In response to challenges posed by privacy regulations and AI-driven changes in search traffic, 71% of publishers plan to invest in new technologies. To sustain revenue growth, publishers are investing in AI-driven analytics, customer data management, and identity resolution. As one publisher noted, personalizing content and engaging audiences will be key in the coming year.

But, it’s not all smooth sailing. Publishers are grappling with significant challenges, including privacy regulations and changes in consumer behavior. These factors underscore the importance of diversifying revenue streams. With audience data, subscriptions, and licensing emerging as planned new streams, publishers are laying the groundwork for sustainable growth in an evolving digital ecosystem.

While the digital ad landscape faces headwinds, the coming year looks promising for publishers who are agile enough to navigate these challenges. Publishers who invest in direct deals, audience development tools, and diversified revenue streams are well-positioned to thrive in 2024 and beyond.

For more insights and a look at the full study results, visit the Publisher Pulse report page, and enter your information at the bottom to download your copy.

The post Publisher Pulse: Key Revenue Drivers and Strategic Shifts for 2024-2025 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.

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.

Sooner or later, third-party cookies will no longer be a viable tool to collect user data. The ethics around data accumulation are changing and the industry is working in overdrive to keep up with the times. 

In November of 2021, Google Ad Manager (GAM) aimed to do just that. They launched Publisher Provided Identifiers (PPIDs) which are specific identifiers that publishers will apply to users based on their first-party data. First-party data is more privacy-focused than third-party data and this initiative was launched with that specific idea in mind. 

The question begs, how will this benefit publishers under constantly evolving privacy regulations and big tech privacy updates? 

What Is PPID? 

According to Google Ad Manager, PPIDs are a unique identifier assigned by a publisher to a user. Publishers will share PPIDs with Google’s programmatic demand and this process will help them customize their ads, targeting, and advertising expertise. 

In a statement at the launch of the product, Steve Swan, Product Manager, Google Ad Manager, said, “By helping publishers expand the use of their first-party identifiers to more transaction types, like the Open Auction, our partners will be able to show ads that are more relevant to their audiences, which will increase the value of their programmatic inventory.” 

However, PPIDs are not actually data. They are identifiers related to a user visiting a publisher’s website. 

When the consumer goes onto the website, publishers can share the user PPID with GAM so the ad serving platform can understand which specific ad requests originate from the same user despite cookies being restricted. 

Essentially, the publisher can identify how many times the user logged into their site and what type of content they are consuming. This is how they are able to customize ads and create audience segments and audience solutions. 

To set up PPID, the publisher creates a unique ID for users based on a first-party cookie or a login ID. Afterward, it will put that ID into GAM and choose who to share that data with. Google will hash that ID and pass it through to buyers for auction.

How Do PPIDs Benefit Publishers? 

One of the main pulls for PPID is the commitment to protect consumer privacy. 

The announcement emphasized that “advertisers who will be able to build segments off of the PPID data will not be able to see individualized information or user data. Because PPIDs are unique to each publisher, there is no way to match identifiers or create profiles across sites.” 

Including the aforementioned benefits, PPIDs assist publishers with: 

• Frequency Capping: This helps publishers identify the user even if they log in from different devices. This will support buyer frequency capping and interest-based ads personalization on programmatic traffic, thus creating a better user experience and potentially increasing publisher programmatic revenue. Publishers can also limit the ad views of a single user. 
• Cross-Screen ID: The same ad will not be shown to the same consumer on different devices. PPID will identify the user and show personalized ads when appropriate. 
• Audience Targeting: Using CDPs and DMPs, publishers can use stored data to create unique user profiles that can be pinpointed across devices. This creates personalized audience segments. 

For instance, using Hulu as a practical example, Ad Tech Explained highlights how using PPID, the publisher can track user frequency to an individual ad across their website, app, or on CTV. Since the PPID is tied to a user login it ensures that the user will not see any one ad too many times.

When it comes to segmenting audiences, Hulu can create targets for their audience based on user credentials and how they interact with content on their platform. Using consumer credentials — including name, email, and billing address — publishers can determine household income and spending habits. From content consumption, they can determine whether a consumer is a sports fan or into the culinary arts, and personalize ads based on that information. All of this information will be added to that Unique identifier and audience segment. 

PPID vs. ESP

In March of this year, Google launched another feature enabling publishers to activate their first-party data called Encrypted Signals for Publishers (ESPs). ESPs allow publishers to share encrypted first-party signals with buy-side platforms of their choosing. 

While at first sight ESPs might look like PPIDs’ competition, both have been described as “two sides of the same coin.” Using first-party data, they each help publishers monetize programmatic inventory. Although, each tactic has a different function. 

ESPs help publishers exchange encrypted information from GAM to non-Google buyers as all publishers and buyers do not work within Google’s ad tech ecosystem. While, as mentioned above, PPID communicates audience insights from GAM to other Google products. 

Some say that ESP gives publishers more bang for their buck. With this function, experts say that publishers have more control over the data they choose to pass and to whom they decide to pass it. (With the caveat that it is encrypted before it enters GAM.) 

Each concept is fairly new in practice through Google, but they both offer ways to navigate ad tech’s new world order. 

Will one outweigh the other? Or will both sides of the coin work in tandem to help publishers thrive under new privacy regulations? Only time and putting the systems into practice will tell.

The post What Is PPID and How Does It Benefit Publishers? appeared first on AdMonsters.

Alphabet Soup: Decoding DMPs, MDMs, and CDPs

Data platforms, broadly speaking, are no different. The market offers a variety of data collection software, including data management platforms (DMPs), master data management solutions (MDMs), and customer data platforms (CDPs). All three ostensibly aim to achieve similar goals:

• Gather and manage customer data from various sources.
• Provide richer insights about those customers.
• In their own ways, provide data and insights back to the business. 

But go one level below that veneer of similarity, and the resemblance fades fast. Before adding a data platform of any kind to their tech stacks, business leaders must understand how these solutions differ, the parts of the organization they impact, and the outcomes that should measure against them.

Defining DMP, MDM, and CDP

To understand the fundamentals of the DMP, MDM, and CDP and their desired outcomes, let’s look at Gartner’s definition of each:

• Data Management Platform: A DMP is software that controls data flow in and out of an organization. It supports data-driven ad strategies, such as segmentation.
• Master Data Management: MDM is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency, and accountability of the enterprise’s official, shared master data assets. 
• Customer Data Platform: A CDP is a marketing technology that unifies a company’s customer data from marketing and other channels to enable customer modeling and optimize the timing and targeting of messages and offers.

Side by side, it’s clear that each solution offers its own range of capabilities geared toward specific goals. Still, it’s pretty tricky to compare and contrast based on these descriptions alone (whoever said identifying the right data technology to power your strategic growth initiatives is easy?).

Selecting the best solution(s) for your business means deeply understanding how these platforms handle different yet essential marketing and business tasks. It’s also why well-defined use cases are crucial to the success of any technology selection, implementation, and ongoing utilization.

The Tradeoffs: When To Use a DMP, MDM, or CDP (and When Not To)

The rise of digital channels where consumers engage with brands — on websites, mobile apps, social platforms, etc. — drove the creation of solutions like DMPs, MDMs, and CDPs. These solutions allow marketers (and publishers) to recognize people by their different proxies and stitch them together for a single view of the customer.

While DMPs, MDMs, and CDPs all serve a data unification function for the business, the underlying architecture and practical utilization of each are quite distinct (and, in some cases, not practical for use at all).

DMPs:

DMPs were explicitly designed to improve the quality of programmatic ad targeting and were once a widely adopted and crucial piece of the ad tech ecosystem. These solutions rely on third-party cookies to collect anonymous audience data from multiple sources and bucket that data into broad segments using algorithms and selective methods of identity resolution. DMPs also pass those segments to programmatic buying platforms to inform ad targeting against high-reach audiences.

However, evolving consumer privacy regulations and the demise of third-party cookies means DMPs are going the way of the dodo — even as they race to re-architect and re-invent themselves for this new world.

Some vendors have already announced plans to sunset their DMP offerings, which could signal the beginning of the end for programmatic advertising. It also means companies dependent on third-party data and DMPs for user tracking and targeting will have to re-evaluate their digital marketing strategy and pivot to tools that leverage consented data if they want to survive (and thrive) in the post-third-party cookie era.

MDMs:

MDM software acts as a single source of truth for various types of business data, including consented customer data. Using batch processes to find connections between different data sources and reconcile them, MDMs create a “golden record” of customers and their various attributes: name, location, address, purchases, etc. By pooling data into a single point of reference, an MDM gives teams across the company (customer service, finance, sales, product development, etc.) the data they need to meet their business needs.

However, this so-called golden record is a bit of a misnomer. It’s actually a “golden association” of a lot of data to one master ID with varying degrees of certainty (email matching has a higher degree of certainty than device matching, for instance).

Moreover, the lack of user interface (UI) for segmentation, activation, and analysis is fine for the IT team but not so fine for marketers, publishers, and other business users to extract much value (okay, use at all) from the customer view created by MDM solutions. These teams can’t execute efficiently when forced to wait on IT and other technical resources for the data they need to engage with customers in the moment.

CDPs:

CDPs are the newest player in this so-called data platforms section of the market. The CDP sets itself apart from DMPs and MDMs in two key areas:

1. the activation-first nature of single customer view it provides organizations and;
2. the user-focused interface designed to make the data accessible to marketers, publishers, and other growth-focused teams for journeys, campaigns, and other orchestration programs. 

The first-party data that customers and prospects have consented to drives CDPs.

They use distinct methods of identity resolution to determine when two or more profiles are the same unique individual and then merge those profiles into one persistent profile. Since this consented data is unified and updated in real-time and made accessible via a UI, business users can activate it instantly — in segments, with campaigns and personalization, and via analytics — while achieving better compliance with GDPR, CCPA, and other privacy regulations at the same time. 

Which Technology Is Right for Your Organization

None of the technologies described above is a silver bullet capable of solving all customer data problems. Since each solution varies in terms of its functionality and capabilities, business leaders need to understand the areas in which they excel, the parts of the organization they impact, and the outcomes they are meant to achieve.

Developing clearly defined use cases can help decision-makers narrow in on these critical factors and ultimately select the right solution for their business.

When employed correctly, use cases describe the current state, target outcome, supporting activities, and relative complexity required to successfully reach marketing or business goals. Going through this exercise can help business leaders level set on where they are today and make more deliberate choices about what solution will work best for their organization going forward.

The post What Are the Tradeoffs Between DMPs, MDMs and CDPs? appeared first on AdMonsters.

That’s exactly what attendees learned at our recent webinar — No Third-party Cookies, No Problem: Ranker on First-party Data in a Privacy-safe World — as Dana OMalley, National VP of Sales at Ranker and Lauren Kroll, Customer Success Manager, Permutive, talked about their partnership and how it helped Ranker realize 4X revenue increase YOY and  25% increase in RFP win rate within six months.

Check out the webinar on-demand now:

In this webinar you’ll learn:

• How to identify your audience without knowing their identity
• The value of collecting rich first-party data and how to activate it
• Why you need to safeguard your business for a privacy-first world
• Why a multipronged approach is the best path forward — marrying contextual, first-party data and zero-party data
• The difference between publisher cohorts and FLoC
• Why edge computing is better, faster, safer than cloud computing for processing data in a privacy-compliant way
• How Ranker saw a 4X increase in revenue year-on-year and a 25% increase in RFP win rate within six months of partnering with Permutive

The post Webinar Replay: No Third-party Cookies, No Problem: Ranker on First-party Data in a Privacy-safe World appeared first on AdMonsters.

Now that the third-party cookie is on its farewell tour, publishers are warily giving their first-party data another look, but a big question remains: how do publishers actually sell first-party data marketplaces and garner advertiser interest?

During an AdMonsters Webinar on Nov. 12, 2020, Insider Inc.’s VP of Programmatic Sales Chris Ryan showed how to do that, sharing Insider’s experience bringing its SAGA first-party data product to market and winning over skeptical advertisers. The full webinar, shared here by Permutive, is worth a watch—check out these highlights.

• While first-party data may have trouble getting the scale that third-party data offers advertisers, first-party will help you demonstrate what makes your publication unique in an increasingly crowded publisher marketplace. This is an overlooked benefit—context matters again.
• Getting buy-in from the top for a data strategy overhaul at Insider was less difficult than getting the mid-level people on board—the planners and inventory managers that would ultimately be the most affected.
• Operations and data specialists need to give sales more credit for their smarts. Insider’s salespeople love dorking out on data—it empowers them in conversations with clients.
• Sales guys need to get comfortable saying, “I don’t know.” Believe it or not, that builds trust with advertisers.
• Comparing campaigns head to head, Insider found users being targeted based on affinities were more engaged (and met advertiser KPIs) than ones targeted with third-party data.
• What tech do you need to stand up your new first-party data marketplace? First is a DMP, but next is an internal spot within the publisher to ensure the data is circulating across the organization.

And be sure to watch the full webinar here.

The post Yes, Virginia—You Can Sell First-Party Data appeared first on AdMonsters.