Ramzi Nasr: Multi-Party Clean Rooms at the Snowflake Summit

Clean rooms may be the latest buzzword in AdTech, and for good reason. VideoAmp’s Ramzi Nasr, VP of Engineering, joined Tyler Craven of NBCUniversal at the Snowflake Summit to discuss an innovative clean room approach that speaks to the scale and flexibility of the framework. But to understand why data clean rooms are an essential part of the future of advertising measurement, we must first understand accurate attribution and its many challenges.

Attribution is the process of determining the value of each touchpoint in a customer journey. It helps marketers understand which marketing channels are most effective in driving conversions. The accuracy of attribution models depends on the quality of the data used, and high-quality data is essential for accurate attribution.

There are a few challenges in measuring the effectiveness of advertising campaigns: data availability, and data privacy. Most measurement companies address these challenges in the following ways:

• Small panels: This approach involves collecting data from a small group of people, and then using that data to apply the results to a larger population. As we’ve all learned, small sample size does not equal accurate representation. 
• Differential privacy: This approach involves adding noise to the data, so that it is not possible to identify individual people. However, this approach can also be inaccurate, as the noise can obscure the true relationships between different data points.

So, why aren’t all clean rooms the same? The above responses to measuring advertising campaigns leave a lot of room for error. With data clean rooms, (TL;DR: secure environments where data from different companies can be shared without compromising privacy), the outcome is more accurate attribution modeling. VideoAmp’s approach utilizes first touch, last touch, even  touch and data driven attribution methodologies and creates a multi-party clean room unlike anything else in the industry.  More than that, our data clean room approach offers:

• Increased accuracy of attribution models
• Reduced privacy concerns
• Increased collaboration between different companies
• Improved decision-making

We’re proud to be at the forefront of these innovations, enabling cross-platform measurement without sacrificing accuracy and data privacy.