We’re excited to introduce the next segment in our Q&A series, Ask the Expert! Ask the Expert features a series of conversations with product experts where we dive into the areas you care most about like identity resolution, targeting, attribution, and more. Our next segment features a conversation about sell-side targeting.
Mike Chowla, SVP of Product at OpenX joins us to chat with Experian’s SVP of Sales & Partnerships, Chris Feo. OpenX is the world’s leading sell-side platform for audience, data, and identity targeting. In their conversation, Mike and Chris review:
- The shift to targeting on the sell-side
- How first- and third-party data are being used on the sell-side
- How OpenX is thinking about alternative IDs
What is sell-side targeting?
Sell-side targeting optimizes the way buyers and supply-side platforms (SSPs) work together. This approach moves the responsibility of inventory and audience targeting from the demand-side platform (DSP) into the SSP, providing advertisers with increased reach and better performance.
With sell-side targeting, locating your target audience becomes easier as you have a more direct connection with publishers. This increases your ability to scale against a target audience. Specifically, the SSP directly matches the buyer’s audience or data segment to the publisher inventory and audience and automatically sends the impression to the buyer’s DSP of choice via a deal ID, providing advertisers with improved reach and performance metrics as well as control over their inventory. With more direct access, your budget can likely go further, and you can decrease your effective cost per mille (eCPM) and get more working media.
“Supply-side targeting is the next phase of how supply path optimization (SPO) and buyers will need to work more closely with SSPs.” – Mike Chowla, SVP, Product, OpenX
Buying on the sell-side vs. open exchange
When buying on the open exchange, you have access to a vast number of impressions. With sell-side targeting, you can apply your campaign targeting directly on the supply-side and activate those impressions through a deal ID. Sell-side targeting works across various formats including web display, mobile, in-app, and connected TV (CTV) for a seamless advertising experience.
OpenX offers the unique capability to match users using their device graph within their SSP. This means you can target users from traditional data sources such as cookies or mobile ad IDs (MAIDs) and reach them in CTV or app environments. This gives you even more reach and precision in your advertising efforts.
The role of first- and third-party data on the sell-side
Buyers are showing a keen interest in bringing their own first-party data into the process of sell-side targeting. Meanwhile, certain agencies have been actively involved in working with identity and data. OpenX is currently collaborating with several agency ID solutions such as Choreograph, Merkel, and Horizon.
Buyers are also purchasing third-party data and data segments from various providers through OpenX’s platform for sell-side targeting purposes. By utilizing this data on the supply side, buyers are able to increase the match rate against their first- and third-party data segments in all environments. This ultimately maximizes scale against these audiences and drives a more efficient CPM due to eliminating waste.
Measurement and attribution on the sell-side
In the current state of SSP advertising, there is more of an emphasis on targeting capabilities than measurement and attribution. That said, SSPs can provide granular log level reports that can be utilized for multi-touch attribution (MTA) or mixed media models (MMM). These granular insights not only inform measurement and attribution models, but they also provide valuable optimization insights such as clearing price.
Additionally, advertisers have all of the same reporting options that they’re used to getting through their DSP because their buys are activated via deal ID in the DSP of their choice.
What to consider when transitioning to sell-side targeting
There are two primary items you should consider when transitioning to sell-side targeting:
- Supply
- Reach
Reach
Collaborating with partners who have the right capabilities can greatly improve reach and audience extension across different devices. For instance, if you bring your first-party audience or a third-party audience and are identifying that consumer via a cookie or MAID, being able to extend that targeting segment to other devices and platforms can be highly beneficial.
Supply
It’s crucial to collaborate with partners who have the right access to supply and direct connections with publishers. While targeting is essential, it’s equally important to have high-quality supply to drive performance.
Reaching consumers in a cookieless future
Whether you’re targeting on the demand or sell-side, it always starts with the consumer and who you’re trying to reach.
Significant changes in the consumer privacy landscape are impacting advertisers’ ability to access various signals emitted by consumers through their devices and browsers. Recent developments from Apple and Google have further amplified this situation.
Alternative IDs as a solution to signal loss
In response, we’re seeing the emergence of alternative IDs like UID2, Ramp ID, and ID5. OpenX supports these types of IDs and considers them crucial for audience buying in a privacy-centric cookie-less future.
We are still in the early stages of this evolution. While some of the IDs have good coverage, cookies will continue to be the primary targeting method as long as they remain available.
Nevertheless, we see alternative IDs as one of several solutions that will become increasingly important as third-party cookies disappear. Contextual buying will also emerge, and a set of solutions will come together to enable advertisers to keep finding their audience in a cookie-less world.
Overcoming signal loss with identity resolution
Looking ahead, as we continue to lose signals due to the evolving consumer privacy landscape, we will witness two things:
- Continued fragmentation
- A wide variety of identifiers
Content will continue to be available on various devices. We’re currently experiencing the emergence of connected TV, but who knows what other devices will surface over the next five to ten years. As cookies disappear, which have been the primary identifier, and alternative IDs are introduced, the wide variety of identifiers will create further fragmentation. This highlights the need for identity in the future.
Identity resolution at Experian matches fragmented identifiers to a single profile to create a unified, cross-channel view of your consumers. Our identity resolution solutions can help future-proof your marketing strategies.
How Experian and OpenX work together
Experian is a key player in OpenX’s OpenAudience solution and helps to power many of their data segments as well as their identity graph. While OpenX collaborates with a variety of providers and operates a fully interoperable platform, Experian remains valuable to the core technology within OpenX’s SSP.
“Experian powers a lot of the data segments and identity graph that OpenX has in our OpenAudience capabilities as part of our SSP.” – Mike Chowla, SVP, Product, OpenX
Watch the full Q&A
Visit our Ask the Expert content hub to watch Mike and Chris’s full conversation on sell-side targeting. In the Q&A, Mike and Chris also share their thoughts on the impact artificial intelligence (AI) will have on the AdTech industry and their go-to sources for staying up to date on all things AdTech.
About our experts
Mike Chowla, SVP, Product, OpenX
Mike Chowla is the SVP of Product at OpenX where he leads product development and innovation, from customer discovery and user research to the development, delivery, and support of a market-leading product suite. Chowla holds a BS in Engineering from the University of Southern California, and an MBA from The University of Pennsylvania.
Chris Feo, Chief Business Officer, Experian
As SVP of Sales & Partnerships, Chris has over a decade of experience across identity, data, and programmatic. Chris joined Experian during the Tapad acquisition in November 2020. He joined Tapad with less than 10 employees and has been part of the executive team through both the Telenor and Experian acquisitions. He’s an active advisor, board member, and investor within the AdTech ecosystem. Outside of work, he’s a die-hard golfer, frequent traveler, and husband to his wife, two dogs, and two goats!
Latest posts
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.
Mother’s Day may not exactly be right around the corner, but the time to send your Mother’s Day emails sure is! Based on our analysis of 186 brands that sent Mother’s Day mailings in 2013, 75 percent of email volume and 80 percent of email-generated revenue occurred between May 1st and Mother’s Day (May 12, 2013). The highest revenue-producing days were five days before the holiday (Wednesday, May 8, 2013) and Mother’s Day itself. This year, the sentimental holiday falls one day sooner than last year (May 11), but you still have more than enough time consider these quick tips for easy wins while planning and executing your campaigns. Tip 1: Give them what they’re searching for Last year, online searches five weeks before Mother’s Day were dominated by searches for the date of the holiday. As such, we recommend including the date of Mother’s Day in your email subject lines, particularly those early in the season, when customers are searching online for, and opening emails with, that information. Tip 2: Set the tone early with your subject lines A sample of early season subject lines that outperformed the overall unique open rate included: Remember Mom on Mother’s Day, May 12 Get a head start on Mother’s Day (plus a gift for you) Just arrived: Mother’s Day Gift Sets To Mother, With Love Tip 3: When it comes to timing, it’s the thought that counts Think through the timing of your emails depending on order delivery deadlines. On May 8th of last year, the largest revenue producers for email were orders for flowers and gifts placed in time to be delivered by Mother’s Day. Email subject lines on May 8th included reminders of the delivery deadlines: Last Chance: Free Shipping/No Service Charge for Mother’s Day! ENDS TODAY: Enjoy Complimentary Second Day Delivery in Time for Mother’s Day Tip 4: Let them treat themselves On Mother’s Day, the top email revenue generators were “self-gifting” (treat yourself on Mother’s Day only), Mother’s Day online sales and free shipping, as well as e-gift cards: Free Shipping Today Only! Happy Mother’s Day You deserve a treat yourself! HAPPY MOTHER’S DAY! Treat yourself to 30% off today only Last Chance: eGift Cards in Time for Mother’s Day Other email performance highlights: Note: All email performance highlights are based on comparisons to Mother’s Day mailings without the highlighted feature from matched brands. To all those in the midst of Mother’s Day campaign planning, good luck and happy sending!