The Real Meaning of “Data-Driven” Attribution

June 5, 2014

This is a guest post from Casey Carey - Head of Marketing, Adometry by Google.

Over the past few weeks, there has been a significant amount of discussion regarding attribution models. At this point, you may be curious about how data-driven attribution, or any attribution model for that matter, fits into your marketing organization’s priorities.

Score Keeping without the Blinders

If you watched the Preakness or Kentucky Derby recently, you may have noticed many of the horses wearing blinders, a marketing measurement metaphor if there ever was one. In a sport with such a small margin for error, these eye covers play a vital role in helping the horses maintain focus on the path ahead rather than what is taking place on either side. Unfortunately, for years many marketing organizations made decisions with a similarly narrow viewpoint with the idea being, let’s look at each of these things individually and then compare results at the end. Often this was done out of necessity more than desire, but the byproduct was typically a combination of poorly-optimized campaigns followed by disappointing results and difficult questions from stakeholders across the company.

Marketers are rethinking how to best engage audiences during a period of rapidly shifting consumer behavior. By now it should be clear that the “customer journey” is no longer a straight line or predictable path to purchase. In fact, roughly 65 percent of all revenues come from multi-touch conversion paths, the majority of which involve impressions across multiple channels. Intuitively, marketers know customers are engaging with the brand across channels, but the vast majority still lack the ability to monitor and measure the impact of these interactions holistically. This causes a disconnect between what channel-specific reports say they contributed to revenue versus what actually occurred.

Learning to Trust the Data Begins with Openness

One common barrier for many organizations when attempting to adopt data-driven attribution methodologies is a feeling they are trapped by existing investments. Sometimes this takes the form of data trapped inside vendors’ proprietary reporting systems, other times it might be internal processes or change management issues obstructing anything from rocking the proverbial measurement boat. In either case, the result is marketers receiving multiple versions of the “truth” instead of a united picture that allows them to analyze and optimize their marketing mix using data-driven methodologies that take these variables into consideration.

Luckily, platform providers, like Marin, are opting to build an open ecosystem in which data can be incorporated from a variety of sources—including site and ad analytics as well as e-commerce data—to enable customers to make informed decision based on the entirety of data available.

From a measurement standpoint, this is invaluable and allows marketers adopting data-driven attribution methodologies, such as Adometry’s, to seamlessly incorporate or “operationalize” attribution insights into day-to-day decision-making workflows. This not only solves the a major data consolidation challenge but also completes the promise of data-driven attribution—trusted measurement that provides not just feedback on how you’ve done but also offers guidance on how you can improve moving forward.

No one is saying this is easy. Marketers faced with consolidating data from a non-trivial number of channel-specific sources and analytics tools know that this takes time and commitment. If you’re struggling with where to get started, these 10 Tactics for Building an Effective Attribution Management Program will help. Attribution is a marathon, not a sprint. But there’s no time like the present to get started.


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