At A Glance
Reaching the right audience is becoming more challenging as digital advertising grows increasingly fragmented, and privacy regulations change. 33Across addresses this by providing real-time intent signals and contextual insights, helping marketers connect with consumers across devices like CTV, mobile, and desktop. Integration into Experian's marketplace makes these solutions more accessible, helping advertisers achieve measurable results.In our Ask the Expert Series, we interview leaders from our partner organizations who are helping lead their brands to new heights in AdTech. Today’s interview is with Allison Dewey (Director, Data & Curation) at 33Across.
Navigating complexity in digital advertising
Digital advertising is more fragmented and privacy-constrained than ever. How is 33Across helping marketers cut through that complexity to drive real outcomes, and what makes your approach distinct?
Reaching audiences without compromising scale or performance is one of the toughest challenges for marketers. Users consume content across multiple devices and channels, making it difficult for marketers to identify and accurately target them with the right message. 33Across segments leverage AI-powered contextual and behavioral signals across privacy-safe environments to help marketers accurately identify audiences, whether they’re streaming content on their connected TV or researching products on their mobile device. What sets us apart is that we don’t just identify valuable audiences; we help marketers also target cookieless inventory and power it with real-time signals.
Marketplace integration milestones
What excites you most about bringing 33Across audiences into Experian’s data marketplace?
We’re excited to bring 33Across audiences into Experian’s data marketplace because it connects our unique segments with a powerful data ecosystem that marketers already trust. Buyers looking to activate audiences that are both privacy-safe and performant continue to tap into the Experian data marketplace for high-quality, high-performing data. We offer a wide range of audience verticals, including B2B, demographic, retail purchase data, interest and intent, and political data. In addition, we offer the ability to create custom segments across verticals.
Our intent-based audiences, built from contextual and engagement signals, help buyers reach consumers on CTV, desktop, or mobile devices with scale. Being part of Experian’s data marketplace accelerates access to these audiences, drives better ROI, and helps brands future-proof their strategies today.
Retail demand signals
Retail brands are racing toward privacy-safe, first-party data. Which 33Across retail datasets or segments are experiencing the highest demand, and what makes them a must-have?
Retail marketers are leaning into contextual and behavioral intent signals to complement their first-party data strategies. At 33Across, we’re seeing high demand for segments tied to shopping intent, including in-market consumers browsing for categories like fashion, home goods, electronics, and health & wellness.
What makes these segments essential is their real-time nature – they can capture consumer interest as it happens. For retail brands looking to expand their reach while respecting privacy, our segments offer scalable, actionable intent that drives results.
B2B without cookies
Reaching real B2B decision-makers at scale is tough with or without signals. How does 33Across deliver both precision and reach in this environment?
B2B marketing often struggles with balancing scale and specificity. 33Across addresses this by combining contextual precision with AI-modeled behavioral signals; this segment approach reaches professionals actively engaging with relevant content and topics, even in environments where IDs are unavailable. Marketers gain access to more signals and, in turn, better reach from 33Across’ unique publisher integrations and audience curation built from machine learning and AI.
We surface intent through content consumption patterns and contextual engagement, unlocking valuable, privacy-safe signals at scale. Allowing B2B marketers to reach real decision-makers in a signal-sparse world.
Use cases
With retail, B2B, and beyond, can you share an example of how brands in these verticals are utilizing your audiences?
Top brands that have a user-focused approach use 33Across audiences to drive scale; performance. These brands enable our segments to precisely reach the right users across devices and increase conversion rates; brand awareness. By reaching the right users, brands have higher conversion rates and increase campaign efficiency.
Supply path innovation
As identifiers disappear, advertisers are looking for scalable, privacy-safe ways to reach real people. How is 33Across helping unlock more addressable inventory and drive performance?
By combining contextual, semantic, and engagement-based signals, we deliver intent-based targeting that performs across CTV, display and video. Higher addressability helps marketers not only extend their reach but also deliver personalized messaging across digital channels in a privacy-compliant way.
Contact us
About our expert
Allison Dewey
Director of Data and Curation, 33Across
Allison Dewey is the Director of Data & Curation at 33Across, where she oversees data partnerships, integrations, and supply-side curation. With a deep expertise in audience targeting and signal optimization, Allison plays a key role in connecting data into the programmatic world. Allison holds a Bachelor’s degree in Psychology from Bates College.
About 33Across
Rooted in over 15 years of data expertise, 33Across harnesses signals to enrich and expand marketers’ audiences and reach them wherever they consume content. Built from over 300 billion proprietary data signals, we apply machine learning and AI to create over 1,500 B2C and B2B segments using privacy-first principles to reach audiences.
Cookieless targeting FAQs
By using contextual and engagement-based signals, advertisers can target consumers across CTV, mobile, and desktop in a privacy compliant way, even as identifiers become less available.
Segments tied to shopping intent, such as consumers browsing fashion, electronics, or health products, are highly sought after because they capture real time interest and drive results.
Combining content engagement patterns with machine learning allows marketers to reach professionals actively engaging with relevant topics, even in environments where IDs are unavailable.
Privacy safe targeting uses real time contextual and behavioral signals to deliver relevant messaging across devices and channels without compromising consumer trust.
Real time intent signals allow advertisers to capture consumer interest as it happens, helping demand side platforms and brands deliver timely, relevant ads that increase engagement and drive conversions across devices like CTV, mobile, and desktop.
Latest posts
To our valued customers and partners, it’s been an exciting week here at Tapad! As announced in a press release this morning, Tapad is now a member of the Experian family. We’re thrilled to continue to grow as a leader in identity resolution under the umbrella of a global expert in data, analytics and technology. Tapad and Experian are deeply connected by our commitment to serving the needs of our customers; and with a focus on quality of the data we provide, we have a common goal for the future of identity in the advertising ecosystem. As part of this announcement, we wanted to assure you, our valued customer, that we remain deeply committed to serving you today just as we always have. Nothing will change in your daily operations with Tapad. Experian immediately recognized that the success and growth of Tapad was directly tied to the strength and depth of its team members. As such, the acquisition will not result in any changes to day-to-day contacts at Tapad, or processes with weekly graph deliveries and other product support. Experian’s faith and investment in Tapad’s future and the future of identity resolution underscores what we’ve always believed our products could achieve and that we will be able to continue serving brands, advertisers, publishers, and the advertising and marketing ecosystem for years to come. On a personal note, I am excited to be transitioning my role as Chief Operating Officer of Tapad to the General Manager position of a global business that’s achieved exponential growth over the past several years; culminating in this strategic acquisition that will no doubt bring even more value to our customers in the future. We remain committed to open communication and welcome any questions you may have. Thank you,Mark Connon | General Manager, Tapad Contact us today
Addressable TV has been through a transformation in the past year. Streaming content has become the most coveted space for creators and advertisers with the rise of new apps and platforms; but the influx of stay-at-home orders around the country have shifted television viewership as we know it, and streaming apps are popping up in droves to take advantage. So, how can you? With no shortage of opportunities to advertise on addressable TV and CTV, how does it fit into the media mix? And furthermore, how can you attribute this household-level device into your overall strategy? Tying it all together Layering addressable TV within digital ad campaigns couldn’t be easier today — but applying the right targeting and cadence between all of your digital efforts; and tying them together in attribution takes the right kind of data. Marketers can use CTV identifiers coupled with other device identifiers available in The Tapad Graph to not only target impressions but also map addressable TVs within the consumer journey; and unify strategies between household decision makers to better personalize messaging. 1 The Trade Desk Q2 2020 Earnings Call Transcript, August 2020; 2 iSpot Report, via Deadline, July 2020; 3 Flixed.io, January 2020 Contact us today
For the past several years ad-tech defined the value of identity at the individual level; made possible by the evolution of data, technology and machine-learning. But, earlier this year COVID-19 set in motion many shifts in consumer digital behavior. The more we’ve been working and learning from home, using devices that are shared amongst an entire household, the more apparent it is that marketers need to shift their strategies to align with these changes. Did you know the average household owns eleven or more connected devices? And the longer we’ve been at home, the more these devices are shared by multiple individuals. If you’re looking for a few simple ways to evolve from an individual focused strategy to a household strategy, here’s a good place to start: Audience segmentation Traditionally, audiences are built with a narrow focus on a single user, and what known attributes about that individual or their brand engagement can be leveraged for a targeting strategy. Now that screens are being shared between multiple users in a home, how can you be sure you’re identifying them correctly, and thus, segmenting them in the right buckets for targeting? The key lies in the ability to connect those points through identity resolution. Using ad exposure from household level devices, followed by a second engagement from an individual within that household can indicate a user is a better candidate for purchase or conversion than others. So before you build audiences for targeting, you can qualify them at the household level for segmentation with more confidence. Example: An auto advertiser uses audience segments from a third party provider such as ‘auto intenders’ to target individuals with new pricing offers. They would continue retargeting these users, unaware that some are connected in the same household, and thus are probably not all in the market to actually get a new car. By bucketing users that share a common household device within this third party segment, they can hone in on which individuals are actually in-market for a car and evolve their strategy to be more effective. Targeting Retargeting, frequency capping and sequential messaging have always been meant for an individual user — the more they’re exposed to your brand in a personalized way, the more likely they are to take the desired action. But, have you considered that multiple users could have a shared initial exposure to your brand? Today, you can target a household of potential consumers on a shared device like a CTV, and employ those retargeting strategies based on that common initial exposure. Starting at the household level, means you can compare movement through the funnel between different individuals in that household, and tailor your targeting accordingly. Perhaps you realize only one person in that household will convert and you tailor messaging to them more frequently, while confidently suppressing the other individuals. Example: a CPG brand uses OTT advertising, but doesn’t incorporate it within their sequential strategy, because they consider it just a ‘brand awareness’ opportunity. By using OTT more strategically as a household level engagement, it can reveal which individuals within a household are more favorable towards a brand further down the funnel. So, you can spend impressions targeting those users, rather than wasting impressions on multiple individuals within the household. Measurement Measurement and attribution are imperative to understanding the path to purchase and making strategies more efficient over time. Often that efficiency involves adding or removing devices and channels from a targeting strategy based on their contribution to an action or conversion by an individual. This year we’re seeing addressable TV devices explode in use, which are shared at the household level. Even desktop computers are being used by more people in the home due to COVID-19. So, assuming a linear path of attribution by an individual is missing the full picture. Identity resolution can help you understand where messaging was more effective for some users in the household than others, and leverage that insight to continue more effective strategies in the future. Example: Without a household view, a direct-to-consumer brand would assume all interactions from one device would be coming from a single individual, and that could create a higher cost-per analysis. By incorporating the household level devices into attribution models, they can find efficiencies between touch points of multiple users, and learn how those split off into individual paths to conversion. Not only can this DTC create a more effective model, but they can use that model to create cost efficiencies in the future. Contact us today