We’ve all seen it: That pair of shoes you bought a week ago still shows up in ads as you browse the internet. The marketing teams representing agencies and their own brands work within the advertising ecosystem to present, then re-present and then re-present again those items you’ve previously expressed interest in, with no knowledge if you’ve bought them or not.
Why has no one yet been able to close the loop by attributing sales to the right channel and removing that ad from rotation?
Fundamentally, this problem remains unsolved because of all the various data silos that aren’t able to communicate with one another. The ad seen on TV can’t inform your phone or laptop that it’s also seen the ad, while the point-of-sale system or online checkout can’t notify those previous touch points to confirm the sale occurred.
Buried deep within these challenges is attribution: assigning value to the advertising strategies and tactics that resulted in the sale. The complexity is further intensified by the massive amount of digital browsing and shopping that people undertake, juxtaposed with the fact that 85-90% of all retail salesstill occur in a physical location. The net result is that proving offline to online attribution is ridiculously complex, with pure digital attribution being only slightly simpler. Finally, there’s no standard method or approach to communicate if items have been sold back into the ad tech ecosystem. There are trillions and trillions of data points created each day across e-commerce, point-of-sale systems and digital advertising, but there’s no way to synchronize these data sets to provide true closed-loop attribution.
Solving attribution will require more sharing of data across more companies. This will be no easy task because there’s more emphasis from the press, public and government to rein in data sharing, which has had limited legislative oversight thus far. Consumers also don’t truly understand what data is being collected and why, even after they’ve clicked “Accept” and opted in to share their data. As a result, there is a growing trend of proposed U.S. privacy regulations at both the federal and the state level, with many modeled off the European Union’s recently enacted Global Data Protection Regulation (GDPR).
At first glance, it would seem that legislation and attribution don’t seem to coexist. Wouldn’t legislation that reduces the amount of data flowing through a company’s systems inhibit the ability to demonstrate attribution? Speak with any legitimate player in the ad tech space, and I believe you’ll find full support for strong federal legislation that provides consumers with options and transparency as to how their data is used.
The nationwide approach prevents a patchwork of state-level policies that both consumers and companies would struggle to understand and implement. Regulation that provides greater transparency and choice to consumer data sharing will force companies to clearly outline the benefits of doing so. People will become more informed and in control of their data. As a result, they’ll share it only when they see corresponding value.
Unified legislation and empowered consumers will ultimately simplify the process of uniting these disparate data silos to demonstrate attribution. For this example in the advertising industry, if consumers clearly understand that their data can help them get better content and ads, keep the sites they visit free of charge, and ensure that items they’ve bought won’t be advertised to them anymore, there’s a clear value exchange here.
Location-data provider Teemo provides an early proof point. It worked with EU regulators to provide a better process to inform consumers about why they should opt in to data sharing, clearly stating the value, and have seen opt-in rates of 70%.
Ultimately, it’s not a lack of legislation that’s holding back attribution; rather, it’s the difficulty of synchronizing the data. However, federal legislation that provides a common framework that all can buy into and adopt will serve as the foundation for bridging the gap between today’s various data silos.
This post originally appear on Forbes.com