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Five Reasons Why Media Mix Modeling is Broken

Volume 5, Issue 10 - October, 2015

Editor, IQ Advisor

Media mix modeling (MMM) has long been the de facto standard for evaluating return on advertising investment across all the channels that constitute a brand’s marketing ecosystem. But after decades of change in the media landscape, MMM is no longer an effective solution for understanding both consumers and the marketing used to influence them. Here are five reasons why more and more marketers are replacing MMM with an advanced, productized attribution approach to measure their cross channel marketing performance:

Infrequent Reporting
Traditional MMM engagements are highly consultative, taking months to deliver and providing a distinct “snapshot in time,” usually in the form of a PowerPoint/Excel presentation. Unlike MMM, advanced attribution is an ongoing process that enables the data to be updated as frequently as you like – whether it be daily, weekly, or monthly. Moreover, it enables you to view your cross channel marketing performance and identify media efficiencies all in a single user interface. This consolidated view of performance not only empowers internal teams to work more productively, but also enables faster collaboration with external vendors and/or media agencies.

Lack of Scenario Planning Capabilities
MMM provides limited insights and typically requires a team of data miners to find new opportunities for optimization. Advanced attribution, on the other hand, puts actionable insights at your fingertips by enabling you to see what has been working, as well as what is likely to work in the future. Armed with this kind of forward-facing insight and “what if” analysis available at the click of a mouse, marketers can make smarter marketing mix and budget allocation decisions to achieve the optimal return.

Siloed Modeling Approach
With more channels and choices than ever before, marketers need greater insight into how their efforts impact consumer behavior and what is really driving marketing results. Yet MMM rarely goes deeper than recommending how your budget should be allocated between channels. Advanced attribution, on the other hand, combines top-down modeling techniques that utilize aggregate-level data from non-addressable channels like TV, with bottom-up techniques that leverage user-level data from addressable channels like online display and paid search. This integrated approach enables you to easily identify the influences and synergies between channels and placement-level tactics, as well as how a change made to one or more of those tactics will impact the performance of all the others. Moreover, the insights and recommendations produced go beyond the channel level, drilling down to the most granular level of data.

Limited Audience Targeting Capabilities
When optimizing media, you need to understand the mix of channels, strategies and tactics that produce the best return across all prospects, as well as the mix that produces the best return for each specific audience segment. Yet MMM doesn’t tie cross channel performance insights back to specific audience segments. Because advanced attribution leverages both offline and online data to associate performance insights with the demographic and behavior attributes of customers and prospects, you can ensure your marketing efforts and spend are optimized to reach the right audience.

Lack of Automation
Unlike advanced attribution, MMM doesn’t allow optimization recommendations to be deployed into the marketplace in real-time. The ability to apply these recommendations to your daily spend decisions and operations, as well as automatically send media buying instructions to your DSP and/or RTB platforms, is essential for effective optimization.

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