“You can’t manage what you don’t measure.” This is the idea behind media mix modeling.
If you’re not measuring your marketing efforts and analyzing them regularly, how will you know what’s working? How will you know whether or not you are spending your budget effectively?
The answer: you won’t. Your company will miss out on opportunities to optimize marketing efforts and increase ROI.
A data-driven approach to digital marketing will help you understand how people interact with your content and campaigns. You’ll better understand what they want, and how to improve their experience to convert and retain customers.
Media mix modeling helps you identify the effectiveness of various marketing channels and make informed decisions about where to invest your budget. But, what is media mix modeling precisely? How can you use it to grow your brand?
What Is Media Mix Modeling?
A media or marketing mix is the combination of all the media or communication channels you use in your marketing. For example, your media mix may consist of traditional and digital channels—print, broadcast, linear and connected TV, social media, mobile app, and search engine marketing.
Media mix modeling (MMM) is an analysis technique to help advertisers understand the impact of different media channels on their bottom line. You may also hear it referred to as marketing mix modeling.
You see how each channel in your marketing or media mix influences brand awareness, purchase intent, and other key performance indicators.
To do this, you need historical data from multiple sources. Then, you have to aggregate and analyze that data to identify trends that can impact conversion like seasonality, pricing, promotions, and so on.
The result is an actionable report that shows what media mix will work best for your business at any given time of year.
Media Mix Modeling vs. Attribution
Media mix modeling differs from attribution because it’s more holistic. It helps you see the big-picture impact of marketing strategies across channels. Attribution looks at individual channels to determine whether they’re responsible for driving sales and revenue.
Attribution helps you understand how each channel contributes to your overall success, but it doesn’t consider how they fit together as part of an integrated marketing strategy. In contrast, media mix modeling is used when there’s a need to analyze long-term trends and understand how current efforts will impact future results.
Attribution models like multi-touch attribution are incredibly important. However, brands can benefit from combining strong attribution with media mix modeling.
When you use media mix modeling, you’ll get aggregate data showing:
- How much money you spent on each channel and overall
- What impact that had on marketing performance and sales
On top of that, many marketers also use incrementality measurement. When combined, all three create a complete picture of your marketing performance.
How Is Media Mix Modeling Used?
Media mix modeling provides a way to measure and compare the effectiveness of media channels, as well as predict future performance.
It can help you decide which channels to invest in and which to cut, as well as predict the performance of new campaigns. It also helps you determine how much of your budget you should allocate to each channel. Here’s how:
MMM uses a multi-linear regression model to estimate the relationship between media spending and sales volume.
In statistics, multiple linear regression uses two or more independent variables to predict the outcome of a dependent variable. In marketing, this means you are comparing independent variables (ad spend) on each channel with dependent variables (sales and market share).
Marketers can use MMM at all levels—from brand managers to CEOs—to understand how their marketing efforts are working (or not).
How Media Mix Modeling Works
MMM can be a messy, complex endeavor. There are several steps involved, and they may vary depending on the nature of your business. Here’s an overview of how it works:
1. Data collection
Data is essential to any marketing strategy, and media mix modeling is no exception. Not only must you collect data, but the quality of that data can make or break your decision-making process.
First, you need to collect data from real people and not just any data. You should use first-party data. If you’re using unreliable or incomplete data to create your models, they probably won’t be very accurate. Before moving forward with MMM, you must ensure that the data is correct, complete, and representative of your target audience.
The data you collect should be as specific and detailed as possible, including information about the person’s age, gender, location, income level, education level, and occupation. For MMM, specifically, you’ll want to know your spending and costs for each channel you use. Then, the CVR or conversion rate, transactions, and sales.
2. Channel mapping
Next, map out the channels you will test (if any) and ensure there’s a straightforward way to gather analytics about them all.
For example, if you’re testing social media ads on Facebook and Instagram, you should also include a link to a survey that asks respondents which platform they saw your ad. This will help you determine which channel is the most effective and whether it’s worth investing in more ads on that platform. Be very clear on how you will get the analytics you test, so you can easily compare the different channels’ results and determine which is most effective.
3. Aggregating results
Once you have your data, it’s time to analyze it and determine which channel was most effective at driving traffic and sales from each ad campaign.
This will allow you to decide better what marketing efforts will succeed or fail based on all available information combined instead of relying on anecdotal evidence from each piece alone.
4. Determine the cause
Once you know which ad was more effective and why you can use this information to build a better strategy for your next campaign. If you saw a lower ROI from the ad campaign, it might be because your audience is skewed toward people who are less likely to buy. If this is the case, don’t waste time and money trying to convert these people through more ads. Instead, focus your efforts on attracting new customers (or ones who are more likely to buy).
5. Keep testing and learning
Note that this is not the end of your testing. It’s simply a step along the way. You’ll want to continue running these tests until you’ve found what works for your business and can replicate that success repeatedly.
Media mix modeling will show you where to allocate your marketing spend based on what has worked best historically across all channels at every stage of your campaign lifecycle: awareness, consideration, purchase, and even loyalty building.
You can use this data to continually optimize your campaigns so that they get better over time. This is especially important for eCommerce companies, which generally have a higher return on investment (ROI) than traditional businesses.
If you’re already running ads and want to take your marketing strategy to the next level, consider trying out media mix modeling.
Example of Media Mix Modeling
Media Mix Modeling is a tool for measuring how much you should spend on different types of advertising. It helps you to decide how much money to spend on each ad and where it will get the best return on investment (ROI).
Let’s say you’re an advertising manager at a company selling products via traditional retail channels. You want to know how much more profitable your advertising efforts will be if you increase spending for some or all of your campaign components (TV ads, billboards, print ads)
To do this, you run through a few steps:
- Determine how much you spend on each type of advertising (e.g. $10K for TV ads and $50K for influencer marketing).
- Calculate each channel’s average return on investment (based on your current campaign). For example, if you spent $10K and received $100K in revenue from your sales campaign, your return would be 10%.
- Calculate the difference in return between your current campaign and an updated one by subtracting each channel’s average ROI from its respective cost.
- Add all these differences to get a total value of increased ROI. For example, let’s say your TV ad campaign was $10K but only returned $100K in revenue. On the other hand, an influencer marketing campaign cost $50K but returned $650K. Through MMM, you might consider influencer marketing more profitable, and decide to increase your spending there instead of on TV.
The Power of Media Mix Modeling
Media mix modeling is not simply a matter of spending more on the media you think will work best. It’s about finding that perfect blend of channels. Then optimizing them to reach your target audience at the right time, in the right place, and for the right price.
The media mix modeling process starts with an understanding of your target audience. You need to know where they are, what they do, and how they think to make the most effective ad spend decision.
The next step is identifying which channels will reach the right people at the right time. Once you’ve determined which media outlets are best suited for your campaign goals, optimize their performance against your objectives and goals.
The result is an optimized media mix that will provide the best return on investment for you.
The future of media mix modeling is promising. It allows advertisers to make decisions based on data instead of intuition.
With it, you can quantify how much each type of media contributes to your success. budget allocation and campaign strategy. It also allows them to create more effective campaigns, leading to greater ROI. Goodway has data scientists that can conduct media mix modeling, along with other techniques, to optimize your marketing. With us, you can easily analyze how different media channels work together and find the most effective strategy for your brand. Contact us today.