Like many mediums in ad tech, privacy-compliant technologies, or more specifically, data clean rooms are becoming oversaturated with solutions. So, data clean room providers must differentiate themselves from one another.

Optable partnered with The Globe and Mail’s advertising arm, Globe Media Group, to stand out in the marketplace. Following this collaboration and a successful campaign for VIA Rail, Optable wants the technology to enhance audience targeting and drive revenue growth through advanced data strategies.

For example, in their recent VIA Rail campaign, Optable’s data clean room matched VIA Rail’s customer data with The Globe and Mail’s subscriber base, enabling highly targeted advertising. 

We sat down with Bennett Crumbling, Head of Marketing, Optable, to understand how his approach achieved 3.4 times greater reach and 2.5 times more cost-effective results. 

Andrew Byrd: The ad tech industry has promoted data clean rooms as one of the major privacy-focused solutions, but some publishers seem hesitant to test them out. What would you say to them?

Bennett Crumbling: Like any new technology, there is an adoption curve. Organizations must adapt to fit these new solutions into their processes and find the right ways to capture the value they can create. Many misconceptions about data clean rooms are rooted in the idea that publishers either need loads of 1st party data and tons of engineering resources or give a large amount of money to what I call “ID middlemen” to make data collaboration work for them.

A couple of years ago, many thought DCRs were more of a luxury good used by the Disney & Netflix of the world rather than a core part of any publisher’s monetization strategy. What Globe & Mail has been able to accomplish, which is indicative of the types of success we have seen with tons of other publishers of varying shapes & sizes, is a clear sign that this is no longer the case.

We think data collaboration will become just as embedded in the planning, targeting & measurement stages of advertising as the DMP or the ad server. 

AB: Based on your data, Optable’s data clean room played a critical role in securely matching VIA Rail’s customer data with The Globe and Mail’s subscriber base. Can you explain how the data clean room ensures privacy and security while facilitating such collaboration?

BC: Optable’s data clean room technology ensures privacy by utilizing different  Privacy-Enhancing Technologies, or PETs, throughout the data collaboration process. Depending on the goal of the data collaboration, Optable’s platform provides purpose-built applications that use the appropriate encryption method to achieve these goals.

Whether the goal is to build a securely matched audience directly for targeting and activation or to share insights about audience behavior, we have developed end-to-end workflows that enable publishers and advertisers to easily complete a successful collaboration while always protecting individual identities from either party and ensuring audience privacy.

AB: The VIA Rail campaign achieved impressive results, including a 3.4X greater reach and a 2.5X lower cost per reach. What role did Optable’s technology play in achieving these outcomes, and how did you optimize inventory targeting for this specific audience?

BC: Optable’s clean room was pivotal in helping VIA Rail and The Globe and Mail securely match their data sets, enabling custom audience segments like “Lapsed Travelers” and “Mid-Week Travelers.” Our platform allowed for highly targeted ad placement, ensuring that VIA Rail reached the right people at the right time. Additionally, the lookalike modeling extended the campaign’s scale beyond direct matches, enhancing reach and cost efficiency. This level of precision and scalability is what drove the remarkable 3.4x greater reach and significantly lowered costs per impression.

AB: For publishers like The Globe and Mail, how does Optable’s platform help enhance audience engagement and grow readership through data-driven insights and targeting strategies?

BC: Optable’s platform allows publishers like The Globe and Mail to gain a deeper understanding of their audience by securely combining their first-party data with that of their advertising partners. This enables them to create highly relevant and personalized content and campaigns that engage specific audience segments. By leveraging data-driven insights, publishers can identify patterns in user behavior, tailor their offerings, and strengthen their value proposition to readers, all of which contribute to increased engagement and readership growth.

AB: How can publishers use Optable’s technology to monetize their first-party data better while maintaining a privacy-safe environment for their users?

BC: With Optable, publishers can securely unlock the value of their first-party data by collaborating with advertisers without compromising privacy. They can offer advertisers highly valuable audience packages and insights for targeted campaigns using clean room technology. This privacy-safe approach ensures compliance with data regulations and builds trust with users, who can feel secure knowing their data is protected.

This represents a significant opportunity for publishers to monetize their data through bespoke partnerships and premium advertising deals.

AB: How does Optable help publishers build stronger relationships with advertisers, like in the VIA Rail campaign, and what are the long-term benefits for both parties in this type of collaboration?

BC: Optable’s clean room technology enables deeper collaboration between publishers and advertisers by facilitating seamless, privacy-safe data sharing.

In the VIA Rail campaign, The Globe and Mail provided VIA Rail with unique audience insights based on their rider’s behavior and with Globe & Mail readership insights, leading to more effective campaign targeting. This type of data collaboration not only enhances campaign performance but also strengthens the trust and relationship between publishers and advertisers.

In the long term, these partnerships can evolve into mutually beneficial deals, such as subscription bundling or co-branded offerings, which drive revenue and engagement.

AB: What advice would you give to publishers just starting to explore data clean room solutions? How can they leverage these tools to create more effective partnerships with advertisers?

BC: My advice would be to start small but test ASAP – test clean room solutions on specific campaigns and with trusted partners to understand their value. Clean rooms allow publishers to unlock their first-party data and offer advertisers deeper insights and more precise targeting, which enhances campaign outcomes. Over time, as publishers get comfortable with the technology, they can expand their data collaboration to build stronger, more data-driven relationships with advertisers.

The post Optable and The Globe and Mail Push for Data Collaboration with New Clean Room Partnership appeared first on AdMonsters.

You must dig deep into each PET solution, to decide which works best for your business goals. For instance, how do you decide which Data Clean Rooms (DCR) fits best? 

DCRs offer a way to analyze sensitive data without compromising privacy or security. Myles Younger, Head of Innovation and Insights at U of Digital, likens them to a pivot table in Excel; data clean rooms grant users access to insights from large datasets without directly accessing the underlying raw data. This ensures that PII remains secure while enabling detailed analysis and actionable insights. 

As Younger points out, the key to success lies in how companies use clean rooms to drive client value. “Advertisers want what they’ve always wanted: new ideas, insights, and clear performance measurement,” he explains. 

Therran Oliphant, an advisor at Thirdwave and AdMonsters Advisory Board Member, adds that publishers are evaluating data clean rooms with more sophistication as they consider factors such as decentralization, privacy, and data orchestration. He points out that the ability to keep first-party data on-premise, without moving it into cloud environments, is crucial for many publishers and their ad ops teams. 

This decentralized approach allows them to retain full control over their data while leveraging the clean room’s tools to share only non-personal insights, ensuring both data safety and effective marketing execution. When used correctly, DCRs bridge the gap between performance optimization and data security, making them a vital part of the future of digital marketing.

We took a deep dive to discover seven of the top Data Clean Room solutions to help you find the perfect fit for elevating your business’s data strategy.

7 Top Data Clean Room Solutions

AWS Clean Rooms 

Amazon’s Data Clean Room solution, AWS Clean Rooms offers a secure solution for companies to collaborate with partners on data analysis without sharing or duplicating sensitive information. By setting up a clean room in just a few steps, businesses can securely work with other companies on AWS, using privacy-enhancing tools to protect their data. 

AWS Clean Rooms supports a range of use cases, from improving customer insights and optimizing advertising to enhancing reporting and research. Clients like Fox, Comscore, and Amazon Ads use this platform to unlock valuable insights while safeguarding data privacy.

According to Adam Solomon, Global Head of Business Development and Go-to-Market for AWS Clean Rooms, AWS sees itself as a facilitator, enhancing data clean rooms’ capabilities by enabling secure data sharing without moving it between platforms. 

AWS exemplifies this through partnerships with The Weather Channel and Lotame to enable faster and safer insight generation. Rather than competing directly with data clean room providers like Habu or InfoSum, their ultimate goal is to provide foundational privacy-enhancing technologies for others to build on.

InfoSum

InfoSum’s data clean room solution offers data protection and seamless collaboration through a match system that allows companies to analyze multiple datasets without moving or sharing data. Through decentralized data processing and patented ‘non-movement’ technology, InfoSum ensures that sensitive information remains private, eliminating data exposure or misuse risks. 

This privacy-first approach supports a range of data-driven strategies, such as audience planning, activation, and measurement, while providing superior speed and efficiency, even in multi-party collaborations. Its flexible, intuitive tools allow marketers to control their data processes without specialized technical expertise.

One of InfoSum’s key features is its ability to boost match rates by integrating with any identity provider, enabling seamless crosswalks and transparent match testing across multiple datasets. By connecting data silos both internally and with external partners, businesses can unlock valuable consumer insights and enhance targeting and personalization. 

What are some of their most prominent integrations? Netflix, Samsung Ads, and WPP’s data and tech platform Choreograph which is run mostly through GroupM. 

LiveRamp (and Habu)

LiveRamp’s data clean room technology provides companies with a secure environment to use and share data while maintaining consumer privacy. One of its key features is robust privacy and governance, which ensures data protection, giving media companies confidence in the safety of their information.

LiveRamp’s data clean room offers strong interoperability, allowing marketing organizations a seamless connection to their  partners (CTV providers like Amazon, Facebook, and Google.) Liveramp designed the platform for ease of use, making it accessible for marketers to analyze campaigns across multiple channels, including TV, CTV, and social media. 

By offering a unified view of performance data across all advertising channels, LiveRamp enables marketers to draw holistic insights, enhancing their strategic collaborations. Furthermore, it allows them to maximize every customer touchpoint, optimizing engagement. 

LiveRamp recently acquired Habu to assist with accelerating data collaboration with enhanced clean room tech. This is the industry’s only interoperable platform for seamless data sharing across all clouds and walled gardens.

Google Ads Data Hub

Google Ads Data Hub integrates with BigQuery to combine first-party data with Google’s event-level ad campaign data, providing valuable insights enhancing  advertising efficiency and optimize campaigns. It ensures user privacy by grouping results over multiple users and implementing robust privacy checks.

The platform supports access to Google, mobile device, and publisher-specific user IDs for comprehensive campaign analysis while maintaining strong privacy controls. Enhanced audience building and management capabilities are available for integration with other Google Ad products.

BigQuery Data Clean Rooms offers a secure, privacy-focused solution for managing and analyzing data without duplication. It helps publishers refine audience targeting, comply with privacy regulations, and collaborate efficiently with partners. With quick deployment through Google Cloud Console or APIs, it supports real-time updates and provides aggregated metrics for data-driven decisions.

Their most recent integration with DSP, Viant Technologies, allows seamless onboarding of privacy-safe first-party data into Viant’s Data Platform, enhancing targeting and measurement capabilities. 

Snowflake

Snowflake Data Clean Rooms, launched following Snowflake’s acquisition of Samooha in December 2023, integrates advanced data clean room technology into the Snowflake ecosystem. 

Now available through the Snowflake Marketplace, this offering simplifies setting up and using data clean rooms without incurring additional access fees. Snowflake Data Clean Rooms provide a user-friendly interface and industry-specific templates, allowing organizations to efficiently collaborate on sensitive data while maintaining strict privacy and governance standards.

To overcome the traditional barriers of data clean room deployment Snowflake designed its Data Clean Rooms solution, making it accessible to companies of all sizes. It facilitates secure, cross-cloud collaboration across AWS and Azure, and integrates seamlessly with Snowflake’s open data cloud ecosystem. By leveraging Snowpark for AI/ML and other built-in privacy features, Snowflake ensures that sensitive data remains within its secure environment while enabling deeper analytical insights. 

In addition, they have partnerships with Netflix and Snap. 

Optable

Optable’s data clean rooms offer secure environments where multiple parties collaborate on data while keeping the underlying data private and intact.

These clean rooms ensure that each participant maintains control over their data. Key features include permissioned access, where all parties must consent to any operations, and built-in privacy protections to safeguard information. Optable provides each customer with a secure data collaboration node (DCN) to store their data, and they can configure the connectivity and information transfer level within their own DCN.

Additionally, non-customers can download an open-source utility to manage their data independently. Optable’s versatile data clean rooms support various functions such as planning, measurement, and activation.

AppsFlyer Data Clean Room

AppsFlyer’s Data Clean Room offers a distinctive advantage with its advanced custom business logic and self-owned buckets. The custom business logic feature allows users to run tailored queries within the clean room, providing precise insights specific to their marketing needs. The self-owned buckets enhance data control by enabling users to manage data upload, integration, and access permissions independently, ensuring that all data handling aligns with their unique requirements and privacy standards.

Moreover, AppsFlyer’s Data Clean Room simplifies the reporting process with its user-friendly interface and automated report configuration. This solution streamlines BI tasks and ensures compliance with stringent privacy regulations like GDPR and CCPA. 

This clean room also encompasses a wide range of channels to provide a comprehensive view of your marketing efforts. This includes digital channels such as social media, search engines, display advertising, and video platforms. By integrating data from these diverse sources, the clean room allows you to gain a holistic perspective on campaign performance and optimize strategies across all your marketing channels while maintaining stringent privacy standards.

Debunking the Myths Around Data Clean Rooms

If you are hesitant to try out data clean rooms, Younger urges you to let go of your misconceptions. 

The biggest misconception about data clean rooms is that their use cases are new or unclear. In reality, most have been around for decades, such as publisher audience segmentation, advertiser audience onboarding, and event matching for post-campaign attribution. Data clean rooms simply represent the latest, most secure way to execute these longstanding processes with enhanced privacy protections.

“We work with ad tech companies and platforms across the industry, and the ones that seem poised for success are the ones that are positioning clean rooms as unlocks for client success, not as technical marvels,” says Younger. 

And for some experts, data clean rooms, or data collaboration as a more broad practice, will be a large foundation to uphold the industry. This is especially true as we shift toward first-party data assets as the prominent cornerstone of digital marketing strategies in a privacy-first world.

The post What Are the Top Data Clean Rooms Solutions? appeared first on AdMonsters.

Worldwide, finding a consensus on nearly anything is just about impossible. Yet, when thinking about the way people interact with brands online, there are two glaring truths: consumers demand personalization and privacy in nearly equal measure.

Studies show time and again that nearly 90% of consumers want to do more to protect their online privacy, and almost as many consumers will choose one brand over another if that brand provides a personalized experience. Both of these aspects of digital advertising and commerce are now table stakes. Striking the balance between the two, however, can be difficult, particularly for upstart brands. 

On the privacy front, many brands must contend with increased regulation. Especially in a more globalized marketplace, brands need to conform to international regulations, including GDPR, CCPA, and many more, which can limit the amount and type of consumer data they can collect.

This is all leading to the eventual depreciation of third-party cookies. While it’s true that Google has walked back from its plans to eliminate cookies in Chrome, other browsers have degraded their value, and their continued use in global commerce can run afoul of privacy regulations. Moreover, even if cookie depreciation is slow, brands can find a point of differentiation by offering services that demonstrate respect for consumer privacy. Traditionally, this means turning to transparently collected first-party data.

Yet for smaller advertisers, building up stores of that valuable data can be nearly impossible; third-party cookies are a cheap and abundant way to deliver that needed personalization at scale.

Looking to the future, data clean rooms can be a conduit for advertisers to continue offering highly personalized experiences while also respecting consumer privacy through multiparty collaboration and first-party data access.

What Are Data Clean Rooms?

To understand what a data clean room is, it’s first essential to know why it rose to prominence about a decade ago. For smaller brands and advertisers, there isn’t the luxury of vast amounts of first-party data for targeting and personalization efforts. However, if advertisers could share data with other smaller entities, perhaps everyone could benefit from those insights. 

Data clean rooms provide a secure virtual environment where multiple parties can analyze and collaborate using shared, anonymized data sets without the risk of exposing or sharing the underlying data. These virtual platforms provide the necessary data protections that can enable collective user data programs while remaining above board with regulators.

The Importance of Multiparty Collaboration in Data Clean Rooms

As regulation increases and consumer sentiment moves more towards privacy, brands and advertisers will need to rely more heavily on their first-party customer data. Collection of this data must be ethical and based on a value exchange, with consumers willingly offering their information in exchange for exclusive offers, access to gated content, rewards programs, and much more.

For larger brands with massive customer bases, accessing this first-party data provides a major competitive advantage over smaller brands. If you already have a user base of hundreds of thousands of customers, turning that user data into something actionable is almost as simple as flipping a switch. Smaller brands don’t have that same luxury, which is where collaboration becomes essential.

Data clean rooms level the playing field for smaller advertisers by pooling first-party data to create a unified resource that all contributors can access.

What Advertisers Can Do With Pooled First-Party Data

By working together, small and mid-tier advertisers can enjoy the same insights as larger brands with massive stores of first-party data through data clean rooms.

The utility of this pooled data can’t be understated; bringing in anonymized consumer information from multiple brands can dramatically improve customer experience across each brand’s channels. By analyzing aggregated data, advertisers can identify patterns and trends that might not be evident from their data alone. Zooming out and broadening the pool of insights enables more precise audience targeting, which can improve the effectiveness of marketing campaigns.

Advertisers can also leverage this pooled data for performance tracking and benchmarking campaign efficacy against industry standards or competitors to help identify areas for improvement.

Data clean rooms help facilitate this collaboration, extending beyond data sharing. It can also enable advertisers to co-create targeted campaigns with partners, which can help optimize ad spend and maximize reach.

Why We Need Clean Room Standardization

Once you understand the utility of data clean rooms, it’s pretty easy to see the difference they can make industry-wide. Unfortunately, one of the biggest challenges of data clean rooms that threaten their adoption is a lack of rules and standards for contributors.

Standardization works to ensure consistency and trust across platforms. Establishing uniform protocols and frameworks for data security, privacy, and collaboration can facilitate seamless data sharing and analysis between different parties, reducing complexity, enhancing efficiency, and encouraging continued collaboration.

Additionally, locking in set security protocols guarantees that all parties adhere to the same stringent regulations, thus protecting consumer data more effectively.

In early 2023, the IAB Tech Lab set out to create a set of unified standards for data clean rooms. While this project is still ongoing, it opens up the conversation for parameters of collaboration in the future.

Data clean rooms are not without faults, but their adoption is critical to enable small and mid-sized advertisers to compete with larger companies as the availability of third-party data dwindles. Coming together, creating a standardized methodology for data clean rooms, and using that combined data effectively can be a major win for the entire industry.

The post The Crucial Role of Data Clean Rooms in the Future of Digital Advertising appeared first on AdMonsters.

At this point, it’s not news that years of ongoing changes in data privacy regulation have created massive amounts of change in the way that data is being used (or not used) across the advertising industry.

As IAB Tech Lab CEO, Anthony Katsur, often says, “Just like energy, finance, or healthcare, advertising is now a regulated industry.” As part of this trend, publishers face challenges in creating sustainable revenue growth.

Navigating Data Privacy in Advertising

Whether it’s the continuing decline in ad revenue that digital publishers are grappling with or the never-ending struggle that the streaming television industry is having to reach profitability it’s clear that owners and publishers of media are feeling the effects of these changes.

One of the areas where these changes are most visible is within the publisher’s data technology stacks. Increasingly, publishers are centralizing the many data sources they need for monetization within their data warehouse. While this evolution brings the promise of insights and connectivity, publishers also need a purpose-built application layer to help them activate and get the most value from their data.

DMPs: From Central Role to Obsolescence

For years publishers relied on DMPs to be at the center of their monetization efforts. As cookie-based monetization becomes less and less dependable and publishers’ distribution channels continue to fragment outside of the web these systems have failed to develop new solutions for key functions like app and historical data collection, 2nd-party audience enrichment, and programmatic activation.

This leaves most legacy DMPs relegated to web-based data collection, audience segmentation, and simple ad-serving activation. Additionally, traditional DMPs were not built with important capabilities such as data clean rooms, identity resolution, and PETs which are extremely important in our privacy-centric world.

Data Warehouses: A New Hub for Monetization

Many DMPs have responded by integrating large data sets through mergers and acquisitions to help fill gaps around identity, some are playing catch up by trying to build more privacy-centric features like identity and clean rooms, and others have decided to completely go out of the business. A response to this lack of innovation by DMPs in recent years has been more organizations investing in their data warehouse to centralize their various audience data sources. The question is, is the data warehouse alone enough?

The Missing Piece: Purpose-Built Applications

As we talk to customers in the market it’s clear that they need applications that can work with their data warehouse to create efficiencies and grow their revenue. One of the biggest challenges is actually activating data.

Data warehouses often rely on applications and integration providers to make data more actionable which leaves publishers building expensive custom solutions and navigating complicated operations.

Similarly to how the Composable CDP movement has stepped up to help marketers evolve how they activate data in their warehouse, media owners and publishers (and new companies like retail media) need solutions that are purpose-built for both the era of privacy as well as ad monetization use cases.

Embracing the Future of Audience Monetization

Audience monetization platforms of the future need to be able to combine the streamlined audience building and activation (in both programmatic and direct)  that legacy DMPs relied on, while also allowing for more complex tasks like normalizing various data sources, running complex identity resolution models and collaborating within data clean rooms.

As free and scaled 3rd-party cookie data goes away the monetization is shifting to the publishers and media owners who are investing appropriately in their 1st-party-data, and there’s a major opportunity to create profitable growth. Investing in technology to help power this growth is crucial and will separate the winners from the losers during this period of change.

The post The Data Warehouse Has Replaced Many DMP Functions, but Is It Enough for Publisher Data Monetization? appeared first on AdMonsters.

The streaming wars are  entering a new generation, marked by Paramount’s potential revival through Skydance and the emergence of unconventional social media entrants like LinkedIn, X (formerly Twitter), and TikTok. 

Increased merger and acquisition (M&A) activity is also shaping the advertising space as legacy media players adapt to shifting consumer preferences toward streaming. This transformation underscores the growing complexity of the media landscape and the necessity for advertisers to diversify their campaigns and reach their audiences effectively.

The revival of Paramount through Skydance exemplifies how traditional media companies are reinventing themselves to stay relevant in the streaming age. Skydance, known for its high-quality content and production capabilities, can potentially breathe new life into Paramount’s streaming offerings, attracting new subscribers and retaining existing ones. This move highlights the importance of content quality and brand recognition in the highly competitive streaming market. Here are other ways to approach the new generation of entrants while still ensuring effective reach and campaigns.

Programmatic and CTV Integration

At Dstillery, we have seen firsthand how brands and marketers are refreshing their strategies to navigate this evolving environment. Integrating Connected TV (CTV) into hands-on programmatic buying platforms and leveraging clean room data matching are key strategies that marketers and brands use to better understand the impacts of CTV advertising compared to standard linear television.

With its ability to deliver highly targeted ads to specific audiences, CTV is rapidly gaining traction among advertisers across all parts of the funnel and becoming a factor when looking at budgets. By using programmatic buying platforms and clean rooms to combine fragmented reporting from walled gardens, advertisers can better target the right audience and optimize budgets. Yet, this tactic is still in its early growth stages

Adopting AI Targeting and Measurement Technology

Adopting AI targeting and measurement technology is crucial. These advanced tools help media buyers understand and then find customers on the most relevant types of content, genres, networks, or categories. AI-driven insights can reveal patterns and trends in consumer behavior that might not be immediately apparent through traditional methods. 

For instance, an AI system can analyze vast amounts of data such as aggregated historical reporting or ACR data related to their campaigns to better understand and optimize against their desired KPI and audience. 

One of the critical aspects of effective targeting in  streaming is understanding how ID-based targeting translates into CTV delivery to better identify your audience. While cookies allow for a 1:1 relationship between an ID and a single browser for targeting, these cookies do not exist on other devices, and so must often be probabilistically matched to a household via an IP address. This means that while one person in a household may belong to a given audience, ads will be shown to everyone in that household. It is essential to consider this when selecting your audiences or using content-based optimizing features to better fine-tune your targeting.

The Streaming Players

The continuing growth of ad-supported tiers on leading streaming platforms and potential entries of social players like LinkedIn, X, and TikTok further intensifies the competition. These platforms bring unique strengths and audiences, challenging traditional media companies to innovate and adapt. 

LinkedIn, for instance, could leverage its professional network to offer niche content tailored to career development and industry insights, while TikTok’s short-form video format appeals to younger audiences looking for quick, engaging content. X’s vast user base and real-time engagement capabilities could position it as a formidable player in live-streaming events.

Increased M&A activity among legacy media players reflects their efforts to consolidate resources and expand their streaming capabilities. These media giants aim to enhance their content libraries, technological infrastructure, and market reach by acquiring or merging with other companies. This trend will likely continue as companies strive to stay competitive.

What Is in Store for Advertisers

These developments mean advertisers must navigate a more fragmented media environment. Diversifying campaigns across multiple platforms and formats is essential to reaching the best audiences. Advertisers must stay abreast of the latest trends and technologies to engage viewers and measure the impact of their efforts.

Overall, the new generation of streaming wars presents challenges and opportunities for advertisers. By starting to take CTV into your programmatic buying platforms, leveraging clean room data matching, and adopting AI targeting and measurement technology, advertisers can better navigate the fragmented media landscape and reach their desired audiences. 

Understanding the nuances of both ID-based and content-based targeting, as well as staying informed about industry trends will be crucial for success in this dynamic environment. As the streaming wars evolve, advertisers must remain agile and innovative to stay ahead of the competition.

The post Conquering the Streaming Wars: An Advertisers’ Guide to Reaching Audiences in  Fragmented Media  appeared first on AdMonsters.

Given the breakneck speed of digital innovation nowadays, publishers need a competitive advantage. Standing out comes from the power of rapidly and accurately analyzing data. Take The Weather Company, for example, the global titan in weather data and forecasting, is supercharging their mobile analytics game after joining forces with Lotame and AWS Clean Rooms.

This powerhouse collaboration has slashed insight generation time by an eye-popping 98% and boosted query efficiency sevenfold, enabling The Weather Company to deliver data that’s not just fast but razor-sharp and hyper-relevant to its clients and partners. AWS Clean Rooms facilitates this by providing a secure environment where companies can collaborate on datasets without sharing or copying the underlying data, enhancing data privacy and compliance.

But let’s talk specifics. By digging deep into the behaviors and preferences of their travel audience, The Weather Company unlocked insights that go beyond the surface, fine-tuning strategies for travel advertisers. For instance, by analyzing user interactions on The Weather Channel mobile app, they can distinguish between frequent and infrequent travelers and preferences toward air versus land travel. This granular insight has allowed The Weather Company to craft finely tuned, targeted, and effective advertising strategies that deliver exceptional results for their advertising partners.

In our exclusive Q&A, I spoke with Dave Olesnevich, Head of Data & Advertising Products at The Weather Company, to unpack the technical challenges and victories of the integration. We explored how AWS Clean Rooms enhances data privacy and compliance, tackles the unique hurdles of mobile data, and shapes the future of ad targeting and campaign efficiency.

Lynne d Johnson: Given the increased scrutiny on data privacy and compliance, how does the AWS Clean Room technology help The Weather Company navigate these complexities? How has this transformed your day-to-day operations?

Dave Olesnevich: AWS understood the assignment when it came to creating a privacy-forward environment where multiple parties can collaborate with data quickly and easily. CISO’s office is more amenable to the clean room environment versus moving data out of house for engagements.

The AWS Clean Room isn’t magic though — participants have to bring high-quality data to the table in order to create insights that become actionable. We can control what data is accessible on a case-by-case basis, which is a table-stakes feature. The Weather Company now has a new way of working with our customers to create value. We’re still in the earlier days of utilizing data collaboration platforms for advertising engagements at scale, and I expect a lot more usage in the future.

LdJ: With the new system reducing the insight generation time by 98%, could you discuss how this acceleration has transformed your approach to ad targeting and campaign efficiency? How quickly can changes in weather patterns now influence ad placements?

DO: Time to value is going to change when we fully operationalize the system. The value is first to our customer, we can help them achieve their desired outcomes with a reduced number of hops in the process. The LOE to produce actionable insights for the C-suite is at our fingertips, so it’s not just paid, but owned and earned for the CMO and BPO, with opportunities for the CFO and COO as well. As weather becomes increasingly more impactful to the bottom line, we can help leaders harness weather intelligence for use across their business.

LdJ: How have these faster insights already impacted a campaign or strategy? What have been the most significant impacts on your business and client interactions?

DO: Now more than ever, we’re able to develop what we call a Weather Strategy for our customers across the enterprise, with less time blocking and tackling and more time spent unlocking the value of the insights to drive desired outcomes for advertisers across their entire media mix. Like many in our ecosystem, we’ve been working with Lotame and AWS for a long time. We’re all leaning in to build the next generation of advertising.

LdJ: Looking forward, how does The Weather Company plan to further leverage this enhanced data processing capability? Are there new types of data analytics or services you’re aiming to explore that were not feasible before?

DO: We’re just getting started. Targeting, measurement, attribution. We’re working with our customers to help them understand how weather impacts their customer behaviors and their business operations. End-to-end weather impact in advertising, from planning through activation and measurement is the future state.

The post Weathering Data Storms: How The Weather Company, Lotame, and AWS Clean Rooms Supercharge Mobile Analytics appeared first on AdMonsters.

With the decline of third-party cookies, data collaboration platforms might be the industry’s answer to improving data’s value. 

According to the IAB, data collaboration involves combining and analyzing data within a company or alongside partners for various purposes. Data collaboration platforms offer secure environments to share data safely while meeting privacy and security concerns. Plus, they help organizations better understand their customers’ needs by turning data into insights.

In this Q&A, Alexandra Theriault, Chief Growth Officer, Spherical at Lotame, shares how organizations can leverage data collaboration to access, analyze, and activate data. The tech company recently expanded the offerings for its end-to-end data collaboration platform, Spherical, to allow marketers and media owners to advance the potential of first-party data within their organization and across partners. 

Emily Dalamangas: We have heard a lot about the challenges for marketers, agencies, and media owners with the decline of third-party cookies. How have these challenges affected data enablement?

Alexandra Theriault: Truthfully, they haven’t in a meaningful way yet. Yes, brands and media owners have been discussing and thinking about the loss of cookies for years. Many have invested time and resources to build up their first-party data resources and test cookieless options. Still, at the end of the day, unless you’re Google or Amazon, that precious resource will only get you so far. 

Cookies do exist today, and many organizations are still using them. Cookie deprecation may feel more real now that Chrome has sunsetted 1%, but that’s a minimal number of people in the grand scheme of Internet users. 

As we heard from our recent Lotame webinar about data collaboration strategies, there’s a lack of clarity about which direction the market will go, and there are too many options for brands to wrap their heads around. But in the meantime, data collaboration provides a perfect onramp to continue doing the important work of data enablement, analysis, and activation. 

ED: Many organizations leverage data collaboration platforms to drive greater value from first-party data. What are the competitive advantages of this approach? 

AT: Scale! In Lotame’s case, our Panorama Identity Graph brings more data to the table. Because we don’t require joins to happen solely on a deterministic ID (HEM) or a MAID, we can empower both data collaboration participants to bring known and unknown data to a collaboration. 

In one use case, we recognized an 11% overlap between a publisher and a brand, equating to roughly 27k uniques from the brand’s 260k qualified leads. Bringing more first-party data to the collaboration is a strategic advantage to having a statistically significant dataset to analyze.

ED: Lotame recently launched two new tools, Lotame Collaborate, and Lotame Onboarding, for its Spherical platform. Why are these tools a necessity in today’s advertising landscape? 

AT: Collecting and combining first-party data is an age-old problem. Digital marketers often have data silos within their organizations that work against their best efforts to understand current customers and prospects for their next best. 

Onboarding solves that problem within a company by enabling digital marketers to create a single source of truth for their first-party data. Those same marketers understand the preciousness of known first-party data, such as emails, but scale is a real and present issue for the vast majority. 

Data collaboration tools empower digital marketers to combine their first-party data — both emails from logged-in users and web or app visitation data — to permission with an external partner for accurate scaled analysis.

Data collaboration tools empower digital marketers to combine their first-party data — both emails from logged-in users and web or app visitation data — to permission with an external partner for accurate scaled analysis. The goalposts haven’t changed: understand consumers and meet their needs. Data collaboration is the evolution of an essential toolset for marketers and the partners of their choice – media owners, other brands, etc. – to do just that in a more sophisticated, privacy-conscious way.  

ED: A common misconception is that data collaboration and data clean rooms are the same. Can you clarify the difference? 

AT: Data clean rooms represent a core capability of data collaboration but not the complete solution. There are various types of data clean rooms with a wide array of capabilities. 

Lotame’s Spherical platform differs from a traditional clean room in that it addresses a company’s internal needs to collect and connect its first-party data for analysis and activation and the ability to enrich, analyze, and activate that data with external partners. 

ED: Matching first-party data, such as email addresses, with digital identifiers is essential for marketers and media owners to ensure addressability and scale. What advice would you give them in navigating data onboarding in a cookieless world? 

AT: Email addresses don’t ensure addressability and scale unless you’re a walled garden like Google or Amazon. Most digital marketers need to think beyond their known first-party data to the wealth of signals from web visitors who aren’t logged into your site or data representing your customer from panels or surveys. 

If more scale from first-party data is required to meet campaign objectives, test different machine-learning options to generate lookalikes. Don’t just trust the walled gardens’ black box solutions. If one performs and another doesn’t, how can you attribute what worked and what didn’t? 

ED: Spherical empowers organizations such as RE/MAX, LLC, Publicis, and Dentsu. Can you share an example of how an organization has found success with data collaboration? 

AT: RE/MAX partnered with Advance Local using Lotame Collaborate. The two brands permissioned part of their first-party data for in-depth overlap analysis and indexing. Advance turned that collaboration into sophisticated personas for RE/MAX based on lifestyle interests and granular keywords. 

In addition, the collaboration pointed to new markets of opportunity for RE/MAX to consider opening a physical location where interest in real estate was high.  

ED: What does the future of data collaboration hold, and what should marketers and media owners do now to prepare? 

AT: We anticipate AI to play a larger role in data collaboration, especially in analysis and persona building. The best preparation is practice. Our industry changes so quickly all the time. Test, test, and test again until you discover what works best for your company and your marketing dollars. If we’ve learned anything in advertising, a one-size-fits-all approach won’t work for the 99% of digital marketers whose use cases differ.

The post Enabling Data Collaboration as Cookies Crumble: An AdMonsters Q&A With Lotame’s Alexandra Theriault appeared first on AdMonsters.

In the dynamic landscape of data management and privacy, a transformative shift is unfolding, steering the way organizations manage and share data. This shift, heralded by the advent of decentralized data clean rooms, is giving rise to ‘Hedged Gardens’ and ‘Clean Houses’, signifying a progressive evolution from the traditional ‘Walled Gardens’. This transformation is underpinned by advancements in data clean rooms, differential privacy, identity resolution, and the burgeoning need for interoperability, setting the stage for a more collaborative and secure data environment.

“Advances in data clean rooms is a testament to how fast ad tech adopts a new technology when it unlocks utility,” said Shailley Singh, EVP of Product and COO at IAB Tech Lab. “Data clean rooms are a viable option for activation of audiences and reconciling measurement in a privacy safe manner while keeping the data within your custody and policy controls”

Traditionally, organizations have operated within ‘Walled Gardens’, a term used to describe a closed ecosystem where all operations are controlled and confined within the boundaries set by the organization. While this approach offers control and security, it limits the potential for collaboration and innovation, as data cannot be easily shared or leveraged outside the organization’s walls.

Hedged Gardens: A Balanced Approach to Data Collaboration

The concept of ‘Hedged Gardens’ emerges from the limitations of Walled Gardens. Unlike a walled environment, Hedged Gardens allow for controlled data collaboration between different entities. These environments are meticulously designed with ‘hedges’ – not impenetrable walls – symbolizing the balance between data privacy and data utility. Organizations can collaborate and derive insights while ensuring that the data remains secure and privacy is not compromised.

“We typically talk about Data Clean Rooms as a mechanism to activate data for targeting, but there is a whole use case of enriching data with insights, offering Hedged Gardens a unique opportunity to provide advertisers with incremental value,” said Mebrulin Francisco, Global Head of Data Strategy & Martech at EssenceMediacom. “As a Data Strategist, I can directly enrich my client’s customer data with key publisher data; getting a fuller picture of what customers are consuming or purchasing directly from the source through automated and repeatable queries.”

At the heart of this transformation are decentralized data clean rooms. These secure environments enable the convergence, processing, and analysis of data from diverse sources without compromising the raw data’s confidentiality. By addressing privacy concerns, these clean rooms facilitate data sharing and collaboration that were once hindered by traditional models.

“Clean rooms are one of the most important tools in a data-driven retail media toolbelt. Data collaboration enabled by clean rooms can solve some of the most important issues in the industry today–like audience creation, transparent measurement, and robust identity graphs. Clean rooms and a co-op garden approach to data provide advertisers with access to near real-time insights and full-funnel measurement,” said Evan Hovorka, VP of Product and Innovation at Albertsons Media Collective. Adding that, “shoppers get the benefit of a personalized ad experience, and clean room partners are rewarded with industry growth and innovation—all boats rise with the tide.”

Match keys play a pivotal role in the efficiency of data clean rooms by serving as unique identifiers that connect related data from various sources. Created through identity resolution, these keys merge information about the same entity into a unified dataset, ensuring accurate analysis. Their importance is magnified in settings that require high interoperability, allowing for the fluid exchange and use of data across different systems. Match keys effectively act as a common language, facilitating the integration of data from diverse origins. Additionally, they are instrumental in bolstering data privacy. By anonymizing data before it’s shared or analyzed, match keys prevent the exposure of personal and sensitive information, aligning with strict data protection standards.

Identity resolution in the data clean room is the linchpin for unlocking unparalleled insights, driving informed decision-making, and ensuring precision. “In the dynamic landscape of today’s digital ecosystem, where customer interactions span multiple channels and devices, identity resolution forms the bedrock of a unified and holistic understanding of our audience while enhancing privacy through the use of pseudonymous identifiers rather than PII.” said Max Parris, Head of Identity Product at Liveramp. “This not only results in higher match rates, but also cleaner matches in collaborative use cases that take place in the data clean room. Identity resolution is not just a necessity; it’s the catalyst that propels us towards innovation, customer-centricity, and better business outcomes.”

In addition, implementing data clean rooms entails overcoming technical challenges such as integrating disparate data sources into a unified environment, ensuring data quality, and maintaining real-time processing capabilities, all while safeguarding data integrity and privacy. These challenges necessitate advanced data mapping, transformation techniques, and the use of high-performance computing resources. Despite their benefits, data clean rooms face limitations like potential data bias, the risk of creating new data silos, and significant technical and financial barriers, particularly for smaller organizations. To address these issues, organizations must establish clear guardrails and best practices, including rigorous data audit trails, transparent data processing methodologies, and the adoption of open standards for interoperability and accessibility, ensuring effective and secure collaboration within data clean rooms.

Clean Houses: Ensuring Data Integrity and Privacy

‘Clean Houses’ extend the concept of data clean rooms, emphasizing the importance of maintaining data integrity and privacy. In a Clean House, data is not only brought together but is also cleaned, processed, and stored in a manner that adheres to the highest standards of data privacy and security. This is where technologies like differential privacy and identity resolution play a crucial role.

Differential privacy is a framework designed to ensure that the privacy of individuals in a dataset is protected when statistical analyses are conducted. It achieves this by adding a certain amount of random noise to the data or to the outputs of queries on the data, making it difficult to infer information about any individual. The key is to balance the noise so that valuable, aggregate information can still be extracted without compromising individual privacy. The sensitivity of the queries—how much a single data point can affect the outcome—and the desired level of privacy (often quantified as a privacy budget) dictate the amount of noise that needs to be added. As this concept gains traction, tools and practices are being refined to apply differential privacy effectively, ensuring that data analysis can be both useful and privacy-preserving.

“Data Clean Rooms provide Hedged Gardens the opportunity to mobilize their consented 1st Party Data asset in a way that gives them complete control of the data collaboration process while maintaining tight reins on data governance practices. This is exciting to see.” said Mebrulin Francisco, Global Head of Data Strategy & Martech at EssenceMediacom. “And while data clean room technology will not solve all ad tech problems, they are a powerful tool within the tool kit. Providing the marketplace an opportunity for a safer, privacy-enabling solution to data collaboration.”

IAB Tech Lab’s Data Clean Rooms: Guidance and Recommended Practices Version 1.0 established common principles, functions, and privacy enhancing technologies for Data Clean Rooms and outlined some limitations and guardrails when engaging with DCR. In addition, with the Open Private Join and Activation Version 1.0, IAB Tech Lab provides a path to using the outputs of clean rooms in actual activation of first-party matched audiences while ensuring partners do not learn more than what they already know about the personal information of data subjects and the data does not leak while transacting in real-time bidding systems.

A Brighter Future with Collaborative Data Ecosystems

“Data clean rooms are the modern solution to the walled gardens of the past. Being able to securely share double-blind queries to different parties and collaborate on the same first-party data is a game-changer in how we’ll move forward from cookie deprecation,” said Rosemary DeAragon, Global Head of Retail & Consumer at Snowflake.

As organizations continue to embrace these advanced technologies, a new era of data collaboration is on the horizon, characterized by enhanced security, privacy, and mutual growth. With decentralized data clean rooms leading the charge, the future promises a more collaborative and secure data ecosystem. The advent of Hedged Gardens and Clean Houses marks a transformative moment in data management and privacy, ensuring that data collaboration is conducted in a productive and protective manner.

The post The Dawn of Hedged Gardens: The Evolution of Data Collaboration appeared first on AdMonsters.

Staying ahead of the curve has become more challenging than ever in the ad tech industry. The traditional ad industry was already grappling with losing cookies and other identifiers, striving to compete with the formidable triopoly of Google, Facebook, and Amazon. As it was gearing up for this transformative shift, the digital advertising landscape experienced a significant downturn. 

The COVID-19 pandemic accelerated changes in consumer behavior, causing the market to soften as pandemic restrictions eased. The growth in digital ad spend, which had been on a double-digit trajectory, slowed to just 8.6% in 2022, leaving media giants and digital platforms with no choice but to reevaluate their strategies.

The pandemic forced major players like Disney, Roku, Spotify, and Warner Bros. Discovery to make difficult decisions, including staff layoffs. The year 2023 brought challenges, with industry titans like Google, Microsoft, Meta, and Amazon handing out pink slips to 50,000 employees in January alone. While advertisers are still spending, the expenditure isn’t as high as previously predicted, and scrutiny over ad investments has intensified.

Despite these challenges, experts expect the ad market to reach a record high of $326 billion in 2023, thanks to the explosive growth of streaming and short-form video ads on platforms like TikTok. Optable’s Data Collaboration for Media Owners latest white paper predicts growth in e-commerce, travel, and entertainment advertising, presenting opportunities amidst uncertainty.

Adapting to Change

Several key strategies are emerging as the advertising industry navigates these turbulent waters. Media companies focus on scaling programmatic and data-driven advertiser engagements to capture more revenue share from the advertising triopoly. Simultaneously, they enhance traditionally non-data-driven aspects of their ad businesses, such as sponsorships, to remain competitive and appeal to advertising partners.

However, the successful execution of these strategies requires the right technology. Over the past decade, the industry has witnessed a proliferation of adtech and data management solutions. Media executives now question whether these investments have met their expectations and can adapt to the industry’s constant churn. New consumer privacy regulations further complicate matters, limiting digital identifiers and forcing marketers to rethink how they use data for digital advertising.

The Data Management Landscape

Before delving into the intricacies of data collaboration, it’s essential to understand the data management technologies adopted by media companies:

1. Data Warehouses: Companies like Snowflake and Databricks have taken the lead, with a market size of $27.93 billion in 2022.
2. Customer Data Platforms (CDP): MParticle and Segment.io are notable players, with a market size of $2 billion in 2022.
3. Data Management Platforms (DMP): Adobe and Oracle have made their mark, with a market size of $2.46 billion in 2022.

These technologies have enabled media companies to leverage their first-party audience data effectively. However, they face challenges in securing data collaboration with their advertising partners

The Need for Secure Data Collaboration

Digital marketers, who rely heavily on data for planning and executing ad campaigns, are increasingly concerned about data privacy, particularly in light of regulations like GDPR. A survey by GetApp revealed that 82% of marketers are worried about data privacy in their activities. Consequently, digital marketers are eager to collaborate directly with media partners in a secure environment to enhance campaign targeting and measurement.

Data clean rooms have emerged as a solution to this need for secure data collaboration. These rooms allow companies to share and analyze first-party consumer data while safeguarding individual identities. Giants like Google, Facebook, and Amazon have already adopted this approach with their advertisers, paving the way for private data clean rooms to gain momentum.

Benefits of Data Collaboration for Media Owners

Media companies that can accommodate advertisers’ demands for private data collaboration stand to gain a significant market advantage. Data collaboration can boost revenues by attracting new advertisers, securing larger commitments from agencies and advertisers, and commanding premiums on ad products that leverage shared data.

However, media companies face a dilemma: scaling data collaboration initiatives while simplifying their technology stacks and reducing costs. In a rapidly growing economy, complexity often takes a back seat to immediate growth opportunities. However, when the focus shifts to efficient growth and profitability with limited resources, complexity becomes costly and hinders operations and innovation.

Successful publishers must enhance their standard products and create differentiated offerings to compete with dominant platforms like Google and Amazon. Achieving this requires data collaboration solutions that can harness various types of data, seamlessly integrate with existing tech stacks, and offer ease of use.

Challenges in Data Collaboration

Implementing data collaboration, especially when relying on a mix of data management technologies, introduces several challenges:

Lack of Interoperability: Data clean room solutions often lack true interoperability, requiring collaborators to use the same system. This becomes a barrier when key advertising partners use different systems. Some media owners resort to multiple solutions, which can be costly and time-consuming.

Fragmented Data: Data fragmentation poses a challenge in combining audience data and advertising campaign data to provide holistic insights. The multitude of technologies available for this purpose may not be optimized for advertising use cases, leading to complexity and delays in responding to advertiser requests.

Not Intuitive for Business Users: Bridging the gap between technology and business teams in ad-supported media organizations is challenging. Ad Sales and AdOps teams lack the expertise to generate custom queries, relying on data scientists from other departments. This results in time-consuming back-and-forth interactions and lost revenue opportunities.

Gaps in Data Privacy and Security: The complexity of data collaboration involving multiple systems, users, and regions makes it difficult to maintain robust privacy and security standards. Privacy Enhancing Technologies (PETs) have progressed, but the plethora of options and regional considerations add to the complexity.

The Path Forward

To thrive in this ever-changing landscape, media companies need holistic solutions that can harness the power of data, integrate seamlessly with existing tech stacks, and cater to business users. The industry is realizing that the key to success lies in simplifying complexity and reducing friction in data collaboration.

Cloud-based data collaboration applications offer a promising path forward. These applications facilitate interoperability, ease of use for business users, and enhanced privacy and security. They enable media companies to streamline their operations, reduce technology costs, and innovate more efficiently.

As data collaboration continues to evolve, proving value remains a top challenge. Media owners must demonstrate the ROI of their data collaboration efforts. While challenges persist, the potential for increased revenue, reduced technology costs, and improved operational efficiency positions data collaboration as a crucial component of the future of advertising.

The post The Crucial Role of Data Collaboration in the Future of Advertising appeared first on AdMonsters.

Steven Goldberg, VP of North America Publishers, LiveRamp, shared post-cookie solutions for publishers at PubForum Miami. He told attendees why publishers should be testing strategies now and how to leverage authentication tools, ID solutions, and data clean rooms.

 Increase CPMs With Authenticated Traffic

“The good news is the majority of the publishers are already preparing for a cookieless world. The issue is how aggressive they are with their approach,” said Goldberg. 

Goldberg emphasized that publishers must start testing solutions before Google makes its move. The time is now when they still have a runway to try out opportunities. At LiveRamp, Goldberg works with North American publishers to deploy the Authenticated Traffic Solution (ATS) product and suggests that publishers should consider an authenticated traffic strategy that shows, from a CPM standpoint, far superior results against other inventory.

Newsweek, a LiveRamp premium partner, saw impressive increases using  ATS. The publisher saw a total eCPM as high as 224% versus cookieless browsers. Newsweek had an average lift of 52% across all web browsers using ATS against Chrome traffic.

Goldberg highlighted a LIveRamp study looking at 70+ global publishers with ATS. It found a 100% improvement in CPMs on Safari and 113% on Firefox. “When compared to traffic that still has cookies, we’re seeing anywhere from 20 to 50% lift on average from most of the publishers,” said Goldberg.

Addressable Inventory Is a Value Exchange

Goldberg pointed out that many publishers think addressable inventory creates friction. But he said they should look at it from the perspective of creating a value exchange. If publishers provide value to their users, then users are likely to provide something to capture in return, such as an email or a sign-up.

Several authentication strategies publishers have tried over the last couple of years include newsletters, paywalls, sweepstakes, and single sign-on from social media platforms.

“But there is not a silver bullet for any one publisher, and it’s not realistic to think that a publisher is going to ever get to 100% authentication,” said Goldberg. But once they reach the 30 to 40 percent authentication range, publishers then achieve a scale where they can begin to have beneficial conversations with advertisers about their data.

Clean Rooms for Data Collaboration

A division of LiveRamp’s business is the commercial side, where the company works with publishers, advertisers, and agencies on initiatives like data onboarding and clean rooms to ensure they can continue to generate advertising revenue and obtain measurable results.

“It has been the year of data clean rooms. Everybody wants to talk about them,” Goldberg noted. “But then, when you ask who is actually using them, the answer is not too many people. They are not used as often as they are spoken about.”

LiveRamp recently partnered with CafeMedia on deploying LiveRamp’s privacy-first data collaboration platform, which enables marketers to securely connect with readers on one of the largest digital properties on the open web.

ID solutions are being considered a promising alternative to cookies. But publishers are often confused about ID’s purpose and where to begin with so many available solutions in the marketplace. 

“The industry has relied on cookies for so long, and when you have a big cookieless problem, there are going to be a whole slew of companies that are going to try to solve it,” explained Goldberg. 

He does not think LiveRamp is the sole solution and advises publishers to look at multiple options for authenticated and non-authenticated inventory and select the most relevant ones to their business. Publishers should weigh the scale of the solution and its uses on both the demand and the supply sides.

The email hash is at the root of ID solutions and first-party data gathering, but companies such as Apple’s Hide My Email are impacting the availability of authenticated inventory.

 But Goldberg thinks the email hash is here to stay as a stable identifier even though he admits that the degree of authenticated inventory may decrease.   

The post PubForum Miami: LiveRamp Preps Pubs for Post-cookie Era appeared first on AdMonsters.