Marketing Mix Modeling
Which One Is Right for Your Business?
Although some have termed 2016 the year of attribution, many marketers (and their bosses) still aren't clear on exactly what attribution is. The concept is simple: You attribute credit and value where they are due.
And in today's analytics-driven, multichannel world, we all want to know not only whether what we're doing is working but also where, when, with whom, and how well.
But drill down a level, and most marketers are unclear which measurement approaches are right for their business:
- Do I need high-level insights around budget planning, or tactical insights to optimize in a channel?
- What can I get out of my existing tools, like site-side analytics?
- When do I need something specific?
- What's the difference between all the solutions that claim to offer attribution, and which one makes sense for me?
To answer those questions, marketers need a clear understanding of their options. That starts with defining two of the most common approaches to marketing measurement: marketing mix modeling (MMM)—used interchangeably with media mix modeling—and attribution.
Both MMM and attribution are sophisticated models for measuring cross-channel marketing activities, but they work in different ways, for different reasons.
Marketing Mix Modeling and Attribution Defined
Search for the "technical" definitions of MMM and attribution, and you won't get very far in comparing their differences. Gartner defines marketing mix modeling as "analytical solutions that help marketers to understand and simulate the effect of advertising, and to optimize tactics and the delivery medium."
Gartner's glossary doesn't offer a definition for attribution, but its friends at Forrester define it like this: "The practice of using advanced statistical approaches proportional credit to marketing communications and media activity across all channels, which ultimately leads to the desired customer action."
You can sense some nuances there, but not much helpful differentiation. So let's break it down ourselves. What's the difference between marketing mix modeling and attribution?
Marketing Mix Modeling and Attribution Compared
MMM and attribution differ across several key dimensions: approach, media type, time frame, and data inputs.
MMM: Top-down, macro-level, econometric models. MMM analyzes historical data on independent events to deliver organization-level planning, spending, and performance metrics. It measures the overall value of the marketing function.
Attribution: Bottom-up, micro-level, data-science models. Attribution uses user-level data to construct and analyze the path to conversion, from vendors to channels to individual creative. It measures the independent value of specific marketing tactics.
MMM: Primarily offline. MMM grew from CPG brands' interest in the impact of traditional marketing activities, such as TV, radio, print, and point of sale. That focus is changing as a result of the growth of digital marketing, but MMM's strength remains offline.
Attribution: Primarily online. Attribution grew out of the wealth of available digital data and need to evolve from last click/view. It focuses mostly on online sales and other digital conversion events. That focus, too, is evolving as marketers seek to understand how their digital programs influence offline channels and vice versa.
MMM: Uses aggregated, historical data based on weekly increments. Typically performed bi-annually or annually.
Attribution: Uses account-specific, real-time or near real-time data measured down to the second. Results updated daily.
MMM: High-level marketing and sales data, plus external factors. MMM integrates marketing and sales data, especially offline-centric factors such as revenue data, benchmark data, and marketing cost data, with outside and macroeconomic variables, such as market elasticity, marginal profit, seasonality, and even weather and recent news events.
Attribution: Granular marketing and sales data. Attribution measures each touchpoint on an individual user's path to conversion using advanced algorithms to understand how each media asset performed along that path. It integrates with ad tech providers, such as DSPs (demand-side platforms) and DMPs (data management platforms), for a broad and deep view the digital landscape. Most attribution solutions are capable of incorporating seasonal, geographic, and other attributes as well.
Which One Is Right for Your Business?
Both attribution and marketing mix modeling offer marketers valuable insights into how their marketing investments are performing. The best approach for your business depends on your objectives and KPIs. It may be attribution, MMM, or a combination of the two, such as unified marketing impact analytics—more on that later.
MMM: Breadth—the ability to analyze offline variables, including those outside of the marketer's control, alongside planning, spending, and performance metrics in a single view. MMM is particularly effective for "push" marketing tactics driven by specific events from which customer reactions are spread over a relatively short period of time. It also excels at quantifying the financial value of brand ads.
Attribution: Speed—the ability to understand how channels, tactics, spends are performing on a daily basis, particularly for "pull" marketing, when the event is triggered by the customer. Attribution enables marketers to update campaigns on the fly, such as shifting budget by a tactic, as well as glean long-term insights around scenario planning and forecasting. The granular view also allows for insights not possible from a higher perspective, including subtle synergies between channels and the ability to allocate revenue to channels proportional to impact.
MMM: Because of MMM's longer time frame, the factors that inform the model can change after the data is collected and analyzed, affecting the accuracy of long-term forecasting. Accordingly, short-term, "on the fly" optimization is a challenge, too. Marketing activities that show a lack of variability, low reach, or strong correlation with larger elements in the same plan are challenging to measure with MMM as well. And aggregate data that averages all consumer behavior does not provide the user-level insight that goes beyond analytics into understanding and optimizing the customer journey.
Attribution: Attribution models tend to skew toward digital channels and customer acquisition, with less emphasis on offline strategies and external factors. In underdeveloped solutions, this can lead to over-attribution to digital activities. Attribution traditionally did not account for baseline conversions (those that would have happened anyway, without any marketing efforts) as well as MMM, although that's changing in newer models. And attribution solutions based on single models, whether simplistic (such as last-click) or more advanced (such as game theory), may not meet the needs of every use case and business model.
MMM: Brands that focus on offline channels, especially CPG; businesses highly affected by external factors and branding; and organizations that don't have user-level data.
Attribution: Brands with significant cross-channel marketing investments, especially in the digital space; companies focused on customer-based optimization and insights; and any organization focused on user-level conversion.
Where We're Headed
Although marketing mix modeling and attribution are different methods that meet different needs, the decision isn't either/or. One approach may provide exactly the information and analysis you need, or it may not. Depending on your organization, you may benefit from one, both, neither, or a blend of the two.
Forrester is calling that last, integrated approach "unified marketing impact analytics (UMIA)." UMIA combines the top-down and bottom-up methodologies with cross-channel modeling to deliver results that perform equally well online and off. It borrows high-level objectives and outputs from MMM, such as the ability to assess the overall value of your marketing programs, with the granularity of attribution, such as optimization by channel, to calculate both the short- and long-term impact of media. That makes it both fast and thorough.
Although UMIA is a newer concept and it's difficult to execute, it promises to be more practical and actionable to marketers up and down the chain, especially those charged with cross-channel impact and incrementality.
The Bottom Line
As marketing technology evolves, the lines between these categories and solutions continue to merge and blur. It's important to understand the available offerings, but it's more critical to choose a measurement tool that helps you understand and optimize your marketing investments—no matter what it's called.
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