In this article, we'll cover what marketing/media mix modeling (MMM) is, why it's valuable and when you might want to use it. We'll also explain how you can use attribution versus MMM.
What is Media Mix Modeling?
As a marketer, it's important to understand the effectiveness of all your marketing efforts. You might have a great Twitter feed but find that Facebook ads don't bring in any conversions. Or maybe you've spent months developing an email list, only to find out that the people on it are less engaged with your content than before.
Mixed media modeling (MMM) is a technique for understanding how your various different marketing efforts are working together—and sometimes against each other—to drive conversions or sales.
The goal is to accurately attribute the right amount of revenue to each channel, which can help you understand what's working and what isn't.
A media mix model is an advanced form of attribution: instead of simply attributing a sale to the last channel someone interacted with, MMM attempts to understand each marketing effort's long-term effectiveness and how well they work together. The process involves collecting data from multiple channels (e.g., email, TV ads, Facebook) and then analyzing it in order to determine which channels are driving sales or conversions.
Why is media mix modeling valuable?
Media mix modeling is valuable because it helps you understand how your marketing efforts are working together. Knowing the impact of each piece of your marketing program allows for making better decisions and planning for the future.
You can use media mix models to:
- Understand the relationship between your marketing efforts by examining their relative strengths and weaknesses
- Measure the effect of individual campaigns and advertising messages on sales, profits or other business metrics
- Predict pricing and product changes that will maximize overall performance
Risks of Media Mix Modeling
Media Mix Modeling is not a perfect science. While it can be used to predict the impact of a marketing campaign, it's important to keep in mind that even the best models have limitations and errors.
MMM is not a substitute for good marketing. If you want your marketing efforts to succeed, you need to understand what works for your target audience and what doesn't work for them. This can be done with qualitative research methods like focus groups or usability tests; quantitative surveys; A/B testing; controlled experiments; etc..
MMM is not a substitute for good analytics. You also need to know how many people are actually exposed to each channel so that you can adjust accordingly if needed after seeing the results from your campaign (or campaigns). Analytics include things like visitor tracking (Google Analytics), website activity tracking (Hotjar), email open rates/click-throughs/conversions (Litmus), advertising performance metrics (Facebook Ads dashboard), etc..
Attribution versus media mix modeling
Attribution is a way to understand how your different marketing efforts are working together. Media mix modeling is a tool that helps you plan, measure and optimize your marketing campaigns - but it's also an attribution tool. Attribution helps you assign value to different channels, which in turn helps you determine whether or not those channels are worth continuing to invest in.
Mixed messaging? Not at all! Attribution and media mix modeling are distinct terms but they're related concepts: they both deal with understanding how different marketing efforts interact with each other (or don't).
Rockerbox leverages a number of measurement methodologies to help you to draw the most complete picture of your marketing efficacy.
- We use first-party data to help you understand how different marketing channels are contributing to your overall goals.
- We use our own industry data to help you understand how well the campaigns and tactics you’re running are performing on an industry level.
Media mix modeling is a powerful tool that allows you to understand how your different marketing efforts are working together. This helps you make better decisions about how much money to put into each channel and what types of content to create based on the audience’s needs.