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.
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Published in MediaPost With the explosion of smartphones and digital tablets and the steady rise of Internet-connected televisions, gaming consoles, and more, consumers are increasingly watching online video when and where they want. New research from Experian Marketing Services on cross-device video found that as of October 2013, 48% of all U.S. adults and 67% of those under the age of 35 watched online video during a typical week, up from 45% and 64%, respectively, just six months earlier. At the same time, the share of households considered “cord-cutters” — those with high speed Internet but no cable or satellite TV — is on the rise, and that has a real impact on marketers and on the medium of television, the recipient of the largest share of advertising dollars. While the growing trend in cord-cutting is understandably disturbing to cable and satellite companies and disruptive to the television advertising revenue model overall, the growth in online viewing creates opportunities for marketers. Online video viewers can be more easily targeted and served up advertising that is more relevant, responsive and measureable. Marketers can also be more confident that their online ad was actually seen given that viewers are typically unable to skip ads. And while CPMs for online video ads may generally be lower than those of TV, marketers can use that savings to negotiate costs based on clicks or transactions rather than impressions, giving them a better picture into audience interest and insights to inform their budget allocation. Expect “Cutting the cord” to continue Today, over 7.6 million U.S. homes or 6.5% of households are cord-cutters, up from 5.1 million in 2010 or 4.5% of households. One thing enabling consumers to cut the cord is the rise in Internet-connected TVs, which allows viewing of Internet video on demand without sacrificing screen size. In fact, a third of adults (34%) now have at least one TV in the home that is connected to the Internet either directly or through a separate device like an Apple TV or Roku, up from 25% in 2012. With the launch of devices like Google’s Chromecast and the Amazon Fire TV, those numbers are sure to rise even more in the months and years ahead. Cord-cutters like the bigger screen Our analysis found that the act of watching streaming or downloaded video on any device is connected to higher rates of cord-cutting but the act of watching on a television is the most highly correlated. In fact, adults who watch online video on a television are 3.2 times more likely than average to be cord-cutters. Those who watch video on their phone (the device identified in the analysis as that most commonly used for watching online video) are just 50% more likely to be cord-cutters. Millennials are more likely to be cord-cutters We found that households with an adult under the age of 35 are almost twice as likely to be cord-cutters. Throw a Netflix or Hulu account into the mix and the rate of cord-cutting among young adult households jumps to nearly one-in-four. Given these surprising stats, many Millennials may be cord-cutters without ever having “cut” a cord. And that’s an important trend to watch since it means a significant portion of this generation will never pay for TV. Millennials are also the most device-agnostic, with over a third saying they don’t mind watching video on a portable device even if it means a smaller screen. That’s more than double the rate of those ages 35 and older. This decentralized viewing can create headaches for marketers who need to start a relationship with Millennials during this stage of their lives when they’re most open to trying out new brands and have yet to settle down. On the plus side, marketers who do manage to reach this audience will find them much more open to advertising than average. In fact, Millennials are more than four times more likely to say that video ads that they view on their cell phone are useful. So while the challenge is big, so is the potential reward.
Published in AdExchanger. “Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. Today’s column is written by Tom Manvydas, vice president of advertising strategy and solutions at Experian Marketing Services. The proliferation of connected electronics has spurred new interest in device-recognition technologies even though they have been in use since the 1990s. As we enter the “Internet of Things” era, device recognition will significantly impact the ad tech ecosystem. Many network advertising technologies are becoming obsolete as cookie blocking grows and the Internet becomes more mobile and device-centric. Device recognition will be yet another technology challenge for marketers but has the potential to overcome many key tracking, measurement and privacy issues with which data-driven marketers have struggled. By leveraging device recognition technologies, marketers can protect their investments in Web 2.0 ad tech, like multitouch attribution, and improve their overall digital marketing programs. Device Recognition Vs. Cookies Device recognition attempts to assign uniqueness to connected devices. By focusing on the device, you are able to “bridge” between browsers and apps, desktop to mobile and across OS platforms like iOS and Android. Device-recognition IDs function like desktop cookies for devices but with four important differences: 1. Coverage: Device-recognition methods are largely immune from cookie limitations. About half of mobile engagements on the Web do not involve cookies, while third-party blocking impacts up to 40% of desktop engagements. 2. Persistency: Device-recognition IDs can be more persistent and less fragmented than most desktop cookies. For example, Apple’s UDID or Android ID are permanent, and network node IDs like MAC addresses are near-permanent. Proxy IDs such as IDFA are persistent but can be updated by the device owner or ID provider. 3. Uniqueness: Devices are unique and cookies are fragmented. The digital media industry incurs substantial overhead cost and loss of efficiency when dealing with fragmented profiles and obsolete data caused by cookie churn. However, device-recognition methods are limited in their ability to recognize multiple profiles on shared devices. 4. Universality: Device-recognition technologies are universal and generally work across devices and networks. However, interoperability issues across device operating systems, such as iOS and Android, can limit the universal concept. There are many types of device-recognition technologies but two basic approaches to device recognition: deterministic and probabilistic, each with their pros and cons. Deterministic Approach: Accurate And Persistent But Complicated Deterministic device recognition primarily uses the collection of various IDs. While the mobile developer is familiar with the variety of IDs, it’s important that marketers become better-versed in this area. Examples include hardware IDs (including serial numbers), software-based device IDs (such as Apple’s UDID or the Android ID), digital data packet postal codes or proxy IDs (such as MAC addresses for WiFi or Bluetooth, IDFA for both iOS and Android and open-source IDs). Deterministic methods improve the accuracy of tracking, targeting and measurement over current cookie-based methods. They can improve the ability to more persistently manage consumer opt-outs. But the proliferation of device types limits the universality of deterministic device recognition. Without uniform standards across platforms, marketers need to account for multiple ID types. Also, deterministic device-recognition methods are not well developed for desktop marketing applications. The lack of interoperability across deterministic device IDs makes execution too complicated. Deterministic device IDs were meant for well-intentioned uses, such as tracking the carrier billing for a device. However, they present privacy and data rights challenges, leading to blocking or limited access by companies that control IDs. Probabilistic Device Recognition: A ‘Goldilocks’ Solution Probabilistic device recognition may be the ideal solution for a connected world that does not rely on cookies nor wants to use overly intrusive deterministic device recognition. Probabilistic device recognition is not a replacement for deterministic IDs. Instead, it complements their function and provides coverage when they are not available. The probabilistic approach is based on a statistical probability of uniqueness for any single device profile. This approach creates a unique profile based on a large number of common parameters, such as screen resolution, device type and operating system. This process can uniquely identify a device profile with 60% to 90% accuracy, compared to 20% to 85% accuracy for cookie-based identification methods. Probabilistic IDs are more persistent than cookies with better coverage, but less persistent than deterministic device IDs. The natural evolution of the device takes place over time and prevents persistent identification. Probabilistic device recognition can be universal and is not impacted by interoperability issues across platforms — the technology used to generate a probabilistic ID on one network can be the same technology on another network. Unlike some deterministic device recognition approaches, there is no device fingerprinting. Probabilistic device recognition accurately identifies profiles in aggregate, rather than a single device. That’s the inherent beauty of probabilistic device recognition: It can generate more accurate targeting results than cookie-based methods without explicitly identifying single devices. This is more than good enough for most marketers and significantly better than what’s available today. Another benefit is the absence of any residue on the device — no cookie files, flash files or hidden markers. Probabilistic methods can work on devices that block third-party cookies or connect to the Web without using any cookies. For example, you might have a hard-to-reach but valuable audience segment. Probabilistic device recognition could effectively increase your reach on this segment by 40% to 50% and increase the overall targeting accuracy by two times. Let’s say the actual population for this segment is 100,000 members. The typical cookie-based approach might reach 28,000 members but the typical probabilistic device-recognition approach could reach 65,000 members. A Decline In Hardware Entropy If you take a close look at the emitted data from today’s devices, it is not easy to analyze it for device identification. That’s because the data footprint of one device looks a lot like another. Device recognition augmentation methods can address this, such as device usage profiles, geo location clustering, cross-device/screen analytics or ID linkage for first-party data owners. In the short term, device-recognition technologies, particularly probabilistic methods, can greatly improve today’s digital marketing programs. Marketers should become fluent in their use cases and benefits. If 2013 was the year of mobile, I think we’ll see a surge in marketing applications based on device-recognition technologies in 2014. Follow Experian Marketing Services (@ExperianMkt) and AdExchanger (@adexchanger) on Twitter.
According to Experian Marketing Services’ 2014 Digital Marketer: Benchmark and Trend Report, social media Websites are playing an increasingly important role in driving traffic to other Websites, including retail sites and even other social networking sites, at the expense of search engines and portal pages. For instance, as of March 2014, social media sites account for 7.72 percent of all traffic to retail Websites, up from 6.59 percent in March 2013. Further, Pinterest, more than Facebook or YouTube, is supplying the greatest percentage of downstream traffic to retail sites. According to the Digital Marketer Report, more retailers are directing their customers to social media within their email campaigns. In fact, 96 percent of marketers now promote social media in their emails, and it shows. In 2013, for instance, email Websites generated 18 percent more clicks to social networking pages than the year prior. Social drives more traffic to other social Websites Social media Websites are driving more and more traffic to other social sites. In 2013, 15.1 percent of clicks to social networking and forum sites came from other social networking sites, up from a 12.5 percent click share reported in 2012. Despite driving the greatest share of traffic to social networking sites with 39.1 percent of clicks, search engines’ share of upstream traffic to social declined a relative 13 percent year-over-year. Among the other top referring industries to social, only the portal front pages industry — which includes sites like Yahoo!, MSN and AOL and is closely affiliated with search engines — showed a drop in upstream click share providing further evidence that increasingly all (or most) roads lead to social. To learn more about key trends in social media traffic, including downstream traffic from social sites and the share of consumers accessing social media across multiple channels, download the free 2014 Digital Marketer: Benchmark and Trend Report.