majeure” category. It’s all of the factors that are out of your control like macroeconomic conditions, weather, natural disasters, competitor activities, and more.
Internal factors
Finally, there are internal changes that arise from alterations in how you do business. This can include a change to your product distribution, changes to the product or service itself, price changes and sales process changes. This category is akin to the classic "4 Ps" of marketing: product, price, and place — with promotion being covered by our media activities.
Product, Price, Place, and Promotion together are often called Marketing Mix elements. While MMM is about finding the optimal marketing spend mix, you need to add the other factors into your marketing mix model as well in order to account for those factors.
By measuring the business-critical variables in these categories, a MMM analysis can begin to identify which variable has the strongest contribution to changes in performance and which is driving your performance.
In other words, you can begin to model what would happen if you hadn't run that TV campaign, or if you added additional marketing investments to your mix.
Also read: Incrementality in marketing explained
And remember, the more data you feed into the marketing mix model, the more factors you can draw insights from.
Why you should implement MMM
As modern marketers, there are an ever-increasing array of different tools and media that we can leverage to get the word out about our product or service. Some of those tools are easily trackable, but some are not. Plus, some tools lose a bit of their attribution tracking capacity as you blend aggregated data with other tools.
This all means that marketers need a way to account for as many below- and above-the-line tactics as possible, while also digging into which is the most valuable for their marketing campaigns.
Alex does point out that the desire to precisely pinpoint exact attribution of each tactic is misguided. Rather, you can begin to get a sense of incrementality.
A birds eye view for you marketing strategy
With an market mix modeling approach, marketing managers and CMO's can gain a wider and more holistic view of their marketing ecosystem. With that larger, more bird’s eye view, they can get a better handle on which “levers” should be pulled at different times.
If we look back to our variable categories, just by monitoring external variables and our media activities, we can gain an understanding of how fluctuations in the consumer price index are influencing the effectiveness of targeted digital spend versus broadcast.
Sounds useful, right? Here are 3 more analyses that can benefit from an MMM approach.
- ROI analysis
- Forecasting
- Pricing
1. ROI and ROAS analysis
Which marketing investment is giving you the best return? ROI (Return on Investment) and ROAS (Return on Ad Spend) analysis are probably the most common use for marketing mix modeling. It gives business leaders a quick summary of where their money is being best spent. Then, they can make decisions about how to shift marketing investment strategies to keep getting the best return.
2. Forecasting
With forecasting and MMM, it’s less about using a crystal ball to see future sales and revenue, but instead about planning future marketing budgets and overall spend. After all, there are a lot of teams that will want to know what the marketing spend will look like in the months or year ahead.
While we won’t get exact future figures from MMM, it can be used to roughly predict what those budgets, and the impacts of any changes, will be. This is still quite useful, since it allows other parts of the business to plan accordingly.
3. Pricing
If you drop the price of your product or service, will more customers flock to it? Will you steal market share from a competitor? Or, does a price decrease lower the perceived value of said product or service? These are all incredibly difficult and complex questions to ask, requiring equally complex analysis to arrive at a reliable conclusion.
However, with the right data and analysis model, you can begin to make predictions about how the market may react to changes in your pricing structure.
What are some of the disadvantages of marketing mix modeling?
One of the drawbacks of market mix modeling, is that it requires a lot of high quality data. That might be a problem if you are working with a small marketing budget, or if your organization has only just began media buying at scale. (Most marketing mix models require at least two years of historical data in order to forecast.)
And even if an organization does invest a lot in marketing, it's often difficult to collect all the aggregated data needed for an MMM analysis. In order to effectively collect all that data, you'll need a robust tool like a marketing data hub.
Gaining your bigger picture
Market mix modeling can be a powerful tool that can help you identify incrementality, marketing effectiveness, measure ROI, forecast future performance, and more. And while it requires high-quality data, it is well worth investing in your data skills and maturity as an organization.
More frequently asked questions
How do companies integrate MMM findings into their strategic marketing plans?
Integrating market mix modeling findings into strategic marketing plans involves a detailed analysis to understand the impact of various marketing activities on sales and ROI. Businesses typically adjust their marketing budgets, reallocate resources across channels, refine target audiences, and optimize marketing messages based on MMM insights. The process requires collaboration across departments to align marketing strategies with overall business goals.
What are the specific challenges or limitations of MMM in digital marketing contexts?
Digital channels, like Google Ads, Meta, LinkedIn and others, generate vast amounts of data at a high velocity. It can be difficult to identify relevant variables and ensure data quality. Another challenge might be to get all that data formatted in the right way and use it for MMM analysis.
Can MMM be effectively used by small businesses or startups with limited historical data?
MMM can be challenging for small businesses or startups due to the requirement for extensive historical data. However, these entities can start by collecting and analyzing the data they have, focusing on key metrics that reflect their marketing efforts' performance. Over time, as more data becomes available, they can refine their market mix modeling approach. Additionally, they might explore simplified models or seek external expertise to leverage MMM principles effectively.
