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.   

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These websites are often niche, focusing on specific topics or interests and catering to particular audiences. While they may have smaller audiences, their visitors are more engaged and passionate about the content they consume.

The lack of addressability on long-tail sites presents a significant challenge for advertisers seeking to reach specific audiences without access to the data they need to target them effectively. 

Publishers on long-tail sites face several challenges related to addressability, including the difficulty of attracting advertisers to their sites due to the lack of addressable data, lower advertising rates, and data privacy regulation challenges. For them, collecting and using data for targeting purposes becomes more complex and potentially risky. 

Overall, the addressability challenges associated with long-tail sites can significantly impact publishers, making it difficult for them to monetize their content effectively and attract advertisers. We reached out to the AdMonsters community to learn what publishers want to know about these challenges. Then we chatted with Rowena Lam, Sr Director of Product, Privacy and Data at IAB Tech lab, about their solutions and technologies that address what is happening. 

Yakira Young: The farther out the long-tail, the more the skill-set biases content creation over technology. How does web infrastructure affect balancing long-lail while being privacy compliant?

Rowena Lam: An effective web infrastructure can allow for easier integration of privacy tools like consent management platforms or CMPs, which help manage user consent and help comply with privacy regulations.

When secure databases, encryption, and access controls are used, the infrastructure can help long-tail sites manage their data securely. Efficient web infrastructure helps optimize website performance so implemented privacy features don’t negatively impact their user experience.

YY: When it comes to single sign-on, many consumers hesitate to share consent with long-tail sites they aren’t familiar with. How can publishers address this hesitation and better gain consumer trust?

RL: I think the first thing that publishers can do is clearly explain what the benefits are for the consumer when they’re providing consent. That’s the baseline, and then they need to maintain consumer trust by making sure that they are complying with applicable privacy regulations.

And that means that they need to be, or should be, providing consent on how the data is being used, giving consumers control over it, and respecting their preferences after making their choices.

YY: What are your thoughts on consent banners?

RL: They’re usually managed by consent management platforms. CMPs surface consent banners, and there are various ways that we see them utilized. 

I think the one that is most widely known and understood are those cookie banners that we see everywhere. But we do have CMPs that display and some websites that choose to display something much more robust, allowing for many different choices rather than just accepting or rejecting all cookies.

YY: How could a publisher go about finding the right CMP? Some CMPs give the option to accept all cookies or reject cookies that consumers may not want to consent to. However, many CMPs are unclear about what’s really being rejected. How can a publisher go about utilizing a transparent cookie banner?

RL: Publishers should talk to as many privacy vendors as their time allows to see what sort of controls and flexibility the actual providers give them as the publisher or the person who owns the website. I think that’s the key because many of these privacy vendors who are CMPs or consent management platforms do have a lot of options and flexibility for publishers to toggle with. It’s about exploring the different options and finding the one that makes the most sense for their particular use case.

YY: How does IAB Tech Lab plan to balance the need for addressability with consumer privacy, particularly in the long-tail, and what portfolio of privacy-safe addressability solutions are you creating?

RL: The Tech Lab plans to balance this need for addressability with consumer privacy by creating a portfolio of different privacy safe addressability solutions. It includes developing technical standards for Data Clean Rooms, which allows for the sharing of data while preserving consumer privacy.

On February 16th, we announced the launch of the first in the Data Clean Room standards portfolio. We launched the Open Private Join and Activation or OPJA for short specification to support and define interoperable clean room interactions for digital advertising. On that same day, we also announced the launch of the Data Clean Room Guidance and Recommended Practices. Both of these are open for public comment until April 17th.

In addition to those Data Clean Room standards, we plan to expand on the Global Privacy Platform or GPP. This consent signaling protocol already supports consent signaling requirements for multiple jurisdictions, which we plan to expand to additional jurisdictions. 

We are also working on an Accountability Platform, which is a technical audit framework designed to help participating companies demonstrate that they are respecting consumers’ preferences and restrictions.

YY: I read an AdExchanger article about what it takes for cookieless solutions to work, and it stated that it’s important for brands to cast a wide net across consent IDs, including the Trade Desk’s UID 2.0, LiveRamps IDL, Merkel’s Mercury ID and many more. Do you agree with this?

RL: These days, the reality lies in that many ID solutions are available in the market today, and they’re not all entirely the same. What’s really important is that brands evaluate each of those IDs, like the ones you’ve mentioned, and others that are available based on the use cases that are important to them and understanding the components that make up the ID to understand ID viability and durability

Many of these IDs can handle ad targeting. That’s one of the most common use cases folks are considering, but brands should also consider other use cases like measurement and attribution.

YY: Can you tell us more about the Privacy-Enhancing Technologies working group (PETs), its goals, and how IAB Tech Lab plans to facilitate post-cookie and privacy-first addressability for audience activation and measurement?

RL: The Privacy Enhancing Technologies or PETs working group within Tech Lab is focused on educating and driving awareness of PETs in the digital advertising ecosystem. The PETs working group also develops standards and stewards open-source technical solutions.

We’ll need a portfolio of privacy-safe addressability solutions, and advancing the efforts around PETs specifically is one piece of that. Updating industry taxonomies and publishing standards for Data Clean Rooms, which we discussed just launched last month. At this time, the group is inviting the digital advertising community to participate in developing these PET standards and solutions to facilitate the cookiepocalypse and privacy-first addressability for audience activation and measurement.

YY: What feedback are you receiving from those participating in the working group?

RL: Well, the two pieces that were launched last month were born from the PETs working group. The feedback from the working group was that we need to set a real baseline, which is why the Data Clean Rooms: Guidance and Recommended Best Practices document came first. We have to establish a baseline understanding of something like a data clean room for us to be able to further these types of conversations.

YY: What other plans does IAB Tech Lab have in store beyond 2023? How will the digital advertising sector evolve over the next few years?

RL: Tech Lab is committed to developing the foundational technology and standards that enable growth and trust in the digital media ecosystem. In 2023 specifically, some key focus areas for us are consumer privacy, addressability, and PETs.

We are expanding the Global Privacy Platform or GPP to provide a comprehensive and secure consent signaling framework across multiple jurisdictions. We are also establishing a data subject rights signaling framework, providing the industry with a more consistent way to communicate across the digital advertising supply chain when consumers are exercising their other rights like their right to access, delete and modify their data.

We are also working on an Accountability Platform, a technical audit framework, to ensure adherence to the preferences set by the users at the digital properties they visit. 

Beyond 2023, Tech Lab will continue to focus on solutions for brand safety and ad fraud, identity, data, consumer privacy, ad experiences, measurement, and programmatic effectiveness.

We expect that the digital advertising sector will continue to evolve over the next couple of years. Tech lab is going to remain at the forefront of developing the technical standards to ensure that the industry moves forward in a sustainable but also a privacy safe manner.

The post Solving the Long-tail’s Addressability Issue appeared first on AdMonsters.

It is not just another ETL (Extract, Transform, and Load) machine. And it needs to have specific capabilities that qualify a product or service as a Data Clean Room. The phrase stems from the industrial manufacturing facility concept, where a clean room was a controlled area to minimize contamination and maintain the integrity of the product. 

In that sense, a Data Clean Room can be defined as an environment that minimizes exposure of personal data and maintains the integrity of the individual’s privacy. But that’s easier said than ‘defined or understood’. 

Understanding Data Clean Rooms

Over the last two-three years, there has been an increasing interest in the industry to deploy first-party data sets for advertising purposes for targeting audience segments. Data Clean Rooms have emerged as one of the solutions to enable first-party data for several marketing and advertising use cases.  

Many providers offer Data Clean Room services ranging from independent startups like Infosum, Habu, Decentriq to established players like Amazon Web Services and Snowflake and even large publishers and walled gardens offer clean room products on their own platforms. 

Earlier this month, the IAB Tech Lab released its Data Clean Room Guidance and Recommended Practices, a definitive guide to understanding Data Clean Rooms. The guide highlights:

• What they are, and what are the common capabilities expected of a Data Clean Room product or service
• What are the potential applications for addressability and activation, insights and enrichment, and measurement and attribution
• How do they work outlining roles and different operations performed in a Data Clean Room
• What are the data privacy, security, and governance controls you should expect from a Data Clean Room
• What constraints and limitations should one expect when engaging in a Data Clean Room?
• How can you select the one that is right for your needs?

Utilizing Data Clean Rooms

While it is exciting to see the industry adopt a new privacy technology, working with multiple providers is challenging due to the increased cost and friction of preparing, managing, and extracting data and outputs from differently set-up Data Clean Rooms. And that is not all; using the outputs with different business partners also poses similar challenges, e.g., targeting an audience segment through different ad serving systems, e.g., SSPs and DSPs.  

In conjunction with the Data Clean Room Guidance & Recommendation Guide, IAB Tech Lab launched the Open Private Join and Activation (OPJA) specification, the first in a series of (upcoming) Data Clean Room Interoperability standards. 

OPJA is an operation designed for finding overlapping audiences between buyer and seller data sets so that the buyer can target those audiences at the seller’s digital properties via programmatic supply and demand side ad serving systems. 

OPJA deploys security and privacy technologies to accomplish three key design goals:

• Security of personal information
• Privacy of individual identity
• Privacy of audience membership

To achieve these goals, OPJA describes two components:

• Matching system defines the input and output structure and formats and matching techniques using well-established methods that leverage privacy technologies — Private Set Intersection or Trusted Execution Environment
• Activation Protocol defines the encryption labeling techniques and encryption protocols and how the publisher and advertiser should use the resulting outputs of the matching system

The security and privacy requirements do not end with the data clean room components, and the design goals must be maintained while using the outputs in the activation systems. OPJA lists potential privacy and security threat scenarios and design requirements for activation systems to preserve the three design goals.

Looking Into The Future

This is just the beginning of IAB Tech Lab’s work on Data Clean Rooms. We will develop more interoperability specifications for other use cases, e.g., measurement and attribution.  

IAB Tech Lab will also develop extensions to other standards for deploying Data Clean Room outputs to preserve privacy and security design goals while using the outputs. 

As the use of Data Clean Rooms matures, the industry needs to come to a  consensus about how Data Clean Rooms operate and develop a collection of canonical use cases and standards for interoperability among the participants engaging in a Data Clean Room. 

To learn more about the Data Clean Room Guidance & Recommended Practices and the Open Private Join & Activation (OPJA) Specification, please click here .  Both releases are available for public comment until April 17, 2023.

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