Marketing Mix Models & Multi-Touch Attribution - Why Marketers Need a Unified Solution

In today’s fragmented media landscape, the consumer’s path to purchase has evolved into a meandering trail of online and offline touchpoints, making it even more difficult for marketers to understand the true return on investment. This shift has caused the need for marketers to leverage measurement tools and rich data in order to make truly informed marketing decisions. Predictive models are the chassis and engine for data-driven decision making. When allocating marketing resources and executing marketing tactics, there must be a predictive model that links marketing actions to marketing objectives, sales--for example, so that marketers can gain full clarity over their efforts on a micro to macro level.

Here are three reasons that it’s essential for marketers to leverage a single, unified measurement model:

  1. The two measurement models in use today are fundamentally different. Today’s most competitive marketers use marketing mix models (MMM) and multi-touch attribution (MTA) models to inform strategic and tactical decisions respectively. Each model lives in isolation, uses different sets of data, and shares some features. Where the marketer subscribes to both models the two incompatible pieces are cobbled together either formally by an MMM+MTA provider or informally by marketers themselves.

  2. Processing different data results in different outcomes. Because the models are developed using different data, the results are most often biased. What’s the impact? Certain factors are accounted for in one measurement model but precluded from the other and given that that the models are substantively different in the way they link marketing actions to marketing objectives, you end up with results that are both conceptually and mathematically irreconcilable. The result is a ”house of cards” solution that jeopardizes the effectiveness of millions of media dollars.

  3. The current solutions can’t work when forced together. Marketing-impact analysts tend to think about a “unified” model in two different ways, one that cobbles together separate but linked equations, and one that ensembles models as a weighted-blend which is determined by their ability to predict performance. These two perspectives are in stark contrast to thinking about it in a cohesive singular framework.

    The issue with the first approach is that it doesn’t resolve the conflict between the two sources of information. This happens partly because an MMM model has no knowledge of how individual consumers make choices. Forcing separate equations together is taking the easy way out.  It estimates the impact of aggregate data and user-level data with two different models rather than think about modeling how consumers truly make decisions.

    The issue with the second approach is that it determines the blend of models based upon how well they fit observed behavior rather than how well they predict the causal impact of marketing activity. This trade-off sits squarely against the whole point of predictive analytics, which exists to measure the causal impact of marketing, and guide marketers to make financially efficient and effective decisions.

A new, more elegant solution is required.

A sophisticated unified measurement model will yield major marketing outcomes. In order for MMM and MTA to exist harmoniously they must be effectively unified in a single model. They must establish the same link between marketing actions to marketing objectives, and they must process the same data inputs, considerations, and variables.

Having a model that exists within a single framework allows the entire marketing organization, from the channel marketing managers to the CMO, to more effectively execute and optimize marketing efforts.  A single model provides a consistent and more accurate set of metrics, better informs how to drive decisions and allocate marketing budget, and helps marketers influence the entire consumer journey and achieve key objectives.

Want to learn more about MMM+MTA best practices? Get in touch with one of our experts,

This post was written by Michael A. Cohen, Ph.D., Head of Data Science & Analytics, AOL Convertro