Turn on the television and you’ll be bombarded by pharma ads—all competing for your attention. But, like marketers in other industries, pharma struggles with measuring the effectiveness of their advertising. As a recent eMarketer report noted, two-thirds of U.S. marketing professionals said their company had increased its prioritization of marketing attribution in the past year. But while marketers are focusing more resources and attention on attribution, the same report noted that very few marketers are using what can be described as more advanced attribution practices.
The challenge is especially pronounced in pharma, where 9 out of the top 10 firms spend more on marketing than they do on R&D. Considering the relatively small target audience for most drugs, compared to the carpet-bomb approach of a television campaign, it’s clear that there’s room for improvement when it comes to attribution of pharma marketing dollars. Employing advanced attribution methods to measure advertising effectiveness cannot only reduce marketing costs, but it may also help to reduce the cost of pharmaceuticals.
The Technical Challenge
For many marketers, attribution has been about building a series of probabilistic models based on sampling. Without a transparent view into a plurality of household IDs, marketers are forced into a scenario in which they must project the results of their campaigns based on a sampling of records that do happen to match.
But using samples in attribution isn’t as accurate as a deterministic, or actual, match. In fact, any skew in the sampling process produces a less accurate model. Those inaccuracies are then amplified by the adjustments marketers make to optimize results. In effect, the old adage applies—garbage in, garbage out.
This Technical Challenge is Solvable
Solving the technical attribution challenge requires as many deterministic matches as possible. This has always been a challenge for marketers, but it’s become especially pronounced in a world where consumers easily move back and forth between linear and connected TV, mobile and desktop, social and email, digital and offline.
In order to generate enough deterministic matches to do meaningful attribution, marketers must correlate an increasingly broad range of signals, from customer postal addresses to device IDs, IP addresses, emails, and geo-location. In fact, embracing the general concept of location-based tactics such as GPS and geo-fencing promises to improve the scope and accuracy of omnichannel attribution, serving as a reality check for digital that a cookie never could.
But simply solving the technical match rate challenges are not enough to make attribution an effective part of the campaign workflow. Pharma marketers must also go one step further and change their mindset on how marketing works.
Making Attribution Part of the Pharma Marketing Workflow
Historically, attribution has been a product of a consultant’s report commissioned to evaluate the hard work of the marketing department. Such models can take months to execute, making just-in-time or workflow attribution impossible—a real liability when you’re trying to reach a highly specific audience of people who might be good candidates for a particular drug. Even worse, consultants are too often rewarded to produce attribution studies that reinforce client expectations. After all, there’s little incentive for a consultant to give a client bad news they can’t offer a solution for.
But new attribution methodologies that are built around a deterministic approach don’t require consultants to spend months building complex models. Instead, new deterministic attribution methodologies can, and should, be incorporated into the daily marketing workflow.
Once put into practice, deterministic attribution will empower pharma marketers to run their own experiments in something close to real time. A workflow that includes deterministic attribution makes it possible for pharma marketers to optimize media spend based on whether the last message reaches the right audience. They should be able to quickly pivot from email to display or to connected TV as the campaign runs, rather than resetting based on the findings of the previous quarter’s attribution report. And instead of A/B testing creative against probable matches, pharma marketers can test, refine, and adapt their messages based on insights from real audiences on the fly.
Needless to say, we’re talking about a revolutionary change inside of pharma marketing. On the one hand, pharma marketers will be able to make better, faster, and more data-driven decisions about their campaigns. But with a more optimized marketing spend, overhead and waste will be reduced. This savings may also contribute to addressing the rising cost of prescription drugs. There’s a strong case to be made for turning the reduction in marketing waste into lower prices (something that may very be mandated by government legislation). Alternatively, those saved dollars could be reinvested into the R&D budget.
Where to apply those saved dollars may not be up to the CMO, but like marketers in other industries, the mandate is increasingly about delivering a demonstrable return on investment. Bringing attribution up to the speed of digital seems like a pretty good first step.