After a six-month beta period, collaboration in Snowflake Data Clean Rooms using Experian’s offline or digital graph is now generally available for all clients. As part of this, Experian is excited to announce that Experian’s identity graph will be integrated into Snowflake’s Data Clean Rooms. With the growing importance of data privacy and marketing efficiency, this partnership builds off of Experian’s previously-announced integration into Snowflake’s AI Data Cloud for Media.
Adding Experian’s identity graph to Snowflake Data Clean Rooms helps advertisers, advertising platforms, and measurement partners work more effectively. Built upon Experian’s rich offline and digital identity foundation, with support for various identifiers across platforms, collaboration in Snowflake Data Clean Rooms helps clients maximize the value of their data and meet the diverse needs of modern business:
- Collaborate with partners for richer data insights
- Achieve higher match rates
- Improve audience building
- Produce more accurate and complete reports
- Ensure data privacy
- Seamless integration of AdTech and MarTech platforms
Regardless of the identifier type you are looking to collaborate on, Experian has the identity data in Snowflake Data Clean Rooms to support you and your partner. This leads to higher match rates and more resolved data for you to use to benefit your media initiatives.
“Integrating Experian’s identity graph into Snowflake Data Clean Rooms marks a transformative leap for digital marketing. This collaboration empowers advertisers, programmatic platforms, and measurement partners with unparalleled accuracy, privacy, and efficiency. Together, we are excited to provide innovative solutions to meet the evolving needs of our clients.”
Kamakshi Sivaramakrishnan, Head of Data Clean Rooms at Snowflake
The Experian and Snowflake partnership showcases how collaboration can enhance scalability and cost-efficiency. Data clean rooms provide a secure environment where multiple parties can share, join, and analyze their data assets without leaving the clean room or exposing the underlying data. By integrating Experian’s identity graph within Snowflake’s secure platform businesses of all sizes can receive advanced data collaboration and identity tools without the high costs usually involved.
The integration prioritizes consumer privacy and data security. Backed by Experian’s Global Data Principles, Experian’s deep roots in data protection and security provide customers with the most trusted way to share data and protect consumer privacy. With Experian’s graph in Snowflake Data Clean Rooms, customers will get a solution that respects customer consent, safeguards sensitive data, and ensures that processing occurs with the utmost respect for user confidentiality and preferences.
Further, Snowflake Data Clean Rooms uses advanced methods to preserve privacy, such as differential privacy and secure computations on encrypted data, enabling data security and integrity. Together, these methods prevent unauthorized access by keeping sensitive data within the secure confines of the cleanroom on a strict, collaboration-to-collaboration basis.
The collaboration between Experian and Snowflake significantly enhances data matching and identity resolution within the Snowflake Data Cleanroom. Experian’s identity solution uses digital identifiers like hashed emails, MAIDs, and CTV IDs and offline identifiers like name and address. This allows advertisers to reach more consumers and enrich their data. Marketers can easily use their first-party data in the cleanroom, and with Experian’s Graph, they get higher match rates for more accurate targeting and campaign measurement.
The continued partnership between Snowflake and Experian provide advertisers, platforms, and measurement providers a secure and effective way to collaborate. This sets the stage for continued innovation in programmatic advertising, ensuring that our solutions evolve in step with our clients’ needs.
If you’re not utilizing clean rooms for collaboration but have advanced identity needs, you can license our Graph and seamlessly integrate it into your Snowflake account.
Latest posts
While effective affiliate marketing relies on two groups, the publishers (affiliates) who display advertisements online and the advertisers (merchants) who aim to increase sales for their online shop, incorporating insights from Experian Marketing Services’ Hitwise can strengthen affiliate programs.
New data from Experian Marketing Services’ Simmons® ConnectSM mobile and digital panel sheds light on the way smartphone users spend time using their phone, with the average adult clocking 58 minutes daily on their device. On average, smartphone owners devote 26% of the time they spend on their phone talking and another 20% texting. Social networking eats up 16% of smartphone time while browsing the mobile web accounts for 14% of time spent. Emailing and playing games account for roughly 9% and 8% of daily smartphone time, respectively, while use of the phone’s camera and GPS each take up another 2% of our smartphone day. *Activities include use of a smartphone’s native features dedicated to each activity as well as downloaded apps whose primary function falls under the given activity. For instance, “watch video” includes the act of watching video on the smartphone’s native video player as well as use of video apps such as YouTube, Netflix, etc. iPhone versus Android users Smartphone users may constantly debate which operating system is supreme, but we see clear differences between the ways consumers use their phone depending on the operating system that runs it. For starters, iPhone users spend an hour and fifteen minutes using their phones per day, a full 26 minutes more than the typical Android phone owner. Additionally, iPhone and Android smartphone owners use their phones in markedly different ways. For instance, 28% of the time that Android users spend using their phones is dedicated to talking, whereas iPhone users spend only 22% of their smartphone time talking on the device. Android owners also devote a greater share of time visiting websites on their phone than iPhone owners. On the other hand, iPhone owners spend a disproportionately greater share of smartphone time than Android owners texting, emailing, using the camera and social networking. Note on time spent It may surprise some to read that an activity like watching video accounts for such a small share (less than 1%) of the typical adult’s daily smartphone use. However, for the charts above to sum to a single daily total it was necessary to calculate individual activity contribution using a base of all smartphone owners, including those who don’t spend any time engaging in a given activity during a typical day. The chart below provides additional insights into the time spent engaging in the major smartphone activities examining only those individuals who engaged in each activity during a 24-hour period. I’ve also added into the chart a reach and frequency metric to indicate the popularity of each activity and the number of times per day that individuals engage in them. In the chart, the activities with the largest bubbles are those in which the greatest share of smartphone owners engage during a typical day and include the usual suspects: talking (79%), texting (76%), visiting websites (62%), emailing (61%) and social networking (52%). Activities with the fewest daily participants are: watching video, which 2.3% of smartphone owners do during a typical day, and reading, which just 0.5% of smartphone owners do daily. Given that nearly 98% of smartphone users don’t watch videos on their phone during a typical day, it’s easier to understand why video comprises such a low share of the average adult’s daily smartphone use. However, the chart above reveals that those who do watch video on their phone spend, on average, 5 minutes a day watching videos spread out over 4.2 different viewing sessions. For more information on consumers’ usage of smartphones, digital tablets, computers and other traditional and digital media platforms, check out Simmons Connect.
New health information exchanges are prompting data quality efforts, as state and local healthcare agencies look to electronically share patient data.