Data collaboration is foundational to the future of our business. That goes for us here at Gigi and across broader ad tech. When building products to enable data collaboration for our customers, we seek to address three central tenets: 1) maximizing privacy and security, 2) minimizing technical and operational friction, and 3) capturing as many rich signals as possible to enable more effective targeting and measurement.

Our first iteration of the Gigi 1P platform sought to do this for brands by allowing them to authenticate their DTC storefront and collaborate all their transactional data with Amazon ads via the AMC Uploader. This went well. Over the past 6 months, we’ve helped dozens of brands build AMC lookalike audiences using 1P and Amazon signals to deterministically measure outcomes after being exposed to an STV ad across both Amazon and 1P sources. This week, we launched a new set of tools for data collaboration built on AWS Clean Rooms. By doing so, we’re betting our core data collaboration infrastructure on AWS Clean Rooms. Here are the top reasons why we made this choice and why brands should care:

1. AWS Clean Rooms is the most private and secure way to collaborate data with Amazon

   There are now four ways to collaborate first-party data with Amazon ads, all with varying degrees of complexity and security: a) the Amazon Ad Tag, b) Conversions API (CAPI), c) AMC Uploader, and d) AWS Clean Rooms. With the first three options, brands must bring their data “in” to Amazon. This is either done directly via the Amazon DSP or AMC. Bringing data “in” means data needs to move from a brand’s internal data environment into Amazon’s. With AWS Clean Rooms, brands do not need to bring data “in” to AMC; rather, a set of queries can be built to collaborate with Amazon Ads without moving data outside of one’s own data environment. This is also known as data isolation, the most private and secure way to collaborate data with Amazon Ads. This isn’t to say the other means of data collaboration are not secure or fraught with risk. We’re just saying that using AWS Clean Rooms is the most secure way (compared to all other options) to collaborate data with Amazon ads. And we’re thrilled to announce that - as of this week - AWS Clean Rooms is the only way Gigi customers collaborate their data with Amazon Ads.

   
   

2. AWS Clean Rooms bring an interoperable infrastructure for brands to collaborate with other publishers and ad platforms

   As data collaboration becomes increasingly central to marketers, keeping up with each ad platform’s unique tools and mechanisms will be challenging. There will always be another Conversions API or ad tag. Marketers need to invest in an infrastructure that will be interoperable with the various places they allocate their advertising budgets and attention. Building bespoke connections for each ad platform won’t make sense and creates duplicative work. That’s why we’re so bullish on AWS Clean Rooms: it provides marketers with an interoperable infrastructure to collaborate data with other publishers and ad platforms. It is not limited solely to Amazon Ads. Since its inception, AWS Clean Rooms has done a magnificent job of building supply partnerships. Partners like TikTok, and most TV publishers already enable collaboration with AWS Clean Rooms. We are excited to work with our customers to help them expand their data collaboration strategy beyond Amazon Ads.

   
   

3. AWS Clean Rooms allows brands to improve match rates for ad targeting and measurement

   Match rates are the primary metric to measure the fidelity of a data collaboration. They measure how well your audience list connects with the advertising platform’s customer list. A higher match rate means your ads can reach more people you intended, and the greater the insights one can extract from bringing the two data sets together. Since our launch, we’ve seen an average match rate of 49.2%. Across broader ad tech, this would be considered quite good. However, match rates can be improved by using AWS Entity Resolution when using AWS Clean Rooms for data collaboration. With AWS Entity Resolution, brands can enrich and deduplicate their own first-party data using AWS’s built-in machine learning models. Additionally, brands who are customers of 3rd party ID partners like LiveRamp, Transunion, and UID2 can use the signals from these ID partners to enrich their 1P datasets prior to the collaboration with Amazon Ads. Through some initial tests, we are already seeing improved match rates across our customer base and are excited to roll this out across all of our customers.

   
   

4. AWS Clean Rooms will allow brands to build custom lookalike models within AMC

   A little over a year ago, Amazon Ads introduced the ability to build lookalike audiences with Amazon Marketing Cloud for their Amazon DSP campaigns. Brands could create a pool of their highest spending customers over the past year and ask Amazon to find 5-10 million people who look like them. But how Amazon finds those 5-10 million people has been a black box. None of us have known what signals they use to build their lookalike models. With AWS Clean Rooms, brands can soon build custom lookalike models using any signal available within Amazon Marketing Cloud. This not only improves the transparency of how lookalike models are built but also allows brands to optimize their audiences in-flight based on signals they’re receiving from their advertising campaigns. We’ll follow the evolution of this future product release and prioritize working on this in the coming months.

We are confident that building our core data collaboration infrastructure for AWS Clean Rooms will improve our customers' advertising outcomes. Please contact us if you’d like to begin using AWS Clean Rooms to collaborate data with Amazon Ads.