While external data signals are dwindling, data collaboration and Clean Room technology have become pivotal for maximizing monetization strategies. To gain insights into the transformative approach for publishers, we spoke with The Globe and Mail’s commercial data optimization team. They shared their journey and strategies, leveraging collaboration around customer data. 

In the first part of our interview, Kabil Rahaman, the Head of Data Optimization, explains how data collaboration is integrated into their strategy.

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How did The Globe and Mail integrate data collaboration technology into your existing data infrastructure?

We’ve undergone significant changes in the past few years, with a focus on building around our data warehouse. One of the main reasons we chose Optable as our partner is because the process was relatively easy. Leveraging Optable's connection to our data warehouse made it a no-brainer for us to develop that capability.

Are there specific metrics you use to measure the impact of data collaboration on your operations and revenue?

However, focusing solely on revenue provides a limited viewpoint of our impact. To gain a more comprehensive understanding, we look at our interactions with partners and the discussions we have. These conversations are a valuable starting point for measuring revenue opportunities and tracking our progress.

How do you see data collaboration and Clean Rooms technology influencing your data monetization strategy?

Data collaboration is viewed as an extension of our data strategy, enabling partners to bring their data to The Globe's environment for media investment planning. The key metric measured here is the financial success of whether we've matched an audience, targeted a specific audience, or used insights from that match to plan a campaign. 

First-party data is essential for successful data collaboration and activation. Can you describe your journey in building your first-party dataset?

The Globe and Mail’s strategy began over ten years ago. Unlike many digital news media publications that chase clicks and traffic, we focus on providing high-quality news journalism behind a paywall. We believe in a balanced value exchange for readers, whether they pay and subscribe or not. The goal is to nurture a reader’s journey from being anonymous to registering. We collect data at all stages of this journey, ensuring a fair exchange for the news and media consumed. This approach helps us better understand readers' preferences and connect them with better advertising.

What questions do you ask your advertising partners to help qualify and set them up for success in data collaboration?

The Globe and Mail follows a readiness checklist that includes having a first-party data strategy and agreement from key stakeholders (IT, Security, Legal), including an executive sponsor. The presence of a person willing to take responsibility and be accountable for the success of the program is crucial. 

Media agencies, as data controllers or data stewards, face the challenge of managing the expectations of multiple stakeholders while handling sensitive customer data for advertising. Experience in uploading customer lists to platforms like Meta or Google makes collaboration easier.

What makes data matching successful from a publisher's standpoint?

Success in data matching stems from mutual learning opportunities between two customer data owners. Our team is very obsessive about the relationship their readers have with ads, helping advertisers connect with receptive readers and audiences. I see our success in giving brands the chance to connect with our readers in a meaningful way and providing learning opportunities for both the brand and The Globe through customer list matching.

Tell us about your test-and-learn approach. How does it look?

The test-and-learn approach begins with curiosity, aiming to validate an assumption or theory for a partner. It’s a highly collaborative process designed to produce structured tests to address business challenges. We use the acquired insights to improve campaign planning and segmentation.

What are your tactics in case the audience match with the advertiser is not significant?

As an optimistic person, I believe that a low audience match provides an opportunity for customer growth and acquisition. In this scenario, tactics include creating look-alike models, leveraging content insights, and excluding current customers.

It's important to test different tactics and learn from the results. Compare these tactics with a publisher's first-party audience to determine if there's a lift or difference in how the publisher defines their own audience compared to how you would match.

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About Globe Media Group

Globe Media Group is a media and marketing company that empowers advertisers with solutions and content to influence ambitious Canadians. As the advertising arm of The Globe and Mail, Globe Media Group’s offerings are end-to-end across multiple platforms, including digital, video, podcasts, app, newspaper and magazines, as well as custom content and special events. Globe Media Group provides unparalleled access to influential audiences within trusted, premium environments, reaching 20.5 million monthly unique visitors through Globe Alliance—a premium digital network of the world’s best news, business and lifestyle sites. Globe Media Group also connects advertisers to 2.6 million weekly readers of The Globe and Mail, Canada's most trusted news source. Each day, The Globe engages Canadians with award-winning coverage and analysis of news, politics, business and lifestyle topics.

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Identity resolution and ID graphs have become critical components for publishers aiming to optimize their data management strategies. However, building and maintaining an effective ID graph comes with its own set of challenges. This blog post will explore three common challenges that heads of data at publishing companies face and offer practical solutions on how to overcome them.

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Challenge 1: Adding New Data Sources

The Problem

Adding new data sources to your ID graph can be a difficult and time-consuming task. Customer Data Platforms (CDPs) often lack flexibility when it comes to ingesting new data, especially from on-site sources. Additionally, second-party data typically requires extensive cleansing and normalization before it becomes usable, further complicating the process.

The Solution

Partner with technology providers that offer managed services and expertise in data onboarding, cleaning, and normalization. Seek out consultative partners who can help you integrate and test second-party data effectively. These partners should provide tailored approaches to your data ecosystem, ensuring that your ID graph meets the specific needs of various departments within your organization.

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Challenge 2: Maximizing Usability

The Problem

Making your ID graph usable for different use cases is another common challenge. Graphs may need to take different shapes depending on the use case, and different identifiers require various rules for normalization and linking. Additionally, some IDs may need special treatment during the normalization process, and adjusting relationships between IDs can be cumbersome. Determining the right approach can be costly as your engineering teams have to spend lots of time testing and learning custom solutions.

The Solution

Work with platforms that offer flexible tools for graph building and shaping. These platforms should make it easy to ingest new data sources through their SDK or your cloud storage. Look for solutions that allow you to easily change how graphs are structured to suit different use cases. This flexibility will maximize addressability for advertising and ensure that your graph remains useful across various organizational functions, such as subscription marketing automation or evaluating audience lifetime value.

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Challenge 3: Adding New Integrations for Activation

The Problem

Effective orchestration of your ID graph requires seamless integration with multiple endpoints for ad activation, marketing automation, and internal business intelligence. Data warehouses-only solutions aren't well equipped for this as they typically require multiple steps for data to leave the warehouse before being used in another system. Establishing these connections can be complicated and time-consuming, making it difficult to act on your data promptly.

The Solution

Utilize an audience management platform that offers purpose-built connections to activation endpoints for various publisher use cases, including advertising, marketing, and business intelligence. The key here is the availability and speed of custom integrations. Platforms that can quickly establish these connections will enable you to leverage your ID graph more effectively, ensuring that you can act on your data in real-time.

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Conclusion

Building and maintaining an ID graph is essential for modern publishers, but it doesn't come without its challenges. By focusing on platforms that offer flexible data ingestion, partnering with consultative experts for data onboarding, and using audience management platforms with purpose built-tools and quick integration capabilities, you can overcome these common hurdles.

Ready to optimize your ID graph? Book a call with our expert team today to explore how we can help you streamline your data management and boost your publishing efforts.

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‍In our previous article, we explored the concept of purpose-built identity solutions and the new pivotal role of ID graphs. As we dive deeper into the nuances of identity solutions, it's crucial to understand ‘ID Bridging’ and its impact on extending both direct and programmatic revenue streams. 

As we shift away from reliance on cookies & third-party identifiers publishers need comprehensive and scalable solutions that help them link, or ‘bridge’, their first and second party data sets as well as create interoperability with their demand partners and other activation channels. The most important dynamic here is that publishers maintain control and transparency so that these practices are in line with their advertisers and demand partners standards.

"ID Bridging is an important capability in our tool set as we continue to enter a world where free & widespread identifiers are less accessible & usable across our ecosystem. Being able to bridge together IDs from our various internal datasets as well as work with second parties ultimately helps to make cookieless environments more addressable to our advertising partners." said Paul Bannister, CSO of Raptive. "Working with Optable has helped us to scale and refine this practice across our large network of sites. Their ability to work as a true SaaS provider and give us the controls we need across various sources of identity helps us to both test and optimize our addressability and work closely with our demand partners so that we are doing this in a way that is acceptable to their standards."

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Understanding Addressability Evolution and the Role of ID Bridging

The ad tech system was engineered to provide marketers with rich datasets to target audiences with paid advertising, having made ‘Identity’ and ‘Addressability' synonymous for a decade. However, trends in data privacy are moving us away from third-party cookies and app-based identifiers.

Now, publishers face a challenge with these shifts due to lack of investment in understanding their audience data. They must pivot as linking user identity and addressability becomes increasingly difficult. Many publishers find that they lack the data science and engineering expertise to build & scale high quality and reliable identity graphs without the help of specialized platforms. To keep their data-driven ad products in line with these changes publishers are reviving concepts like contextual advertising and introducing new ones like identity cohorts and enriched audiences. As a result, traditional ad products are evolving, and publishers need to reassess their Data Management platforms and tech stack to maximize growth in both direct and programmatic monetization channels.

Amid signal loss in the programmatic market, ID Bridging was introduced to maintain addressability. It is the privacy-safe process publishers use to match their first-party audience identity signals with static signals from second parties to grow their understanding of their audiences. This subset of identity resolution is key as digital advertising heavily relies on these signals. 

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How is ID Bridging used for programmatic advertising?

A major trend in the response to the aforementioned changes in addressability and the need of linking identities is the creation and adoption of Alternative IDs. In programmatic advertising, an ‘Alternative ID’ refers to any identifier other than third-party cookies that is used to track and target users across websites and devices. Many different ‘Alternative ID’ projects have come about in recent years and many of them vary greatly depending on the companies behind them and the geographical regions they operate in (due to variation in privacy laws).

Some of the major ones include The Trade Desk's UID 2.0 project, ID5, and LiveRamp's RampID, but there are many more, and they continue to pop up in new and different commercialized forms as companies respond to the changing market. As a result, publishers are finding ways to use ID Bridging to actually enrich the bid requests they are sending to programmatic advertising platforms with Alternative IDs via a process called Bid Enrichment. 

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This process helps publishers increase the chances of an ad buying platform finding their inventory to be addressable and, therefore, maximizes their “ad yields.” By working with a first party DMP, publishers can not only implement this solution, but they can also test and optimize different identifiers. Some legacy programmatic data providers have offered other methods of doing Bid Enrichment by loading all possible Alternative IDs within their tags; however, this has limitations for non-web channels, creates latency across web pages, and does not allow for insights and optimization into what Alternative IDs are providing the best yield.

It is important to note that the IAB Tech Lab is working on new standards to support these changes. These standards will help give better guidance to publishers on how to best implement ID Bridging and Bid Enrichment, as well as to Marketers on the best practices for measuring success when their Publisher partners and ad-buying platforms are using these solutions.

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How can ID Bridging help improve directly sold inventory?

Beyond programmatic advertising, ID Bridging also plays a critical role in enhancing directly sold inventory. By increasing addressability, ID Bridging helps media owners and publishers grow revenue from the inventory they directly sell to advertisers.

The addressable advertising ecosystem is likely to continue to be in flux in the near future, given ongoing regulation in the US, Canada and other markets. Going forward, it is incredibly crucial for publishers to invest in a first-party DMP that can support addressable advertising in the many forms it will take. This means investing in a DMP that natively supports first-party ID Graphing and enables Bid Enrichment via Alternative Identifiers.

Integrating second-party data providers into the data strategy can be a big leap. By working with identity and data providers, publishers and media owners can grow authenticated users and enrich the existing data with additional traits and attributes, leading to a more detailed understanding of their audience. 

Besides deeper insights, enhanced data also gives the data teams more power when it comes to creating and leveraging audience segments. As a result, publishers can boost revenue from direct-sold campaigns by offering advertisers access to more defined and valuable audiences. Ultimately, partnering with second-party data providers not only improves audience insights but also drives better ad performance.

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Optable Enables Transparent ID Bridging

The impact of linking identifiers on a publisher's ad business depends on how providers use the IDs. If handled responsibly, ID bridging can enhance ad inventory value and bring revenue. Yet, if providers prioritize short-term gains by adding undisclosed identifiers, it can lead to ID spoofing and challenges with their advertising partners. Industry standards and working groups, such as IAB Tech Lab, are continuing to give guidance on ID Bridging practices to ensure ecosystem integrity. Optable, as a member, proactively develops and complies with these standards.

At Optable we work under a SaaS model, prioritizing transparency with our clients to ensure that agreed-upon identifiers are used in ID Bridging. This approach provides publishers with visibility, control, and the ability to test & optimize different ID providers & datasets to determine the most effective solution for their inventory. On our platform, teams can complete their dataset with identifiers including Universal ID 2.0, Criteo ID and Ramp ID and leverage hashed data from providers such as Experian, True Data, and ID5.

Reach out to us at info@optable.co to learn more about how you can increase addressability & grow revenue using Optable’s Audience Management Platform.

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Québecor has leveraged data collaboration to achieve remarkable success in digital advertising. We sat down with Sasha Audet, Audience and Programmatic Solutions Supervisor from Québecor, to discuss their journey in digital advertising, how they leverage data collaborations with partners to grow their advertising business and their plans for TV ads. Explore insights from Québecor’s experience in our latest interview hosted by Ioana Tirtirau, the Director of Customer Success at Optable.

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What specific objectives are you aiming to achieve with brands and agencies when it comes to data collaboration?

Our primary objective is to provide brands and agencies with deep insights into their audiences through our data. We've worked diligently with Optable to map all our data. Therefore, when a client comes in, we can match their data with ours. This allows them to see their media consumption within Québecor’s ecosystem. These insights add great value for our clients, and we can activate this data directly, employing multiple strategies to engage with the matched audience.

How do you use data collaboration to grow your advertising business today?

We identified new opportunities with data collaboration, particularly in TV advertising. We plan to use data collaboration in our addressable TV business. By integrating Québecor’s digital environment with TV insights, we can better understand real TV consumption patterns. This helps us identify what TV shows our audiences watch, the times they tune in, and the frequency of their viewing habits. These insights enable us to enhance our addressable TV business by providing more targeted advertising solutions.

Can you give us an idea of how you arrived at this point where you have all these interesting insights to show advertisers? How did you go about preparing your data set?

We invested several years in gathering and preparing all this data, ensuring everything complies with legal standards. We tagged all our properties and used first-party data to ensure accuracy and relevance. After years of hard work, in collaboration with Optable and our teams, we made our gateway to accessible and comprehensive data, enabling us to offer these valuable insights.

Does interoperability play a role for you? Is it important in your first-party data strategy?

Absolutely. Interoperability allows us to bridge different ad tech and data stacks, making it a single entry point for Québecor. This capability, enabled by Optable, allows us to work with a variety of data sources and activate data through advertising seamlessly, regardless of whether our partners are existent Optable users or not.

What have you learned from working directly with advertisers, and how does that change your approach in the market?

Advertisers have high expectations and expect the platform to be turnkey. After working with many clients, we learned that successful collaboration requires significant effort from our side, the advertisers, and the technology providers. These experiences taught us a lot, and we were able to streamline our processes and improve our approach. Now, we have established a great process for all parties involved.

Since you mentioned the process, how is it built within your organization? What is the structure between sales and ad operations for a successful product launch? 

We clearly define the roles of each person involved. The critical part is our subject matter experts. They are salespeople and experts in the product that work directly with clients. They play a crucial role, enabling us to minimize the time spent on learning from every individual involved in sales.

How do you qualify your advertising partners for the collaboration? 

We have business rules in place to qualify clients, ensuring they are ready for collaboration. Our subject matter experts are involved in the qualification process, helping our leads to learn and to work with the product. Experts use specific rules to determine a client's readiness. This process requires substantial preparation and alignment with our technology, especially during the initial adoption phase. Since it is a partnership, the partner must also bring the upfront investment with us so it leads to long-term, successful cooperation. 

Ultimately, we aim to provide long-term, evergreen business for our clients with Québecor. There is a certain amount of work at the setup, but once everything is rolling, there is not much intervention.   

Can you share your experience with onboarding advertising partners to prepare a successful campaign?

The key to a successful partnership is thorough preparation. Before we begin, clients must ensure that their legal, marketing, and other teams are onboarded and aligned. Once we start, we focus on optimizing the campaign, making adjustments as needed, and managing expectations regarding performance, as clients have expectations tied to the third-party cookie world. Both internal and external education must be in place for success. Ultimately, the collaborative effort ensures the campaign runs smoothly and achieves its goals.

Québecor’s strategic use of data collaboration and insights sharing shows how effective data management and targeted advertising can drive growth and success in the digital advertising landscape. We look forward to hearing more about innovative projects from Québecor in the future.

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About Québecor

Québecor, a Canadian leader in telecommunications, entertainment, news media and culture, is one of the best‑performing integrated communications companies in the industry. Driven by their determination to deliver the best possible customer experience, all of Québecor’s subsidiaries and brands are differentiated by their high‑quality, multiplatform, convergent products and services.

Québecor is headquartered in Québec and employs more than 11,000 people in Canada.

A family business founded in 1950, Québecor is strongly committed to the community. Every year, it actively supports more than 400 organizations in the vital fields of culture, health, education, the environment, and entrepreneurship.

As data privacy regulations evolve, publishers are centralizing data within warehouses, but is it enough for data monetization? With DMPs falling short, the future lies in purpose-built applications that enhance activation, streamline audience building, and support complex identity resolution and collaboration. Dive into the challenges and opportunities for sustainable revenue growth in this privacy-centric era.

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.

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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.

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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.

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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?

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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.

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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.

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The article was originally published on August 7, 2024, on AdMonsters.com.

With the ever-evolving ad industry toward privacy and security, publishers are seeking innovative ways to maximize their data monetization strategies. As it will soon be impossible to rely on traditional tracking technologies, the need for robust solutions to identify users becomes more sharp. Identity Solutions are stepping up to help publishers with user identification in a fragmented ecosystem.

In our previous article, we explored the concept of an ID Graph and its practical applications. An ID Graph is the result of the process known as Identity Resolution. To complete this process, publishers use a set of operations involving the collection, processing, and linking of IDs to establish unique user identity groups at different levels: individual, household, trait and event. In simple terms, it allows a publisher to determine or infer what individual is visiting its websites or apps, whatever device, email, or IP address he comes from, therefore targeting him with a relevant ad.

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CDPs Are Not Enough Anymore

Initially introduced in Customer Data Platforms (CDPs) and used by marketers, ID Graphs has seen adoption by publishers, reflecting the changing dynamics of the industry. The move toward first-party data and the necessity to introduce new advertising and monetization strategies have driven publishers to opt for new ways of building their audiences. Publishers have utilized CDPs to consolidate all available data, create user profiles to increase the value of their ad inventory for advertisers, and enhance targeting capabilities. However, the limitations of CDP capabilities in real-time data processing, basic identity resolution and segmentation are insufficient to support the complex digital ecosystem where users navigate today and also add additional cost to publishers monetization stack.

As a result, ID Graphs now transcend their original CDP scope in more intricate systems, evolving into complex solutions and integrating into platforms, where organizations can securely collaborate around user data and seamlessly activate it. This expansion unlocks new possibilities for publishers, including those in broadcasting, TV networks, and audio platforms, to monetize what they have on the table.

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Accurate Identification for Better Advertising

Optable’s ID Graph is an example of such an advanced solution, providing highly interoperable environment that unlocks many use cases within the sole platform such as audience segmentation, harnessing insights, audience activation, data collaboration, programmatic bidding with enriched IDs and Privacy Sandbox applications.

To construct an accurate and rich identity graph, Optable groups identities by running several critical operations during the resolving process: 

• Auto-normalization to identify the type of ID and cleanse it. For example, spaces are removed from email data, and the format is adjusted according to the specific ID requirements.
• Deduplication and resolution across all IDs, including resolving IPs which CDPs usually do not do.
• Backend hygiene to prevent over-linkages or extra large clusters, ensuring accuracy and delivering precise targeting
• Setting up the numbers of associations to control bid enrichment application, which is not a use case in CDPs
• Configuring the shape and size of the graph. A larger scale implies lower accuracy, similar to look-a-like models.

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Different Data Types for Improved Identification: Deterministic And Probabilistic Identifiers 

ID graphs are not made the same. They can be built and scaled differently depending on the type of data matching used. There are two following ways to do that:

• The deterministic method of matching is based on data explicitly given to users; therefore, those are identifiers that are most certainly linked to the individual: names, emails, and telephone numbers provided by the person. This approach is more accurate, but it is harder to scale as these data are more challenging to acquire or enrich. The alternative solution for the publisher here is to use the existing first-party data networks of deterministic identifiers. Examples include Experian’s LUID, TransUnion’s TUID and LiveRamp’s Ramp ID. While these centralized ID providers provide a strong foundation for creating an ID graph they oftentimes come with limitations on how they can be used and therefore should be thought of as part of our overall solution.

• The probabilistic method is a broader concept based on predicting user behavior and plausible events similar to those noted for similar IDs originating from identifiers on the household level. Probabilistic IDs are the product of analyzing and stitching different cross-channels and devices' data signals. The method is based on similarities and probabilities and, consequently, is not 100% accurate. However, one of its advantages includes the possibility of measuring a large number of probabilistic events and scaling the ID Graph faster.  

Companies like ID5 and Predactiv offer probabilistic IDs. These providers process signals such as device IDs, IP addresses, behavioral and contextual data to infer the person’s identity and increase data matching rates.  

These two methods created quite a buzz in the industry, arguing that deterministic matching is the right and only way to match data accurately. However, the answer lies in the golden balance, where two methods are combined in different proportions. Here, the publisher must find its own ratio between accuracy and scalability. 

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Enhancing Ad Targeting and Boosting Revenue

In the cookieless environment, with addressability being continuously undermined from signal, identifying and targeting users is an important goal. Publishers can significantly increase their revenue by using purpose-built identity resolution to create comprehensive identity graphs. There are several reasons for this.

First, consolidating and linking data points together on different levels allows publishers to identify and reach more individuals and households within desired customer groups. With a large number of identifiers and licensed data providers evolving in the market, publishers are also able to amplify ad addressability by enriching audiences and scaling graphs. 

Second, by injecting deterministic and/or probabilistic IDs into their database, media companies can activate programmatic ads and achieve higher bid density. Thanks to enriched audiences in the bid stream, publishers can increase revenue.

Third, by working with a third-party data provider, publishers can resolve their identity graph to partner datasets to create new addressable audience segments. These new segments, such as demographics like gender, age, or household income, can then be packaged and sold to advertising partners for ad activation.

Lastly, a growing area of monetization growth for publishers is data collaboration. Data collaboration comes from working directly with advertising partners to safely match data in for both audience activation as well as sharing insights about audience traits or purchase behavior. This helps publishers grow their revenue by creating better plans with their advertising partners and offering unique measurement solutions which ultimately leads to bigger commitments and higher CPMs.

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Identity Solutions are a key part of Optable

Our Identity Solution is designed to help media businesses adapt to the rapidly changing digital advertising realm. Decision makers need to consider comprehensive identity solutions as a new alternative to third-party cookies to deliver performant targeted campaigns and boost revenue in both direct and programmatic advertising.

Optable offers a comprehensive approach to identity resolution and developing customer ID Graphs, enabling our clients to enhance audience engagement and revenue through better addressability and personalized ad content. By establishing a first-party identity graph and processing second-party data, Optable aims to improve addressability across cookieless environments, enrich audience insights, and unlock new revenue streams through data collaboration. Ask for a demo to learn more.

As ad tech undergoes radical transformations, publishers have no choice but to adopt proactive strategies to support their advertising business. This is especially crucial, considering that their revenue depends more on digital channels each year. Therefore, media companies must evaluate new solutions to comply with privacy changes and maintain revenue from ads. One powerful answer to the industry shifts is the adoption of ID Graph, a Swiss knife for data management and activation.

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Unifying Your Audience View: The Power of ID Graph

An identity graph, or ID Graph, merges data from various touchpoints to create a comprehensive customer view. This centralized dataset includes interconnected data from different channels, providing valuable insights into the audience and helping publishers recognize or infer who is on their website. For broadcasters and audio platforms, this means understanding your viewers' and listeners' behaviors across devices and connecting those insights back to the broader audience strategy.

Types of Identifiers that are commonly used in a unified customer graph:

1. Individual IDs
   1. Hashed Emails
   2. PII (name, email address, phone number)
   3. Customer IDs
   4. Cookies
   5. Audio ID
   6. MAIDs, GAIDs and IDFAs
   7. Universal IDs

2. Household IDs
   1. IP addresses
   2. CTV IDs
   3. ZIP code
   4. Device IDs

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A variety of data sources could be used to build and optimize an ID Graph. The most essential sources include first-party audience data from CRMs, data warehouses, and other cloud-based customer platforms, as well as usage data collected from their owned properties through SDKs, streaming data collection, and other methods. For audio platforms and TV networks, data from streaming platforms, set-top boxes, and listener/viewer interactions can significantly enhance the accuracy of custom ID graphs.

Incorporating authenticated personally identifiable information (PII) makes the graph more accurate. The most common approach is to obtain consenting browsing data such as IP addresses and first-party cookies. However, publishers often partner with other identity data providers due to the limitations of collecting first-party data. The company can opt to collaborate with partners that provide identity data, alternative IDs, or licensed data for audience enrichment.

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Profit-Generating Use Cases That Optable’s Identity Solutions Unlock for Publishers

To demonstrate their value to ad partners, publishers must become experts in their audience data. An ID Graph helps publishers determine effective strategies and enhance their ad services. Let's delve into four illustrative use cases within Optable that can unleash effective data monetization strategies.

1. Unlock revenue by using ID Graph in activation of segmented audiences

Signals come from many places. Merging them simultaneously in real time is challenging for most datasets, but not for a purpose-built ID Graph. Graphs allow publishers to update and expand centralized data from multiple locations. This empowers ad sales and data teams to categorize specific user segments and differentiate their audience across all available sources for targeted advertising.

Insights from segmenting help publishers define unique audiences and communicate their value to advertising partners and make informed data strategy decisions, including planning and activation. Segmentation within a comprehensive ID Graph becomes a game-changer for audience activation. For audio platforms and TV networks, segmenting based on listening or viewing patterns can offer precise targeting opportunities, whether through direct ad placements or programmatic buys.

Publishers should seek interoperability within their ID Graph to make activation seamless and hassle-free. This allows to onboard specific customer clusters to ad platforms. For digital publishers, this means activating audiences directly on websites through SDKs or integrating with ad servers. Consider it as a secret recipe on how to reduce the tech stack and to make the life of your data teams a bit easier. In Optable’s highly interoperable environment, publishers can seamlesslyI activate custom audiences with all of the mentioned options, enhancing marketing campaigns and helping partners achieve their goals. Read our article on interoperability to revisit its importance.

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2. Use ID Graphs to boost partnerships through data collaboration

Over the past few years, publishers started increasingly adopting data collaboration to work with their advertising partners in a privacy-protected way to build & activate data-driven campaigns. The key to effective data collaboration is the use of first-party data. Publishers who have invested in strong identity graphs for their first-party data will create efficiencies in segmenting and creating new audiences, grow their partnerships by sharing deep insights with advertising partners, and maximize their match rates and addressability.

Based on first-party data, data collaboration is a robust tool that drives highly accurate and high-performing advertising, generating profits for all involved parties. Optable as a data collaboration platform amplifies the collaboration capability with the custom-built ID Graphs, analyzing user data before and after matching. Publishers can explore this alternative to create new revenue streams amidst industry shifts.

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3. Maximize the revenue from programmatic deals with ID Bridging

The process of stitching first-party identifiers and signals from other data partners into ID Graph is also referred to as ID Bridging. It helps publishers increase the value of their inventory for buyers on programmatic platforms by increasing the amount of IDs they can share with their demand partners for a specific visitor. Sending the enriched bid request with additional identifiers allows publishers to increase the addressability of their audience to the marketers. The outcome for sellers will be growth in bid density and an increase in programmatic yield. 

When proposing this solution to publishers, Optable prioritizes transparency to ensure our customers have full visibility and the ability to choose which identifiers and data signals are used. Optable also recommends that publishers work with their demand partners to determine exactly how IDs can be used. ID Bridging can be an efficient data monetization strategy for media companies, bringing sustainable revenue growth from building durable audiences and selling valuable ad space to advertisers in the post-cookies era.

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4. Test a new way of advertising through Privacy Sandbox 

While Google decided to hand over cookie deprecation to the end users of Chrome, the Privacy Sandbox remains an efficient advertising solution, aligned with privacy regulations and we anticipate over time that this approach will gain usage not just within Google Chrome but also within other browsers. The Optable team offers publishers and advertisers a working solution to test the new technology for programmatic ads. ID Graph complements the new advertising framework by allowing publishers to onboard their audiences for targeting through the new capabilities of the Privacy Sandbox module.

ID graphs are emerging as a powerful data management tool that unlocks data activation and collaboration use cases, assisting publishers to grow their ad businesses in the industry that prioritizes user data protection. With centralized data and enrichment opportunities, publishers can increase revenue from programmatic ads and direct ad collaborations. 

To learn more about Optable’s Identity Solution, ask for a demo. 

In recent years, changes in consumer privacy regulations have created disruption within the media and advertising ecosystem. Publishers have felt some of the biggest impact, needing to change the way they think about ad monetization so they can continue to respect user privacy, reduce reliance on third parties, and build sustainable revenue growth.

Key to helping publishers navigate these challenges is their data management and collaboration technologies, which enable them to utilize private identity data, extract meaningful business insights, and safely scale data activation.

Optable is the maker of an end-to-end data clean room platform for the advertising industry that integrates with BigQuery and enables audience activation and insights through connections with downstream systems.

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Optimizing data management for monetization

The methods and systems that support audience-focused ad planning, activation, and measurement have undergone a massive shift. As a result, publishers are needing to reevaluate their data strategies, shifting their investments to cloud computing and big data. Unfortunately, a lack of innovation in data management platforms (DMPs) for publishers has stifled revenue growth and created inefficiencies in operations. This lack of ROI in legacy DMPs comes from the following challenges:

1. Platforms can’t deliver insights across known users, anonymous traffic, and ad data. A shift across the media landscape to subscription-based approaches and user consent has meant publishers have an influx of first-party data from their audiences. This change means they need new types of privacy-centric tools to not only manage this data, but also combine it with other datasets to derive insights for both internal business operations as well as advertising partners.
2. Identity management can consume the limited resources of modern publishers and media companies. As media consumption has continued to fragment, identifying your audience to deliver relevant media and advertising has become a struggle. Many customer data platforms (CDPs) and legacy DMPs rely on systems that are too rigid and profile-based. This makes it difficult to understand true audience reach and attributes, and leaves analytics teams with incomplete or error-prone data sets.
3. There’s a lack of privacy-centric data monetization tools and integrations for publishers. Given the rapid onset of privacy changes, many publishers are navigating a reduction in addressability, signal loss, and programmatic revenues. Current technologies lack interoperability with second-party datasets, advertising ID providers, and ad delivery systems to support direct sales growth.
4. Legacy solutions are not extendable and interoperable across a growing cloud ecosystem. Data management solutions have not kept up with innovation in the cloud computing ecosystem. This makes data extensibility into native cloud tools for things like modeling, custom queries and data visualization is difficult without cumbersome data copying. Additionally, most data management platforms lack native functionality with cloud-based clean room capabilities, such as data analysis rules and egress restrictions, across all major cloud providers.

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Privacy-safe addressable ad products for publishers

Optable is a next-generation data management and collaboration platform that is privacy-focused by design and built to harness the power of today's big data and cloud computing ecosystem. To achieve this, Optable focuses on three key areas of innovation.

1. Composable identity
The Optable platform simplifies the complexities of processing and analyzing audience identity data in the age of privacy. By creating a flexible identity system, publishers can shape their audience graph for maximum accuracy and addressability. Optable also makes it easy to connect alternative identifiers such as UID 2.0 or ID5 to your audience data so that you can understand the full spectrum of advertiser demand and maximize revenue. It’s also straightforward to enrich audience data through connecting to second-party data sources, such as True Data.

2. Interoperability focused on monetization
Optable makes monetization easy. It’s built the tools and integrations needed to keep up with programmatic ad-tech ecosystem changes. Audience data is fully interoperable through the Google Cloud ecosystem (and beyond), which means it’s easier to activate through ad servers like Google Ad Manager (GAM) or Google DV 360, or use pre-built clean room applications within BigQuery. This combination means that direct sales and ad operations teams can quickly and easily move from ad partner planning, to audience building and insights, to actual campaign activation.

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3. Audience insights across all known users and event traffic
Audience building and analysis with Optable provides a comprehensive 360-degree view without the massive upfront engineering that’s usually required. By allowing audiences to be created, synthesized, and managed using both known user data as well as anonymous events (such as page visits or ad serving events from GAM), operational teams can glean meaningful insights, service unique requests from ad partners, and optimize campaign performance. For example, in the image below, Optable Insights shows customers key information about the size and makeup of their private identity graph as well as the makeup of Traits across their audience.

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One of Optable’s early customers, a major North American news publisher, developed a digital-first publication strategy in 2017. As part of this they built an ad-sales strategy focused on privacy-safe first-party data monetization. A key pillar to their strategy is data collaboration through Optable. Through this investment they achieved a 9% annual increase in ad revenue in 2022.

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How Optable creates growth and efficiency

Optable on BigQuery empowers publishers to monetize their data assets, even if they lack engineering resources. Optable provides the right tools and integrations so teams can use their data to create ad products. For example, audience data can be exported directly into Google Ads Data Manager for easy activation.

The data clean room is one of the most important technologies in the media and advertising space. The Optable platform gives publishers pre-built applications that are powered by BigQuery data clean room primitives and APIs. These can also easily be extended to power secure data collaboration across other cloud ecosystems using Optable’s ‘Flash Nodes’ and ‘Flash Connectors.’ Flash Nodes allow companies to invite partners to easily onboard their data into a limited version of Optable simply for the purpose of collaborating with that partner; this reduces the friction of setting up a whole new collaboration platform. Likewise, ‘Flash Connectors’ give companies a set of primitives that can be shared with partners who use AWS, Snowflake, and BigQuery so that Optable users can collaborate directly with partners who house their data in those environments without moving any data.

Rather than just focusing on moving data in and out, Optable’s platform is built for a data-warehouse centric world, so publishers can extend their capabilities. Identity graphs can be shaped and custom modeling easily deployed directly in BigQuery. Additionally, the capabilities of the Google Data Cloud enable custom use cases such as data visualization in Looker. To help navigate data security compliance, Optable has built ‘Bring Your Own Account’ functionality to allow customers to utilize a Google BigQuery instance that’s fully controllable.

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Optable partners with Google across many areas, including a new integration with the Google Privacy Sandbox APIs that enable publishers to easily onboard and activate audiences as well as enabling marketers to run campaigns via Privacy Sandbox. An early access program enables these capabilities directly through the Optable platform. You can learn more here.

Later this year, Optable is announcing enhancements to its audience data management and collaboration capabilities, including support for ad serving events through Google Ad Manager and the release of the Google BigQuery Connector to allow for zero-copy partner collaboration.

Optable also recently became a Google Cloud Marketplace vendor, so Google Cloud customers can now purchase and implement Optable’s platform directly. Following the recently announced general availability of BigQuery data clean rooms, the Optable and Google teams are working on an integration to unlock more planning, activation and measurement use cases for the media and advertising ecosystem.

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The Built with BigQuery advantage

Built with BigQuery helps companies like Optable build innovative applications with Google Data Cloud. Participating companies can:

• Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices.
• Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable unified AI lakehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. Click here to learn more about Built with BigQuery

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The article was originally published on the Google Cloud Blog as part of a series showcasing tech companies and data providers that are Built with BigQuery.

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The crisp February air of Toronto welcomed a select group of media & advertising thought leaders to Optable's exclusive summit. The agenda promised deep dives into data strategy, privacy's impact, and navigating the ever-evolving media landscape. And it definitely delivered.

Data Collaboration Takes Center Stage

The opening panel, "How Publishers & Advertisers Are Using Data to Build Better Ad Campaigns in the Age of Privacy," kicked things off with a bang. Data collaboration emerged as the undeniable hero, bridging the gap in a fragmented ecosystem. Panelists from La Presse, The Globe & Mail, and Advance powered by Loblaw discussed their shared journey: adapting data strategies, wielding identity solutions, all while dancing around the ever-changing privacy regulations. The panel was moderating by Optable's own Ioana Tirtirau, Head of Customer Success, who helped the crowd to glean actionable insights that could be implemented within their own businesses.

One key takeaway? It's not just about the tech. "The future of advertising lies in finding the sweet spot where data insights combine to create a better experience for the audience and ultimately create business growth. Data is the interface with which were able to create better advertising partnerships." said one publisher exec. The audience couldn't have agreed more, recognizing the need for meaningful campaigns that respect customer privacy and provide real insights into customers’ wants and needs.

Privacy: The Driving Force (and Opportunity)

Deloitte's fireside chat shifted gears, focusing on the elephant in the room – privacy. Experts dissected the seismic shifts caused by regulations and platform moves, highlighting not just the challenges but also the opportunities. "CCPA, GDPR, Law 25, cookie deprecation – it's all about building trust," emphasized a Deloitte speaker. "And trust generates loyalty & engagement, which is the real gold in this game."

Beyond Trends: The Human-Centric Shift

The summit wasn't just about buzzwords and tech. It was about understanding that data and privacy are inherently human-centric. At its core, advertising is about connecting with people, and in the privacy age, that means that collaboration is key.

The cocktail hour wasn't just a networking opportunity; it was a testament to the energy and ideas bubbling up from the room. From Optable's own data experts to seasoned ad veterans, everyone recognized that the future isn't pre-programmed – it's in the hands of innovative minds who can harness data, respect privacy, and ultimately, rethink and rearchitect the media & advertising ecosystem to be more impactful for audiences and more sustainable for businesses.

Key Takeaways:

• Data collaboration is growing rapidly, with the major cloud ecosystems acting as stewards.
• Privacy regulations create challenges, but also unexpected opportunities.
• Third party cookies are officially on their way out and will create a forcing function to re-think our ecosystem.

Optable's 'State of Data Collaboration' in Toronto wasn't just a glimpse into the future; it was a blueprint for navigating it. Armed with actionable insights and a renewed focus on the human element, data & advertising professionals left the venue empowered to redefine success in the privacy-first era.

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