Are you protecting consumer privacy in the AI era?   

Editor’s note: Chris Comstock is chief growth officer at software development firm Claravine.  

Advertisers have no shortage of screens, channels and ad formats for reaching the audiences they care about. But compiling a set of screens designed to reach specific audiences can be a fragmented process that yields complexities for today’s advertisers. And when audience data is involved, the complexities are even greater.

And, with an emphasis on utilizing artificial intelligence (AI), there is even more reason to focus on quality data that is accurate, complete and consistent.

16% of U.S. advertisers were very confident in their organization's ability to tie campaign performance or ROI back to specific data sets, and even fewer (13%) in their ability to tie results back to specific ad creative.

A lack of centralized control around data leaves companies at risk from a consumer privacy protection standpoint and makes it harder to determine the return on investment (ROI). Unsurprisingly, research from Advertiser Perceptions (registration required), commissioned by Claravine in September 2023, found that only 16% of U.S. advertisers were very confident in their organization's ability to tie campaign performance or ROI back to specific data sets, and even fewer (13%) in their ability to tie results back to specific ad creative. And since generative AI usage is increasing the number of creative assets, organized data is a marketing necessity in 2024.

Consumer data privacy is a priority

Companies must prioritize quality data now for a variety of reasons, with protecting consumer privacy being chief among them. Organizations with well-structured, high-quality data are able to more successfully employ privacy-forward methods for data sharing and analysis when compared to industry counterparts. In the same survey, a large majority of respondents (91%) agreed that putting privacy at the forefront is a must for organizations to move forward in competitive, privacy-centric ways.

Data standards go hand-in-hand with new and more effective, privacy-forward ways of sharing and utilizing data. For example, Advertiser Perceptions also found that advertisers with some form of data standards in place are significantly more likely to be leveraging the capabilities of a data clean room. And of those who are using clean rooms, 92% agree it's the new standard for privacy-compliant data matching.

Data-driven brands and marketers are increasingly relying on data clean rooms to respect consumer privacy and comply with regulations. However, without addressing the “messy data” problem, the expectations of clean rooms will not be realized. Standardizing and applying the right data naming conventions and taxonomies are how marketers can make the most use of their data sets and utilize them in the best way possible in data clean room environments.

Generative AI means more data

According to Advertiser Perceptions, two in five advertisers are currently using generative AI and another one in two are considering implementing it. As AI-generated content continues to skyrocket in 2024, it will leave a bottleneck as the still human-powered teams take on the burden of sifting through larger amounts of data to find a specific asset. More specifically, marketing analytics, data operations and advertising operations teams are being tasked with an increased need to categorize and standardize associated data, which is compounded by the accelerated pace of content production.

Marketing teams can’t always keep up with the creatives, which causes incorrect ads being served and money being left on the table.

Advertisers estimate that one in every four ad creatives are served to the incorrect consumer. This presents a huge opportunity for improving the process and gaining greater visibility into ad creative production, storage and utilization, especially as the rate of production continues to ramp up. In fact, research suggests that serving ad creatives to the correct consumer every time would raise ROI for advertisers by an average of 29%.

The use of generative AI means more data is created and needs to be tracked accordingly.

Will poor data quality lead to brand safety issues?

Brand safety is a big industry topic and should be approached as a priority. For half of U.S. advertisers, it can take a day or more to identify that an ad ran next to unsuitable content, leaving a big window of time for ads to be seen and leave lasting impressions on consumers.

Companies have experienced the impacts of these situations, with 18% of U.S. advertisers reporting significant cost impacts to revenue and overall flow of business after experiencing unfavorable associations with content. Ops teams reported the ability to identify where ad creatives were served and whether ads ran next to unsafe brand content much faster than their counterparts. Data standardization is one way that brands can have better control of their ad placements and the overall suitability of how they show up. 

Research indicates that higher quality and better organized data sits at the center of many top challenges, including privacy compliance, generative AI usage and brand safety. The industry winners of the near future will be those willing to put an emphasis on these things, with standardization of data being at the forefront. Companies that update their current value proposition will drive industry growth and be set up for continued success.


Advertiser Perceptions surveyed 140 U.S. advertisers in September 2023 to understand their current and future practices regarding data standards practices. To qualify for the study, respondents had to be spending $50 million annually on digital advertising and involved in decision-making for digital advertising. The study surveyed a mix of respondents across strategic teams, operations and data analytics teams. Agency respondents comprised 30% of respondents, while marketers accounted for 70%.