A marketer's job is both simple and also maddeningly complex. All marketers want to get to the root of the question—“What’s working and what should I invest more funds in?”—but the difficulty comes in when trying to get the right data and the right software to answer those questions.
When it comes to high-level questions of which channels you should invest more funds in overall, MMM (short for Marketing Mix Modeling or Media Mix Modeling) is a time-tested method for assessing the correlation between the money you spend on advertising and the revenue you bring in.
While not a new idea in any sense, MMM is seeing a resurgence in a climate where user-level data is less available. MMM is also attractive to budget-conscious marketing leaders who need clear answers for where to direct spend based on what’s worked in the past.
In this blog, we’ll dig into everything MMM, from its definition to history to applications and beyond. If you’re considering adding a new measurement method to your toolbox, this is a must read.
What is MMM (Marketing Mix Modeling)?
Marketing Mix Modeling, aka Media Mix Modeling, describes the statistical process of determining the relationship between advertising spend and revenue over a historical period of time at a business.
Essentially it answers questions about how revenue increased or decreased with changes in ad spend and mix that can help marketers understand where it makes the most sense to continue or discontinue spending. MMM can also help marketers estimate how potential changes in spend in the future would affect revenue based on that historical data.
When MMM models are run, they also take into account seasonality, weather, and other factors during the time period that may have had an effect on sales. What you’re left with is the closest to the exact correlation between ad spend and sales possible without any elements that might obscure the results.
When and Where Did MMM Originate?
MMM initially came into popular use in the 1960s when the advertising landscape was much simpler than it is today. Big brands like Kraft and Coca-Cola used data-driven marketing mix modeling to understand which magazines or TV channels were most effective at driving sales. Without the software we have today, MMM still allowed early marketers to examine the relationship between their marketing and revenue across various regions and time periods to gain understanding into what was most effective.
As digital advertising gained popularity, MTA (Multi-Touch Attribution) overtook MMM due to its ability to input granular information on users and their activities and map purchases back to specific channel impact.
Historically, and even until recently, MMM has most frequently been leveraged by larger companies—this is because the statistical regressions required to power MMM are time-consuming and require large amounts of internal effort or large sums of money to work with external firms. However, in recent years, several software platforms are emerging that simplify and democratize the MMM methodology to make it more accessible to more businesses.
In March 2023, Rockerbox launched MMM for Shopify (beta), which is an affordable, self-service MMM option for marketers that want both the high-level guidance of MMM and the granular day-to-day insights of MTA.
Rockerbox MMM for Shopify
While historically MMM often involved pricey analyses conducted by agencies that only revealed results for a point in time, Rockerbox offers a version of MMM suited for the modern marketer. Rockerbox MMM for Shopify is priced with the average ecommerce or B2C business in mind and offers regularly updated charts and graphs that let marketers dig into their own data and build out custom budget recommendations.
Why to Choose Rockerbox as Your Marketing Mix Modeling Provider
Rockerbox MMM for Shopify can be used to answer questions like:
- What is the correlation between marketing spend and revenue?
- What should my ad spend be if I want to go after a specific ROAS target?
- What revenue and ROAS can I expect if I implement certain budget decisions?
Specifically MMM for Shopify (beta) lets you look at the high-level correlation between ad spend and Shopify sales. The beta includes top digital channels to start: Google, Facebook, Bing, TikTok, Pinterest, Snapchat, and LinkedIn.
How Does Rockerbox Marketing Mix Modeling Work?
In the Rockerbox platform, this add-on feature includes several UI views to allow you to explore this relationship with your goals in mind:
- A spend recommendation tool that lets you explore the relationship between spend and revenue optimized for various ROAS values.
- The Marketing Budget Worksheet, which gives you guidance on where to allocate spend for the best return and can be exported for further analysis.
- Graphs showing the data behind the model so you can see that the insights are backed in real performance information from your company.
Pros and Cons of MMM
When thinking about the pros and cons of MMM it’s also important to think about how MMM has evolved over the years. Some of the downsides to using MMM that people will mention apply to older ways of doing MMM that are being rectified by more modern methods. That said, there are several things to be aware of when applying MMM at your company.
MMM excels at high-level insights. Because it’s based in your own data, it can offer realistic projections of how your revenue will be affected by changes in spend in the future and can be an incredibly helpful tool for budget-setting. It also doesn’t rely on user-level data and can be used to measure the impact of some hard-to-track channels that it would be hard to analyze otherwise.
When it comes to the traditional idea of MMM, many complain about things like the time involvement and static, point-in-time nature of MMM results. While more and more solutions are offering more agile outputs, many MMM platforms (or agencies that run MMM analyses) are prohibitively expensive (even to the amount of millions per year).
Rockerbox’s MMM for Shopify subverts expectations in this area by offering MMM at $1,000/month—literally 1,000x less expensive than other options.
One other legitimate con of MMM is that it is not helpful for the granular, day-to-day optimization of individual digital channels. For that, you may need to incorporate other methods, such as user-level attribution, to accurately understand performance.
Marketing Mix Modeling + Multi-Touch Attribution — Marketing Measurement for Every Situation
At Rockerbox, we support a multi-methodology approach to marketing measurement. We think you should use different techniques for measuring marketing effectiveness depending on the exact questions you’re trying to answer. When it comes to high-level budget allocation, MMM can provide the clear-cut direction you need—then when channel managers need to access daily insights for optimizing the platforms they manager, you need MTA (multi-touch attribution) to monitor performance and make adjustments.
With MMM and MTA (and lots more insights around the customer and their journey), you’re fully equipped to scale your marketing and your business with confidence.
Ready to learn more about Rockerbox’s accessible MMM solution? Contact our team today for a demo.