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For years, marketers have used the analysis technique of media mix modeling (MMM) — also referred to as marketing mix modeling — to measure and compare the effectiveness of various marketing channels to help inform long-term planning and improve decision-making.
In this guide, we look at how MMM works and some of its key elements. We also assess its advantages and shortcomings, and explain how using call tracking technology to get full attribution for every media channel can deliver real value to your MMM and help increase your marketing return on investment (ROI).
There is actually little difference between media mix modeling and marketing mix modeling; the former is really just a more modern label for the latter.
MMM is a method of analysing sales and marketing data that has been around for more than a half-century. In a modern marketing context, media mix modeling can be particularly useful to marketers because it offers an approach to measuring the performance of digital channels and offline performance.
MMM can provide high-level, holistic insight into marketing activities and performance by analysing the historical contributions of each media channel to overall sales. This creates a picture of the effectiveness of each channel and its impact on ROI that marketers can use predictively to make better decisions when planning future campaigns.
Media mix modeling could prove very useful to your business in the current economy in the same way that marketers have found the method helpful in trying to make sense of the COVID-19 pandemic’s impact on marketing performance. Honing your analysis of marketing spend and ROI before, during, and after a pre-recessionary economy can equip you with insights and tools to better prepare for and successfully navigate future downturns.
MMM involves performing a statistical analysis using multiple linear regression to highlight the relationship between sales or conversions and ad spend. It takes historical aggregated data, typically two to three years’ worth, from marketing and non-marketing sources to identify precisely what causes sales.
Consider MMM the “50,000-foot view” of marketing effectiveness. Unlike most other marketing metrics, media mix modeling doesn’t utilise user-level data such as ad impressions or clicks. In short, it doesn’t measure the customer journey.
MMM instead provides a more holistic picture of the connection between marketing and sales performance, which can help inform strategic long-term planning. Showing which channels are working and which ones aren’t allows marketers to refine their strategies and deliver better outcomes from campaigns.
Consider this example: Say your automotive OEM (original equipment manufacturer) had $10 million in sales in the week of its 0% APR financing event. During that week, the marketing plan called for paid search ads, some regional TV ads in your top four markets of Seattle, San Francisco, New York, and Chicago, and social media outreach through Instagram posts and YouTube videos. You got a ton of test drives booked through your website, as well as over the phone.
By using digital media optimisation tools and MMM, you can determine how much of your total sales in that week were attributable to each marketing channel. The percentage of sales not attributable to any of those channels is your baseline, and it tells you what level of sales you might expect without doing any ads at all. You also have to account for your special financing event to make sure your overall analysis is accurate and not skewed to a major sales success.
For the MMM process, you can use data gathered from marketing channels and actions such as:
Additional information you might want to include in MMM could be seasonal factors such as holidays or established events (like the special financing offer in the example above). Weather conditions that might affect shopping patterns, and economic data such as unemployment rates, consumer confidence numbers, fuel prices, and inflation might also factor into your modeling process. Also, you could incorporate competitor data such as promotions and pricing, new product releases and advertising, and insights from actual customers gleaned from conversational analytics.
These five elements should be incorporated into any MMM analysis:
Media mix modeling can measure sales volume on both a base and incremental basis. Incremental sales are those in which marketing plays a role. Base sales are influenced by non-marketing-related factors such as seasonality, pricing, and branding.
Incorporating data into MMM from media and ad buys can result in valuable comparisons of ad platforms and the impact of your ads. For example, you can compare the relative performance of ads on competing platforms such as Google and Facebook to discern if your target audience is more responsive on one versus the other.
One of the biggest conundrums that marketers face is knowing when to adjust pricing, since any change up or down could critically impact sales, positively or negatively. MMM is useful here because it can help you pinpoint the impacts of price change strategies, allowing you to better understand the relationship between price changes at key moments for your brand.
Here’s one that’s particularly important coming out of the global supply chain crisis fueled by the COVID-19 pandemic. We’ve all learned by now, if we didn’t know it before, that supply chain issues can have a dramatic impact on sales. Using distribution data gathered over the last two years in media mix modeling could be incredibly valuable for companies’ forward planning.
If you roll out a new product, you assume (and hope!) that it will have a positive impact on your company’s sales volume — but MMM can tell you exactly how much impact. This knowledge can inform the type and timing of future product launches.
Media mix modeling tools include Python, a programming language useful for building media mix models. MMM employs multiple linear regression using dependent (sales) and independent (distribution, pricing, media spend) variables to come up with a linear or nonlinear equation that can be used to assign ratios to each marketing channel.
For more detail on using media mix modeling data science to calculate marketing’s impact on sales, check out the MMM primer provided in this post.
Media mix modeling and data-driven attribution provide very different but complementary insights into marketing effectiveness.
As noted earlier, MMM can help you generate a big-picture view of your company’s marketing tactics and performance over time, so you can develop more effective marketing strategies. However, media mix modeling doesn’t provide a more granular, personal-level view of a customer’s journey through the sales funnel.
Data-driven attribution can give you that view. You can use this approach to measure the effectiveness of a specific campaign or series of campaigns once those efforts conclude.
Both marketing attribution modeling techniques are useful, and together, they can help you optimise your marketing efforts by giving you insight into which and how many channels and campaigns have had the biggest impact on marketing ROI.
Media Mix Modeling goes beyond simply measuring marketing performance. It acts as a strategic lens, offering a high-level overview across all marketing channels, revealing the intricate relationship between marketing efforts and sales performance. This comprehensive analysis unlocks valuable insights that empowers businesses to:
Imagine a marketing budget spread thin across various channels, with limited understanding of which ones truly drive sales. Here's where media mix modeling shines. By analysing historical data, it identifies the most effective channels and quantifies their impact on sales. This allows businesses to:
MMM goes beyond basic attribution models, revealing the synergistic effects of different channels. Analysing how channels interact with each other provides a deeper understanding of the customer journey. Businesses can leverage these insights to:
Marketing decisions shouldn't be made in a vacuum. Media mix modeling helps businesses look beyond immediate results by enabling:
Media Mix Modeling offers valuable insights for marketers, but it's important to understand its limitations before diving in. Here are some key shortcomings to consider:
MMM analyses overall sales data, providing a high-level view of marketing performance. However, it doesn't capture the individual customer journey. Unlike other attribution models that leverage personal-level data, media mix modeling can't pinpoint the exact touchpoints that influenced a specific purchase decision. This makes it difficult to assess the effectiveness of highly targeted campaigns or those focused on specific audience segments.
Media mix modeling relies heavily on historical data to build its models. If your business is relatively new or lacks at least two to three years of consistent marketing and sales data, MMM might not be the most effective tool. Without a substantial historical record, the model may struggle to identify accurate cause-and-effect relationships between marketing activities and sales performance.
Building and refining a media mix model can be a time-consuming process. This makes it less suitable for situations requiring ongoing, real-time optimisation. While MMM can provide valuable insights into long-term trends, it may not be ideal for teams needing to quickly adjust and optimise campaigns based on immediate performance data.
The marketing landscape is constantly evolving, with new channels emerging and consumer behavior shifting. MMM models can struggle to adapt to these rapid changes. If significant disruptions occur, such as a new competitor entering the market or a major social media platform altering its algorithm, the model's accuracy might diminish, requiring updates and recalibration.
Media mix modeling is most successful when marketing receives full attribution for the campaigns it develops that drive sales. It’s easy to miss the full picture on attribution, though, especially when a high proportion of sales may be happening offline via phone calls to your contact centre.
Marketing attribution solutions can help fill the gap in attribution models by using call tracking to identify conversions made over the phone. With Invoca’s call tracking software, for example, you can easily develop a complete picture of your true marketing performance and the conversions your media channels are driving by including outcomes from both calls and digital clicks. This makes attribution modeling easier, and it can help you defend your marketing spend, too.
Want to learn more about how Invoca can help you track the phone leads driven by your media campaigns, and decrease cost per acquisition? Check out these resources:
Download our Ultimate Guide to Reducing Wasted Marketing Spend to learn how you can lower your CPA while improving marketing campaign performance.
