How and Why ‘Less Is More’ With Martech Data on the Open Web

By Niv Sharoni, CTO & co-founder, Zoomd (TSXV:ZOMD)

As someone who has spent years working in advertising technology since the launch of the iPhone, I’ve seen the industry’s appetite for data grow.

After TV, print, and radio advertising, digital marketing would finally be accountable to marketers, thanks to click, engagement, and sales data. Marketers would know exactly which element of which ad delivered the sale, finally being able to tell John Wanamaker which half of his ad spend is being wasted.

As we all know, that’s not how the story ends. It’s much more complicated than it looked in the early years of digital and mobile marketing.

Even though we can’t predict with certainty which element of which ad on the open web will generate the conversion, data has made digital marketers smarter. And the intelligence that data enables created an insatiable appetite for data among digital marketers. With access to extensive data from open web campaigns and storage cheap, no one had a reason to say no. Marketers wanted access to every available data point for every single ad unit (Unlike in the walled gardens, where marketers relied to the aggregated reports provided by the platforms). Fortunately for CTOs like myself, the AI revolution only came recently. Marketers were limited by Excel and database technology of the 2010s regarding how much they could analyze the data

Marketing data management changed with GDPR

Europe’s General Data Protection Regulation (GDPR), which was passed in 2016 and took effect in 2018, was the wake-up call for the ad tech industry to take data governance seriously. Though GDPR does not specify a list of allowed data points, it’s intended to ensure that minimal data is used safely and fairly, and eliminated when it’s no longer in use.

Thanks to GDPR, and to support CCPA and the other state data and ad privacy legislation, the marketing industry should focus data collection and management policies on data minimization: only collecting the data that is absolutely needed, and disposing of it safely as soon as possible.  A lot of the data that many companies track and store isn’t necessary and has little value after the conclusion of the marketing campaign. Once a campaign is completed, martech vendors should run post-campaign analyses and then dispose of the identifiable data, including IP addresses, cookie identifiers, device IDs, and demographic, behavioral and intent data.

Though the various US state data privacy laws are all different, from a data governance perspective, unless something changes, most martech vendors should be able to implement the same data management practices to meet the regulatory requirements of all of the state laws.

Inside the walled gardens, the platform does most of the data governance work. On the open web, it work falls on the martech vendors and the advertisers. Whoever holds the data, carries the risk.

An effective way for marketers and martech vendors to solve the data challenges brought on by data privacy legislation is by analyzing and optimizing campaign performance in real-time. Therefore, there is less need to store user data after the conclusion of a campaign. By analyzing campaign performance at the conclusion of the campaign, and documenting the best practices learned from the campaign, future campaigns can be optimized based on the best practices without needing to save and store any user data. For example, no one needs any user data if the best practice from a given campaign is using video for a certain target group. Just having the best practice is sufficient for future campaign optimization as long as the process of documenting campaign best practices is rigorous.

What data is important?

With research from Singular showing that 75% of an ad’s ability to make a brand impression is due to creative, and that ad creative is responsible for as much as 49% of all sales lift, understanding and optimizing ad creative data is important for performance marketers. A few hours after a campaign goes live, the martech vendor team understands which creative units are performing best. Within a couple of days, the team understands which creative elements are driving conversions, and can optimize ad creative in accordance with the results. Once the performance patterns have been optimized, there is little value in saving specific click and engagement data. All a martech vendor really needs is to document, for example, that the 20-second video with the blue background and the 15% off call-to-action performed best, instead of saving all of the campaign engagement data. And even for martech vendors who want to save all of the campaign engagement data, who has the time and resources to re-analyze campaign data after the post-campaign analysis has been completed?

Beyond ad creative elements, marketing technology partners need to understand the relative performance value of the supply they are buying. This will provide more insights to better manage campaign performance than knowing if the ad is being viewed by a 19-year-old male college student from Michigan or a 37-year-old female accountant who is the mother of three kids from California.

In addition to creative performance, specific placement data also provide marketing value. How much one sees depends on which ad vehicles you’re buying. Some platforms provide comprehensive performance data down to the individual app or publisher. Others run a closed auction and show results with no supply breakdown at all.

While marketers have a greater need for user data to build relationships with their customers, they have justifiable reasons for collecting user data. If marketers focus on only keeping data that they absolutely need, and respecting user privacy, particularly for users under 18 and anything relevant to health/medical issues, then they can build strong customer relationships while remaining privacy-friendly and in-line with legislation.

For martech vendors, data minimization is the strategy that will keep clients safe and privacy-friendly.