
Today, Experian announced a suite of next-generation solutions that will help marketers navigate the challenges of cookie deprecation. Powered by the Experian Graph, these solutions will enable marketers to maintain behavioral targeting at scale.
- In partnership with Audigent, Experian announced the early-stage limited availability of Experian Audiences inside the Privacy Sandbox through the Protected Audiences API.
- Experian has also co-developed, with Audigent, an AI-driven contextual targeting solution layered with Experian’s rich Experian Marketing Data to continue delivering marketers scale and performance from their campaigns.
- Finally, Experian continues to evolve its signal-agnostic Graph, including coverage for industry-leading universal IDs, and plans to support IPv6 and phone-based UID2s.
With these solutions, marketers can confidently deliver behavioral targeting after cookie deprecation and benefit from the power of Experian Marketing Data in their contextually targeted campaigns. As the industry prepares for ongoing signal loss and tightened privacy regulations, these solutions and further investments in Experian’s identity Graph ensure Experian continues to power data-driven advertising and achieves the needs of modern marketers: addressable advertising, cross-device targeting, and measurement.
Experian’s Graph allows marketers to target audiences in Privacy Sandbox via Audigent
Building off Audigent’s work with Privacy Sandbox, Experian and Audigent tested the scale of Experian audience data in Privacy Sandbox and found that over 15 days, they were able to match audiences to over 150M Chrome browsers in the US.
This solution – now in alpha – is powered by Experian’s Graph, leveraging an array of identifiers, including hashed emails and Hadron IDs. While the scale of targetable users and ad opportunities is still growing with the adoption of Privacy Sandbox by publishers and SSPs, the results are strong and provide a real-life illustration of how advertisers will be able to reach audiences in this new environment.
“As the industry’s leader in building Interest Group segments in PAAPI, Audigent is thrilled to see world-class data partners like Experian work with us to build innovative solutions that deliver value now and will be absolutely critical as third-party cookies are deprecated in 2025.”
DREW STEIN, FOUNDER AND CEO, AUDIGENT
Data-driven contextual targeting is available through partnerships with Audigent and Peer39
As marketers prepare for cookie deprecation, they are turning to tried and true methods of targeting, like contextual, as they offer targeting strategies based on content and behavior instead of user identity. Experian is co-developing ID-less solutions that upgrade contextual targeting by intelligently indexing and infusing Experian’s rich Experian Marketing Data against contextual signals. By using these products, advertisers gain the ability to reach their audiences with a new and improved solution that delivers scale, performance, and value.
We have beta launched a unique solution with Audigent that indexes Experian syndicated audiences against contextual signals through the power of the Experian Graph and Audigent’s Hadron ID to create PMPs that can be activated on any DSP. As part of the beta, a leading national advertiser ran a test via Audigent to see if this fully cookieless solution could deliver results at parity or better than today’s ID-based options. The scaled 15-day flight not only met existing campaign delivery targets but also exceeded CTR goals by 25%.
Experian has also partnered with Peer39 to make our geo-indexed syndicated audiences (e.g., Purchase Affinity and Demographic data) available through Peer39’s contextual integrations. This allows marketers to confidently reach the right audiences in their digital marketing campaigns without third-party cookies.
Experian’s Graph now includes leading Universal IDs
With the ever-changing nature of signal and identity, we’re continuing steps to be interoperable, and Experian’s signal-agnostic Graph now supports the leading universal IDs: UID2s, ID5 IDs, and Hadron IDs. This is in addition to hashed e-mails, mobile ad IDs, and Connected TV IDs. Our strong coverage against cookieless identifiers means marketers will maintain addressable advertising as the Graph continues resolving data back to consumers and households in a privacy-centric way. In addition to providing greater breadth and depth of signals to reach US consumers, Experian’s Graph is rebuilt weekly, which means our connections are highly accurate, refreshed, and addressable.
“Experian is a valued partner in Nexxen’s unified identity graph powering the Nexxen data platforms, which bring us the ability to seamlessly onboard client data, activate campaigns, and measure performance while maximizing biddable opportunities for our advertisers. They help ensure our clients can continue reaching audiences at scale and successfully execute campaigns.”
Chance Johnson, Chief Commercial Officer, Nexxen
Investments planned over the next year continue to ensure a Graph resilient to signal loss
As connected TV (CTV) viewing continues to dominate, the importance of being able to match to IPv6 increases. Later this year, we’ll add support for IPv6 in our Graph as well as phone-based UID2s. This is in addition to our current coverage of IPv4 and email-based UID2s. As a result, all IP signals and UID2s will be resolved back to Experian’s household and individual profiles and their associated devices, which means marketers and platforms can better understand the full customer journey and reach people across their devices.
Experian’s toolkit of cookieless solutions maintains addressability and ensures marketers can continue to do privacy-safe behavioral targeting at scale
As the industry braces for the challenges posed by signal loss and evolving regulation, the unparalleled breadth, depth, and stability of Experian’s Graph empowers our partners across the ad tech ecosystem to confidently achieve their objectives and navigate uncertainty.
What are you waiting for? Fill out the form to begin testing one of these cookieless solutions
About the author

Budi Tanzi, VP of Product and Solution Engineering, Experian Marketing Services
Budi Tanzi is the Vice President of Product at Experian Marketing Services, overseeing all Identity Products. Prior to joining Experian, Budi worked at various stakeholders of the ad-tech ecosystem, such as Tapad, Sizmek and StrikeAd. During his career, he held leadership roles in both Product Management and Solution Engineering. Budi has been living in New York for almost 11 years and enjoys being outdoors as well as sailing around NYC whenever possible.
Latest posts

The importance of affiliate marketing as a marketing channel is evident; it ranks as one of the most effective marketing channels for retailers, along with paid search and e-mail. 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. I recently worked with Rakuten LinkShare on a webinar which highlights how their affiliate marketing services partnered with Hitwise create a proven package for success by providing valuable and actionable insights to affiliate marketers in understanding and targeting key consumer segments. Identify sites sending traffic to your category For our case study, we examined a custom category of Rakuten LinkShare department store clients and compared them with a category of department store non-clients. Using Hitwise, we examined which publisher sites sent traffic to each of the categories in order to identify the best affiliates to partner with. Among the top 20 publisher websites, a number of fashion and style content websites were sources of traffic to LinkShare Department store clients. Fashion and trend focused affiliate sites, namely ShopStyle and Polyvore, pointed to clear fashion editorial interest amongst those who visited LinkShare department store clients. Consider search terms used to capture consumer interest and intent Next, we looked at generic terms that sent traffic to affiliate site ShopStyle. Terms included searches for products sold in department stores such as variations of “heels” and “dresses”. The data indicates that ShopStyle is a good candidate to partner with because it attracted visits from those who are interested in fashion, looking for a deal, and who are likely in-market for specific products. Monitor effectiveness of affiliate programs and make timely decisions Hitwise can also be used by marketers to evaluate the effectiveness of their affiliate partnerships. For this example, we were able to show that Rakuten LinkShare affiliates sent a larger share of traffic to department store clients versus non-clients, pointing to a clear benefit from affiliate partnerships. As affiliate marketing is an increasingly critical channel for marketers, the importance of selecting the best and most relevant publishers is clear. When used in conjunction with affiliate marketing programs, Hitwise enables marketers to understand competitors’ online distribution and sources of traffic, select the best affiliates to partner with, and quantify the return on investment from partnerships.

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.

Under the Patient Protection and Affordable Care Act that President Barack Obama signed into law in early 2010, healthcare providers are expanding their outreach to as many Americans as possible. In an effort to improve overall care, state and local healthcare agencies are performing health information exchanges (HIEs), electronically exchanging patient data. HIEs provide a new level of access to health information, but data quality needs to be of paramount importance. Patients’ medical records include contact information, such as mailing addresses, phone numbers and email addresses. Entering this data into forms is a process rife with opportunities for human error. Data fields are often riddled with incorrect formatting, typographical errors and contacts that are correct but outdated. Patients’ medical records must be corrected in order to ensure quality care. Several precautions must be taken before an HIE migration. Before outstanding paper records are digitally imported, records should be wiped clean of any mistakes and software tools should be used to verify addresses and eliminate duplicate records. Review this new HIE infographic to better understand the role data quality plays in HIE migrations.