The lack of a viable attribution method has proven to be one of the most confounding dilemmas facing digital marketers. The last ad gets all the credit for a conversion regardless of the preceding exposure stream. This logic is clearly flawed. But last ad metrics consistently permeate digital media reports.
The use of the last ad model drives budgets toward bottom-funnel tactics while the top-funnel suffers from under investment. Compounding this, the industry continues to struggle with basic issues such as the appropriate budget allocation across display and search.
There is a profound disconnect between expectations of what should be possible and the digital marketer's ability to deliver. Terabytes of digital data create the prospect of real time intelligence and smart decision making, but digital metric models are significantly flawed.
Ultimately, the lack of reliable measurement undermines the overall accountability model for the digital sector. And lack of faith in the metrics means lower investment while the potential for increased spend is stuck on the horizon.
In charting the path to a solution it is instructive to understand how the industry wound up in this predicament in the first place.
The birth of digital media in the late 90s was followed by the realization that offline measurement models do not translate to new media. This engendered extreme uncertainty regarding appropriate metrics. The industry needed a solution to grow – and needed one quickly. Last ad attribution delivered a solution that was simple to implement and at the time seemed intuitive.
Technology provided another constraint. Digital tracking generates terabytes of data and sophisticated attribution models need very advanced technology to churn through all this data. Ten years ago the technology was not up to the task.
The last ad model may have been the best solution we had available a decade ago. And perhaps the last ad model deserves credit for helping the digital media grow out of its infancy phase. However, the industry has evolved and so has the technology. So why have the measurement systems not followed?
Unfortunately, standards tend to have a life of their own. The more people adopt a standard the more ingrained and harder it is to change. In other words, the last ad model has stuck largely for the same reason that Microsoft Windows did – first mover advantage effect in a highly-networked and interdependent ecosystem.
While the transition to next generation attribution is a daunting challenge, the climate is ripe for change. The disillusionment with last ad measurement is pervasive and profound. And the advent of cloud computing has lowered the technological hurdles.
Transition to dynamic attribution requires the right model and the right team. First, let us take a look at the success factors for the model:
· Objectivity. Next gen attribution models must be fact-based and driven by algorithms rather than by subjective judgment or assumptions.
· Accountability. The model must deliver a measure of marketing effectiveness that is better than the last ad model. This requires explicit benchmarks that support the transition away from the last ad.
· Usability. Digital marketers require an agile solution that can easily merge with existing reporting systems to deliver the daily and weekly insights required for rapid-cycle optimization.
Long-term success will also depend on the organization tasked with delivery:
· Technology. Crunching terabytes of data daily is still an expensive proposition and one that requires significant investment in the latest systems and a highly specialized team to operate them.
· Scale. The technology investments outlined above require access to a large number of clients to generate the right price proposition through scale. This explains why individual agencies may struggle in taking their solution to market.
· Knowledge. Technology and scale will only drive the collection of data. To turn these terabytes of daily data into actionable insights will require investments in top notch econometrics focused teams with analytics prowess.
The last ad model has become entrenched in the industry by default and its longevity is predicated on the fact that no one has successfully commercialized a better solution. It is only a matter of time before the industry shifts to a new standard that delivers the accountability required for continued growth.
The Media Innovation Group (MIG) is looking provide that standard with our recently launched ZAP Attribution solution. In developing ZAP Attribution, we focused on the two major gaps mentioned above – lack of objectivity and lack of accountability.
ZAP Attribution is powered by an algorithm that determines the impact of each impression on conversion. ZAP Attribution is one hundred percent data-driven. There are no assumptions, and the model delivers fact-based, unbiased results. This in and of itself is an important step forward for the industry.
The attribution challenge goes beyond merely assigning credit beyond the last impression. The attribution algorithm should accurately predict the impact of an optimization decision on performance. We have benchmarked ZAP Attribution relative to the last ad model in terms of its predictive power and our solution outperforms by a wide margin. This means that optimization decisions based on ZAP Attribution metrics will actually deliver the intended gains in performance. It is this gain in predictive power that differentiates ZAP Attribution from other solutions on the market and will be the driving force behind its adoption.
Brian Lesser is SVP, General Manager of the Media Innovation Group (MIG), a unit of GroupM holding company WPP.
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