Header bidding has revolutionized programmatic advertising by allowing multiple demand sources to bid on ad inventory simultaneously, rather than sequentially as in traditional waterfall auctions. This competition results in higher CPMs, better ad quality, and greater control for publishers. 

Today, header bidding is an essential strategy for maximizing revenue and improving the user experience. 

In addition, the auction enables publishers to present their ad inventory to several SSPs, ad networks, and exchanges simultaneously before involving the ad server. This creates a more competitive and transparent auction, as demand sources bid in real-time for the same inventory.

But let’s dive headfirst into this bidding auction. All puns intended. 

How Header Bidding Works

Header bidding enables multiple advertisers to bid on a single ad impression simultaneously. This process begins when a user loads a webpage, triggering the header bidding script to send ad requests to various demand sources like Supply-Side Platforms (SSPs), Ad Exchanges, and Demand-Side Platforms (DSPs). Each demand source responds with a bid and ad creative in real-time, after which the highest bid is selected and displayed to the user.

The real-time bidding (RTB) process enables advertisers to bid on ad impressions within milliseconds, helping publishers secure the best price for their inventory while allowing advertisers to reach their target audiences effectively.

Behind-the-Scenes Mechanics

Header bidding operates on a system that involves creating HTTP requests, handling bid responses, and leveraging user data for efficient targeting.

HTTP Request Generation and Processing

When a user visits a webpage, the header bidding script sends HTTP requests to demand sources. These requests, typically formatted in JSON or XML, contain parameters such as ad unit size, placement ID, and user data. Demand sources process these requests and return bid responses with a bid amount and ad creative.

Pre-bid and Post-bid Phases

• Pre-bid Phase: The header bidding script sends bid requests to demand sources, collects bids, and runs an auction to determine the highest bid.
• Post-bid Phase: The winning bid is sent to the ad server. Ad Server then updates the winning bid in the analytics and displays the ad on the ad slot using its ad-serving logic.

The Role of User Data

The user data is part of the bidding process, allowing selection of certain audiences based on their characteristics: age, interests, and behavior. Through cookies and pixels, the bid of demand sources can be adjusted according to the relevancy and the revenue. However, acts like GDPR and CCPA permit data and personal information to be handled responsibly and only with permission, data anonymization.

Key Components of Header Bidding

1. Header Bidding Wrappers

Header bidding wrappers are JavaScript libraries that sit on publisher websites and enable them to connect with demand sources like SSPs, DSPs, Ad Networks, etc. They help in orchestrating the entire auction using the below steps.

• Manage the setup
• Bids collection
• Ads creative placement

Popular wrappers like Prebid.js and Amazon TAM are used to standardize and simplify the integration of multiple demand partners.

These wrappers streamline bid requests and ensure that all the demand sources can participate in the auction. By connecting all the demand sources wrappers improve transparency and competition in the auction process.

2. Ad Exchanges and Ad Networks

Nowadays in adtech, it’s hard to differentiate between Ad Exchanges, Ad Networks, DSPs, and SSPs as companies are doing all types of activities. Some differences in the process are still there. Ad Exchanges & Ad Networks aggregate demand and connect to supply.

• They connect various advertisers and DSPs.
• They aggregate the demand and help in facilitating real-time bidding b/w publishers and advertisers.
• The real-time bidding ensures everybody gets the benefits 
  • Advertisers get the best-performing slot for their ads. 
  • Publishers get the best rates for their ad slots.

Ad exchanges and ad networks work hand-in-hand with wrappers to process bids quickly and efficiently.

3. JavaScript Implementation

Header Bidding relies heavily on JavaScript implementation, it executes bid requests and collects responses within the user’s browser. It’s crucial to implement JavaScript to ensure maximum profit.

• Proper optimization of JavaScript ensures more demand sources can participate, maximizing revenue for publishers
• To ensure a seamless auction, enable faster page load time & reduce latency.

Each component of header bidding like wrappers, ad exchanges, and networks interacts through this JavaScript infrastructure, to drive efficient and competitive auctions.

Header Bidding Architectures

Header bidding can be implemented through two primary architectures: client-side and server-side.

Client-Side Header Bidding: In client-side header bidding, the bidding process happens within the user’s browser. It is executed by a JavaScript wrapper. It sends bid requests to demand partners, then collects bids, and auction the ad slot based on bids and predefined configuration using RTB. While this method provides transparency and control, it may cause latency and scalability issues, as multiple requests must be processed by the browser.

Server-Side Header Bidding: Provided the Client Side Header Bidding issues, server-side header bidding was introduced. It uses a server to perform the bidding. The browser sends one request to the server, which then handles bid requests to demand partners and returns the highest bid to the browser. This reduces latency and improves scalability but can reduce transparency and control over the bidding data.

Tools to Check Behind the Scenes of Header Bidding

Developer Tools

• Chrome DevTools: Chrome DevTools is a fundamental yet crucial tool for monitoring and troubleshooting header bidding processes. It allows tracking of bid requests and responses in the Network tab, identification of JavaScript errors in the Console tab, and performance analysis in the Performance tab.
• Requestly: A browser extension that simplifies ad operations troubleshooting. Requestly intercepts and modifies HTTP/HTTPS requests, allowing you to fix malfunctioning ads, simulate geo-targeted campaigns, and test custom ad experiences. It also supports testing Prebid.js configurations in staging environments to catch issues early.
• Charles Proxy: A desktop-based application that works similarly to Requestly. It can monitor and debug HTTP/HTTPS traffic. It includes features like bandwidth throttling, session recording, and request/response inspection.

Analytics and Reporting Tools

• Prebid Analytics Adapter: Built into Prebid.js, this tool sends auction data to analytics platforms and tracks key metrics such as bid responses and win rates. It helps publishers optimize their header bidding strategies in real time.
• Professor Prebid: A specialized tool for Prebid users, providing detailed insights into header bidding performance metrics like win rates and revenue impact.
• Google Ad Manager: Google Ad Manager is widely used by publishers for tracking ad performance, impression data, and auction outcomes, offering a comprehensive view of header bidding activities.

Maximizing Revenue Through Optimized Header Bidding Strategies

Header bidding is crucial when it comes to generating maximum revenue with the help of better ad placements. Knowing how it works, how to make it faster and more efficient, and which tools to use, ad ops professionals can reach its full potential. Staying updated on best practices will be key to maintaining a competitive edge in the evolving ad tech landscape.

The post Behind-The-Scenes of Header Bidding and How It Creates Better Ad Quality for Publishers appeared first on AdMonsters.

Ad blocking was last decade’s big problem, right? It is no longer seen as an existential crisis; it’s become a back burner issue.

Why? Consumer adoption rates seemed to have slowed down. Mitigation solutions like Acceptable Ads and adblock walls took the revenue-hit sting out. A sense of hopelessness set in as many believed that they could achieve nothing more. Other looming crises—like signal loss and traffic loss—took center stage. It became a minor headache, easily ignored by popping an Acceptable Ads-laced aspirin.

Now, are you ready to take the red pill or the blue pill? If you take the blue pill, the story ends. You return to GAM and believe whatever you want to believe. If you take the red pill, you enter the blocked web, and I show you how deep the revenue hole goes.

The Red Pill

Let’s start with a hard fact. The majority of your adblocking audience is invisible to you. It does not appear in dashboards like Google Analytics, Adobe Analytics, or in-house reports. It doesn’t even show up in existing adblocking analytics. Off-grid.  

This is dark traffic. You can’t see it. But it’s very much there. 

We are talking about big numbers here. On average, 14-21% of a publisher’s total audience is uncaptured and unmeasured dark traffic. Put another way, the adblocking audience you are currently measuring is just 20-30% of what is actually there.

Sounds sinister? Yes and no. Dark traffic comprises regular people (not bots) accessing your website. People who buy stuff and have above-average disposable incomes. They are hidden from view because they are using a new generation of adblocking software that makes them undetectable by existing solutions in the marketplace.

This is great because you have an audience you didn’t know about. But it’s bad because it’s unmeasured and, even worse, unmonetized. 

This new generation of adblocking software doesn’t just block analytics. It also blocks or doesn’t permit a bunch of other stuff that publishers have come to rely upon: Acceptable Ads, adblock walls, cookie-banners (CMPs), and in-house promotions (e.g., newsletter sign-ups). Because this new generation is so ruthless, we call them brutal adblockers.

The Cause of Dark Traffic

When folks in the industry talk about “adblockers” today, they refer to browser extensions like AdBlock and Adblock Plus. Owned by a company called eyeo, they became the dominant force that drove mainstream adblocking adoption from 2013-2019. 

These adblockers are known for being somewhat hospitable to publishers, albeit for self-serving reasons. For example, eyeo set up Acceptable Ads, allowing publishers to run analytics, adblock walls, and cookie-banners. For this reason, we refer to them as soft adblockers.

Although soft adblockers have grown in usage, they have dramatically lost market share. In 2015, AdBlock and Adblock Plus commanded 80%+ of adblocked page views online. Today, it’s in the region of 25-30%. Therefore, this is the ratio of your adblocked audience that will see Acceptable Ads.

What generates the other 70%+? You guessed it: brutal adblockers that cause dark traffic. There are now 700m+ users globally. Much of this has materialized in the years since COVID-19—the open web’s inconvenient truth.

Who Are the Brutal Adblockers?

When I talk to publishers about this, one of the questions I get asked is, ‘Who are the brutal adblockers?’ If eyeo via Adblock and Adblock Plus isn’t ruling the roost anymore, who is?

Well, it’s not so much a single who, but many players. The adblocking software market has become fragmented. Many companies are getting a slice of the action, offering adblocking through various types of products (it’s no longer mainly limited to browser extensions). 

Adblocking software is available through built-in browsers, operating system applications, VPNs, and at the network level. Hundreds of providers exist across these categories, with popular examples being AdGuard, uBlock Origin, and Brave. 

Most of these providers are brutal adblockers, having found eager user bases dissatisfied with the approach and limitations of AdBlock and Adblock Plus.

Collectively, they’re an unacknowledged audience.

The Brutal Adblocker Era

It’s time to acknowledge that we are in a new era. An era where the terms of the adblockers themselves have fundamentally changed. eyeo, through AdBlock and Adblock Plus, is no longer the main stakeholder and intermediary to which publishers can reach a value exchange compromise with adblocking users. Nor are other methodologies, like adblock walls, effective if blocked.

It’s time to think about adblocking differently. Publishers should build a direct relationship with users of brutal adblocking software and reintroduce a sustainable monetization mechanism.

Realistically, micropayments and subscriptions aren’t going to work for most publishers. The only viable option is to reintroduce ads, using resilient ad delivery that doesn’t get blocked. Today, it is possible to do this—to restore a publisher’s ad stack to brutal adblocking users with ads that users find agreeable and user-centric.

That may sound like a contradiction, but it is not. As eyeo has proven with Acceptable Ads for its soft adblockers, users do not have binary preferences: ads or no ads. Instead, they are open to seeing non-interruptive ads if those are the terms presented to them.

Brutal adblocking users are no different. There’s a clear distinction between the software and the people using it. One is not reflective of the other. While brutal adblocking software is extreme to the extent of what it blocks on publisher websites, its users are regular people. They’re not fanatics. They’re teachers, accountants, students, and doctors. Sometimes, they are using a brutal adblocker by default (e.g., it runs on their employer’s network), and sometimes, it’s because they can (spoiler: most people would rather not see ads if that’s an option).

Publishers that can directly control the terms of their advertising experience while preserving their readers’ preferences and trust will win.

The post Dark Traffic Is Costing Publishers 14-21% of Their Revenue – What Is It? appeared first on AdMonsters.

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

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

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

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

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

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

7 Top Data Clean Room Solutions

AWS Clean Rooms 

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

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

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

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

InfoSum

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

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

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

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

LiveRamp (and Habu)

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

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

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

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

Google Ads Data Hub

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

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

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

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

Snowflake

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

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

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

In addition, they have partnerships with Netflix and Snap. 

Optable

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

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

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

AppsFlyer Data Clean Room

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

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

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

Debunking the Myths Around Data Clean Rooms

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

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

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

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

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

AI has already begun transforming various aspects of mobile marketing, from personalized recommendations to predictive analytics. As AI technologies evolve, their impact on mobile measurement and attribution will become even more pronounced. 

AI’s ability to process and analyze vast amounts of data quickly and accurately is unparalleled. In the context of mobile measurement, AI can provide deeper insights into user behavior, helping marketers understand what users are doing and why they are doing it. This capability will enable more precise targeting and personalized marketing efforts, enhancing user acquisition strategies.

The Power of AI in Mobile Measurement

Predictive analytics powered by AI can forecast future user behaviors based on historical data. For example, AI can identify patterns that indicate a user is likely to churn, allowing marketers to intervene with targeted campaigns to retain the user. This proactive approach can significantly improve user retention rates and lifetime value (LTV).

AI-driven automation will streamline various aspects of mobile measurement and attribution. Tasks that were previously manual and time-consuming, such as data collection, segmentation, optimization, and reporting, can now be automated. This not only increases efficiency but also reduces the likelihood of human error.

Automated attribution models, for instance, can dynamically adjust to changing user behaviors and market conditions, providing more accurate and timely insights. This agility will be crucial in a fast-paced industry where trends and user preferences can shift rapidly.

Privacy Concerns and Regulatory Challenges

While AI offers numerous benefits, the rise of privacy concerns poses significant challenges to mobile measurement and attribution. Users are becoming increasingly aware of how their data is collected, stored, and used, leading to greater demand for privacy protections.

Governments around the world are enacting stricter data privacy regulations. The General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States are just two examples of legislation having significant implications for mobile measurement and attribution.

These regulations require companies to obtain explicit consent from users before collecting their data and to provide transparency about how data is used. This shift towards user consent and control over personal data will limit the amount of data available for traditional attribution models, which rely heavily on tracking user interactions across various touchpoints.

The Shift Away From Traditional Tracking

Additionally, changes to Apple’s Identifier for Advertisers (IDFA) and Google’s upcoming Privacy Sandbox for Android are major developments that will impact mobile measurement. Third-party cookies have been a staple of digital advertising, enabling cross-site tracking and attribution. However, with browsers like Safari and Firefox blocking third-party cookies and Google’s Privacy Sandbox for Android planning to remove any personally identifiable information (PII), marketers need to find alternative methods for tracking user behavior.

Similarly, Apple’s introduction of the App Tracking Transparency (ATT) framework requires apps to obtain user permission before tracking their activity across other companies’ apps and websites. As a result, many users are opting out of tracking, reducing the effectiveness of IDFA for attribution purposes.

Adapting to a Privacy-First Future

The mobile app industry needs to adopt new strategies and technologies to navigate the challenges posed by AI and privacy concerns.

With traditional tracking methods becoming less viable, marketers should explore privacy-centric attribution models. By leveraging solutions like incrementality, marketing mix modeling (MMM), and predictive analytics, it’s possible not just to work with aggregated data, but to gain true insights from it. This involves analyzing trends and patterns at a cohort level rather than tracking individual users, thus respecting user privacy while still gaining valuable insights.

First-party data, collected directly from users with their consent, will become increasingly valuable. By building strong relationships with users and encouraging them to share their data willingly, companies can create rich datasets for analysis and attribution. This data is often more accurate and reliable than third-party data, leading to better targeting and measurement outcomes.

Contextual targeting, which focuses on delivering ads based on the context of the content being consumed rather than user behavior, will also gain prominence. This approach respects user privacy by not relying on personal data and can still achieve effective targeting by aligning ads with relevant content.

The Role of AI in Ensuring Compliance and The Future of Mobile Attribution

AI can also play a crucial role in ensuring compliance with privacy regulations. Machine learning algorithms can be used to detect and manage sensitive data, ensuring that personal information is handled appropriately. AI can automate the process of obtaining and managing user consent, making it easier for companies to comply with regulations while maintaining a positive user experience.

The intersection of AI and privacy concerns presents both challenges and opportunities for mobile measurement and attribution in the coming years. AI has the potential to enhance data analysis, predictive analytics, and automation, driving more effective user acquisition strategies. However, the increasing demand for privacy and regulatory changes will require the industry to adapt by adopting privacy-centric attribution models, leveraging first-party data, and exploring contextual targeting.

As the mobile app industry navigates this evolving landscape, companies that can successfully integrate AI-driven solutions while respecting user privacy will be best positioned to thrive. The next 12-24 months will be a critical period of transformation, shaping the future of mobile measurement and attribution practices for years to come.

The post What Is the Role of AI in Mobile Measurement and Attribution? appeared first on AdMonsters.

Things just ain’t the same for mobile. Times are changing, and signals are disappearing.

We recently outlined what mobile marketers need to know about the Android Privacy Sandbox. Now, we turn our lens toward publishers.

Google’s Android Privacy Sandbox isn’t just another update — it’s a fundamental overhaul of mobile ad infrastructure enhancing user privacy, and impacting how ads are served and measured. But as with any ad tech update, every overhaul comes with both opportunity and complexity. This guide aims to break down these changes, offering a balanced view of what publishers can expect — and what they should watch out for along the way.

What’s Really Going On Inside the Android Privacy Sandbox?

Android Privacy Sandbox is Google’s response to the increasing demand for user privacy. It’s designed to create a delicate balancing act of protecting personal data while still enabling effective advertising.

For publishers, the transition requires rethinking how ads are targeted and measured. While Google presents the Sandbox as a solution to the privacy dilemma, it’s critical to assess whether it meets publishers’ needs without introducing new challenges.

Can it live up to the mobile IDs of the past? Is this really the silver bullet it claims to be?

Core Objectives:

Protecting User Privacy: While this is crucial, what happens to data granularity and advertiser effectiveness when third-party access is restricted?

Balancing Personalization with Privacy: Can the Sandbox deliver personalized ad experiences without compromising user privacy? This is the tightrope that the Sandbox attempts to walk — relevance without invasiveness.

Redefining Measurement Tools: The new APIs promise precise metrics, but the transition might come with trade-offs in data richness and complex implementation.

Showdown: Android Privacy Sandbox vs. SKAdNetwork vs. Ad Attribution Kit

Why pit the Android Privacy Sandbox against Apple’s SKAdNetwork and Ad Attribution Kit? Because they all address balancing privacy with effective advertising — but in distinct ways. By understanding these differences, publishers can make smarter choices about which strategies to adopt as they navigate mobile privacy.

The Publisher’s Playbook: Opportunities and Potential Pitfalls

1. Cross-App Tracking: The End of an Era?

The decline of cross-app tracking is more than a simple shift. It forces data collection strategies that could either unlock new opportunities or leave gaps in your data.

2. Ad Targeting and Measurement: New Tools, New Complexities

The new Sandbox APIs promise a lot but also require a leap of faith. Will these tools deliver the precision they claim, or will they leave publishers with a diluted version of what was once possible?

3. Revenue Implications: Walking a Tightrope

The impact on revenue streams is real. While contextual ads and first-party data are touted as solutions, the practical implications could be more nuanced.

Real-World Experiences: Insights from Early Adopters

1. Gameloft’s Strategic Leap: Testing the Limits of Privacy-First Ad Measurement

Gameloft, a mobile gaming titan, has been at the forefront of adopting the Android Privacy Sandbox. Partnering with Singular, they tested the Attribution Reporting API, balancing effective ad measurement with the demands of user privacy. Their journey highlights both the promise and the challenges of adapting to these evolving standards, particularly in maintaining data accuracy and targeting precision.

2. Verve Group’s Bold Move: Redefining On-Device Bidding with Privacy Sandbox

Ad tech innovator, Verve Group, is pioneering on-device bidding through the Android Privacy Sandbox, focusing on the Protected Audiences API. By moving auctions to the user’s device, Verve reduced data transfers, aligning with privacy goals. But not without running into significant hurdles. Their collaborative work with partners like Remerge has been essential in overcoming these technical challenges, from latency issues to complex implementation requirements.

The Realities of Implementation: What Publishers Need to Know

1. Implementation Complexities: The Devil’s in the Details

Implementing these new APIs requires more than a simple update — it’s an extensive reworking of infrastructure. Publishers should invest significant resources into testing and development to ensure these systems work effectively. Expect compatibility issues.

2. Latency: The Hidden Cost of Privacy

On-device processing is a cornerstone of the Android Privacy Sandbox, but latency can become a significant issue, impacting ad delivery, viewability, speed, and efficiency.

3. Data Accuracy: A Double-Edged Sword

Privacy-preserving methods often result in less data granularity. While this protects users, it can also undermine ad targeting precision and measurement, leaving publishers questioning whether the benefits outweigh the drawbacks. Will we still be able to hit KPIs?

Game Plan For Sailing Mobile’s Privacy-Preserving Seas

1. Hoist Your Sails, But Chart Your Course Wisely

Early adoption is key to catching wind and gaining momentum but plot your journey carefully. Don’t drink the Kool-Aid just yet. Thorough testing and validation are necessary before full-scale implementation, ensuring you’re prepared for the uncharted waters.

2. Steer Your Ship with Trusted Crew

Partnering with reliable DSPs, SSPs, and MMPs is crucial for steering the complex waters. Ensure these alliances are aligned, guiding you towards your specific goals — not just drifting the tide of broad industry trends.

3. Keep a Steady Hand on the Helm: Embrace New Standards, But Stay Informed

As you sail through the shifting currents of the Android Privacy Sandbox, keep a watchful eye on the horizon. While the new Attribution Reporting API offers potential, it’s vital to understand what’s being gained — and what might be lost. Stay informed and ready to adjust strategies as the seascape evolves.

Looking Forward: A Cautious Path to the Future

1. Stay Critical, Stay Agile

As the Android Privacy Sandbox develops, keep a close eye on updates. While it promises much, the reality may require agile adjustments to strategies and expectations.

2. Evolve with the Technology, But Manage Expectations

This shift isn’t a survival strategy — it’s about evolving. But evolution is complex and often slower than anticipated. Prepare for a marathon rather than a sprint.

The Android Privacy Sandbox is not a cure-all, publishers need to navigate these changes carefully, balancing new opportunities with potential pitfalls. By staying informed, skeptical, and proactive, you can make the most of this transition — without falling victim to the hype.

Additional Resources:

– Google Privacy Sandbox Documentation

– AppsFlyer’s Guide to Privacy Sandbox 

The post Dissecting the Android Privacy Sandbox: A Critical Guide for Publishers appeared first on AdMonsters.

As Google’s Android Privacy Sandbox gears up for its anticipated 2025 launch, mobile marketers need to stay ahead of the curve. Remerge, a leading Demand Side Platform (DSP), is at the forefront of this transition, collaborating  with Google and other ad tech partners, such as Verve, AppsFlyer, Adjust, and Singular, to ensure a seamless shift. Luckey Harpley, Staff Product Manager at Remerge, sheds light on what this means for the future of mobile marketing and how to navigate this new landscape.

Why Is Mobile Marketing Shifting to Privacy-First Advertising?

The rise of AI and sophisticated machine learning algorithms showcases the benefits of new technologies, but it also highlights the dangers of these advancements. People want more control over how big tech businesses manage their data. The advertising world is moving towards a privacy-centric future and marketers must adapt.

Apple made the first privacy move on mobile with the launch of its App Tracking Transparency (ATT) framework in 2021. Google’s answer is the Privacy Sandbox, a set of APIs to facilitate the selling, buying, and targeting of in-app ad placements, without requiring third-party cookies in Chrome or cross-app identifiers on Android. For Android, this will provide tracking and reporting via its Attribution API, targeting through Topics and Protected Audiences APIs, and data collection and handling via the SDK Run Time.

Why are DSPs Like Remerge Already Working on Solutions for the Android Privacy Sandbox?

It’s important to avoid a situation like the ATT rollout, where advertisers and publishers were left in the dark before its launch and struggled to understand how to run campaigns after it came into effect.

We want to ensure everything is ready for mobile marketers to run privacy-compliant advertising campaigns on Android without experiencing a drastic decline in performance. Android maintained its position as the leading mobile operating system worldwide in the first quarter of 2024, with a market share of 70.7% so this transitional period is crucial for the well-being of the mobile marketing ecosystem.

Does Google’s Decision to Keep Third-Party Cookies on Chrome Change Anything?

Google recently announced that they no longer plan to deprecate third-party cookies on Chrome and emphasized giving users the choice to opt-in to tracking. This update is unrelated to mobile. A similar approach is likely to happen on Android, where the GAID remains intact, and users can choose whether to share this with advertisers. In this scenario, nothing would change for mobile DSPs and their investment into Google’s APIs – the Android Privacy Sandbox would remain an essential framework for privacy-preserving advertising campaigns.

What Has Remerge Tested and Why Should Mobile Marketers Take Notice?

Remerge’s Research and Development team has been working on the Sandbox for over 1.5 years. They’ve focused on testing the Protected Audience API, which will allow advertisers to run retargeting campaigns on Android.

Tests have been completed with Mobile Measurement Partners (MMPs) like Adjust, AppsFlyer, and Singular. This includes developing a proof-of-concept for Custom Audience Delegation, a mechanism required for remarketing in Sandbox. This allows an MMP SDK to add users to custom audiences on behalf of advertisers based on their in-app behavior. Additionally, the first DSP/SSP on-device bidding test was conducted with Verve. These are small steps but important milestones for Sandbox testing, demonstrating that the Protected Audience API and custom audiences mechanisms are working as planned and validating product capabilities.

How Will a Mobile Marketing Manager’s Life Change When the Sandbox Rolls Out?

Advertisers won’t experience a considerable change in the buying process. At Remerge, marketers will continue to share their user data, desired campaign segmentation, and budget with the Account Management team as usual. Remerge will still be able to target users according to activity within an advertiser’s app and run creatives such as static and video. There’ll be no changes to CTR and CPX reporting, and for ROAS reporting, the data will likely have limited dimensionality, focusing on campaign and country-level reporting.

Google and its partners are doing the heavy lifting on the technical setup. Compared to ATT, the Android Privacy Sandbox is not only far more powerful with its targeting capabilities but also much more complex. This is a completely new tech stack with privacy-preserving mechanisms, and while we might see some performance dips initially, the long-term benefits are expected to be significant.

What About User Acquisition (UA) Campaigns?

While the focus has been on retargeting and the Protected Audience API, the Protected App Signals is supporting UA on Android. Although no industry players have made proposals on the Protected App Signals API yet, advertisers should reach out to their UA partners to discuss their plans.

What Can Mobile Marketers Do Right Now?

Advertisers should start finding a partner equipped to run mobile marketing campaigns on Android. Early adopters like Remerge, who have helped shape components of the Privacy Sandbox framework, will be well-positioned to hit the ground running when it launches.

The post What Should Mobile Marketers Know About the Android Privacy Sandbox Launch? appeared first on AdMonsters.

Political advertisers are forecasted to spend over $12 billion across all channels during the 2024 election cycle, marking the highest spend in U.S. history, according to eMarketer. While a significant portion of that budget will go to linear TV, digital advertising remains a critical battleground. 

Election campaigns are turning to advanced techniques like bid shading to stretch their dollars in this high-stakes environment. But what does bid shading mean for publishers? Let’s dig into how this tactic impacts publishers, the challenges it presents, and how to navigate these waters during this unprecedented election cycle.

What is Bid Shading?

Bid shading might sound like some covert operation, but it’s actually a savvy strategy media buyers use in digital ad auctions. Imagine you’re at an auction, but instead of bidding wildly, you have an algorithm whispering the optimal bid in your ear. 

This algorithm analyzes historical pricing data, current market conditions, and the value of the impression to tweak bids just enough to win ad impressions without overpaying. The method is especially crucial in situations like political advertising where budgets, although large, need to be spent efficiently.

Example:

Picture a political campaign aiming to secure an ad slot. Without bid shading, they bid $10 and pay the full amount. But, with bid shading, the algorithm suggests $7.50 based on past data, saving $2.50 while still winning the spot and potentially saving the advertiser 25% on that impression.

The Impact of High Political Budgets on Publishers

With political budgets hitting an all-time high, this influx of ad spend can be both a golden opportunity and a potential headache for publishers. 

The Upside:

1. Increased Demand: More political dollars chasing your inventory means heightened competition, which typically drives up demand and fill rates.
2. Revenue Boost: Those previously unsold ad slots? They’re now hot commodities, filling up quickly and boosting your bottom line.

The Downside:

1. Revenue Volatility: While demand surges, bid shading introduces a layer of unpredictability as bids are adjusted downward, making revenue streams less predictable.
2. Inventory Devaluation: As campaigns use bid shading to cut costs, the perceived value of your ad impressions might take a hit, impacting long-term revenue strategies.

Navigating the Risks of Bid Shading

Bid shading isn’t just a double-edged sword — it’s a whole cutlery set. Here are the risks you need to watch out for and how to handle them:

Lower CPMs:

Bid shading typically results in lower cost-per-thousand impressions (CPMs). Some publishers have reported CPM drops of up to 20% due to bid shading. This is a direct hit to your revenue as bids are systematically adjusted to lower amounts.

What to Do:

Consider implementing dynamic price floors that adapt to market conditions in real time. This ensures bids won’t drop below a certain level, protecting your revenue.

Inconsistent Revenue Streams:

The dynamic nature of bid shading means your revenue from political ads can fluctuate wildly, complicating forecasting and planning.

What to Do: 

Leverage advanced yield management tools to analyze historical data and market trends. This helps you understand and anticipate the effects of bid shading, optimizing your inventory pricing and placement.

Competitive Pressure:

With multiple campaigns vying for ad space, the pressure to lower prices further increases, risking a race to the bottom.

What to Do:

Enhance your auction strategies with techniques like header bidding. By involving multiple demand sources, you can drive up competition for your inventory, balancing out the downward pressure from bid shading.

Making Bid Shading Work for You

Bid shading isn’t all doom and gloom—there’s a silver lining if you play your cards right. Here’s how to turn bid shading into an advantage:

Leverage Advanced Analytics: 

Investing in tools that provide deep insights into bidding patterns can help publishers adjust their strategies in real time and identify opportunities to maximize revenue.

Enhance First-Party Data: 

Rich, accurate data about audience segments can command premium prices, even in a bid-shaded environment. Investing in data collection and analysis can increase the value proposition for advertisers.

Dynamic Price Floors:

Setting smart, dynamic price floors can help you maintain control over your inventory pricing. Adjust these floors based on real-time market conditions, like time of day, user demographics, and current events to prevent your CPMs from dropping too low.

Auction Strategies:

Don’t just rely on traditional auction setups. Incorporate header bidding to get multiple demand sources competing for your ad space. Increase competition for inventory and mitigate the impact of bid shading from any single source by relying on multiple SSPs and ad exchanges. This improves the likelihood of higher bids, even with bid shading in play. 

Yield Management:

Invest in robust yield management tools and expertise. These tools help you make data-driven decisions about your ad inventory, optimizing pricing and placement to counteract the effects of bid shading.

Collaboration with Buyers:

Build strong relationships with your advertisers. Educate them about the value of your premium inventory and work together to establish fair pricing and bidding practices. This collaborative approach can lead to more stable and beneficial outcomes for both parties.

When in Rome Leverage Bid Shading to Your Advantage

Bid shading is here to stay, especially in high-budget political advertising cycles. Publishers who adapt and strategically manage their ad inventory can thrive, capturing the full potential of these high-budget opportunities.

While bid shading presents both opportunities and challenges, strategic measures can mitigate risks and maximize revenue. Implementing dynamic pricing, enhancing auction strategies, optimizing yield management, and fostering collaboration with buyers is key to navigating bid shading complexities and staying competitive.

Not all of the predicted $12 billion election cycle budgets will be subject to bid shading. Direct deals, bypassing programmatic auctions, will also play a significant role. Publishers offering unique value propositions, like highly engaged audiences or brand-safe environments, can command premium prices despite bid shading tactics.

The post How Bid Shading and the $12 Billion Political Ad Boom Could Impact Publishers appeared first on AdMonsters.

No matter where you stand on the political spectrum, it’s hard not to watch the Supreme Court since it established its conservative majority. When SCOTUS overturned Roe v. Wade — there was major backlash from some and celebratory cheers from others — the country knew we were at a major political turning point. 

Now, the dominoes just keep on falling. With the 1984 Chevron Deference precedent overturning, power has shifted away from the executive branch to the judiciary, potentially transforming federal government operations.  

Consequently, the court’s conservative majority has made many regulations vulnerable to legal challenges. This ruling affects executive actions, including plans to install Wi-Fi on school buses, ban non-compete clauses, implement health care coverage rules under Obamacare, and forgive student loan debt. Plus, the advertising industry will have its blowbacks. 

As no industry remains untouched by this, what does it mean for the advertising industry? The consensus is that it will infringe on regulatory efforts and cause publishers and advertisers to challenge government agency regulations that affect their businesses more fervently. 

A Look Into Chevron

Chevron deference was a legal principle established by the Supreme Court in the 1984 case Chevron U.S.A., Inc. v. Natural Resources Defense Council, Inc. It required courts to follow administrative agencies’ interpretations of unclear laws, as long as those interpretations were reasonable and Congress had not explicitly resolved the issue.”

For 40 years, Chevron deference played a crucial role in administrative law. However, in June 2024, the Supreme Court overturned this doctrine in Loper Bright Enterprises v. Raimondo. The Court ruled that the Administrative Procedure Act requires courts to independently judge whether an agency has acted within its statutory authority without deferring to the agency’s interpretation of ambiguous laws.

Previously, Chevron deference allowed agencies to interpret statutes they administered as long as their interpretations were rational and not explicitly contradicted by Congress. This deference did not extend to agencies interpreting their own jurisdiction or statutes they did not administer. 

The overturning of Chevron deference in 2024 reduced the power of administrative agencies and increased the judiciary’s role in interpreting laws. This change will likely impact various sectors, including advertising, by altering how regulatory guidelines are enforced and interpreted.

Chevron Looms Over Advertising

These regulatory changes will compel the advertising industry to become more adaptable and cautious. As regulations, particularly the ones related to privacy, become increasingly complex, companies must remain agile in their compliance efforts. Publishers and advertisers must be flexible and quickly adjust to new rules and interpretations.

The reversal of Chevron could further incentivize brands to challenge FTC decisions through outside agencies, potentially altering incentive structures within the industry. On the other hand, federal agencies may now adopt slower, more cautious rulemaking, and Congress may need to draft clearer legislation related to privacy.

But not everyone sees this as a negative. Ken Nahigian, co-founder of the Balancing Act Project, sees potential benefits for the advertising industry following Chevron’s repeal. Publishers and advertisers may now be able to challenge industry regulations they believe negatively affect them, a feat Nahigian believed impossible before the repeal. 

“After today, the company will be able to challenge the rule and the courts will decide whether the agency had or didn’t have the authority. This will lead to a more thoughtful and collaborative process for regulating the industries,” said Nahgian. 

The Impact On Ad Tech’s Privacy Detox 

Publishers and advertisers will still face heightened legal scrutiny. Legal challenges to regulations, such as the FTC’s privacy crusade, will have substantial implications for the industry.  Additionally, challenges related to using AI in marketing and various consumer protection regulations could generate ripple effects throughout advertising and digital media.

With increased inspection of privacy compliance in ad tech, the Chevron appeal could add to the already stressful privacy detox ad tech is experiencing right now. Especially with the uphill battle of creating a Federal Privacy Law in the U.S. 

Major privacy-related efforts that may be impacted include the FTC’s Commercial Surveillance and Data Security Rulemaking, updates to the Children’s Online Privacy Protection Act (COPPA), and inter-agency efforts to prevent discriminatory automated systems in housing and employment.

This will require publishers and advertisers to stay ahead of regulatory developments and adapt their strategies accordingly. But this industry is no stranger to adapting to new regulations at a fast pace. It’s the name of the game.

The post SCOTUS Overturned the Chevron Deference Precedent…What Does It Mean for Advertising? appeared first on AdMonsters.

When Apple introduced App Tracking Transparency (ATT), it sent ripples through the digital ad world. Now, Apple is back with another major update: AdAttributionKit (AAK) — the sequel no one saw coming. Announced at WWDC 2024, AAK is set to redefine ad attribution, offering more flexibility, better insights, and a robust privacy-first approach. It’s like SKAdNetwork (SKAN) got a Tony Stark upgrade, complete with a nanotech Mark 50 suit of armor.

The framework will be available from iOS 17.4 onwards, with some features still in beta and slated for release in iOS 18. While AAK builds on SKAN’s foundation, it brings new tricks to the card table, like re-engagement capabilities and a developer mode that makes testing a breeze. Just keep in mind some features are still cooking in the beta oven, and with alternative app marketplaces still finding their feet, AAK’s full impact might take a hot minute to materialize.

Is it the holy grail we’ve all been missing? Let’s dive in and discover how this new framework revolutionizes ad attribution for app publishers, comparing it to SKAN, exploring its key features, and examining its impact on privacy compliance and ad performance.

What is Apple AdAttributionKit?

AdAttributionKit is Apple’s latest framework for measuring ad-driven app installs and user actions. Building on the foundations of SKAN, AAK brings significant enhancements to improve the attribution process. AAK works across both the App Store and alternative app marketplaces, making it a true cross-platform player. It supports multiple advertising formats, including static images, videos, audio, and interactive ads, all while preserving user privacy by limiting the data included in attribution postbacks.

Key Features: AAK’s Superpowers Unveiled

1. Multi-Store Support: AAK isn’t playing favorites. It supports alternative app marketplaces, making it a must-have for regions like the EU where app diversity is the name of the game.

2. Re-engagement Campaigns: Remember that user who ghosted your app? AAK lets you track and woo them back for up to 35 days after they re-engage. It’s like having a second chance at digital love.

3. Enhanced Creative Support: From static images to videos and interactive ads, AAK’s got you covered. It’s an all-you-can-eat buffet of ad formats.

4. Universal Links: Deep linking just got deeper. AAK can send users to specific in-app locations faster than you can say “user experience.”

5. Developer Mode: Testing made easy? It’s not a myth anymore. AAK cuts the BS from testing by removing time randomization and shortening conversion windows. It’s like having a time machine for your ad campaigns.

6. Privacy-First Design: AAK keeps user data locked down tighter than a submarine hatch. It’s all about crowd anonymity and aggregated data.

7. Fraud Prevention on Steroids: AAK is not messing around with ad fraud. It demands ads display front and center in the foreground, ensuring those impressions are as real as it gets.

AAK in Action: A Mobile Game Publisher’s Journey

Imagine you’re running a mobile game. With AAK, you could:

1. Track a user who clicks an ad on a third-party app store.
2. Measure their in-game purchases over 35 days.
3. Re-engage them with a targeted ad if they become inactive.
4. Analyze the entire journey with privacy-compliant data.

SKAdNetwork vs. AdAttributionKit: The Showdown

Think of SKAdNetwork as Thor’s trusty hammer Mjolnir, and AdAttributionKit as his upgraded axe Stormbreaker.  They’re both powerful tools, but AAK brings some new tricks to the battlefield.

Key differences include:

Scope and Reach: SKAN was exclusive to the App Store, whereas AAK extends to multiple app marketplaces, future-proofing your attribution strategy.
Re-engagement: AAK brings the ability to re-engage lapsed users, critical for maintaining user base continuity.
Creative Flexibility: AAK offers a broader array of ad formats, providing more creative freedom to let your creative flag fly.
Privacy: Both prioritize user privacy, but AAK enhances privacy controls with stricter features cranking it up to 11.
Performance Measurement: AAK provides more granular data to help fine-tune campaigns to perfection.

AAK and SKAN: Working Together

The good news is that AAK and SKAN can work together seamlessly. Here’s how:

1. Dual Implementation: Developers can implement both AAK and SKAN simultaneously, allowing for a smooth transition and leveraging the strengths of both systems.

2. Attribution Determination: When both AAK and SKAN impressions are present, the system considers all impressions together. It sorts them based on:

• Click-through vs. view-through (click-through takes precedence)
• Timestamp (most recent impressions prioritized)
• A maximum of six impressions are considered for any conversion.

3. Single Winner: Only one impression can win for a conversion, regardless of whether it came from AAK or SKAN.

4. Consistency in Privacy: Both AAK and SKAN maintain Apple’s commitment to user privacy, ensuring that the combined use doesn’t compromise data protection.

Is SKAdNetwork Dead?

Not quite. Like Vision transforming into White Vision, SKAN isn’t gone – it’s just evolving. Think of it as a gradual transition rather than an abrupt switch. Publishers should begin integrating AAK while continuing to use SKAN, ensuring a seamless shift as AAK becomes the standard.

Show Me the Money: AAK’s Monetization Magic

With AAK, you’re giving advertisers front-row seats to their campaign performance. The re-engagement feature alone is like capturing the brass ring for many advertisers. Add the improved creative support and more granular attribution data, and you’ve got a recipe for happier advertisers and potentially fatter checks for publishers.

AAK’s features translate directly to improved monetization potential:

• Higher ROAS through more accurate attribution.
• Targeted re-engagement to bring back valuable users.
• Cross-store insights to optimize campaigns across multiple app marketplaces.

But, AAK’s impact might hit differently for various app publishers. Game devs might be doing a happy dance over the re-engagement features, perfect for bringing back those high-value players like Ant-Man shrinking into the Quantum Realm and emerging right when you need him. Meanwhile, utility app publishers could be eyeing those cross-store insights, ready to optimize their campaigns across multiple marketplaces. It’s like the Avengers assembling, with each publisher getting to pick their favorite hero’s power to supercharge their ad game.

Privacy in the Spotlight: How AAK Addresses ATT Challenges

While AAK doesn’t entirely eliminate the hurdles posed by ATT, it offers new solutions — like Doctor Strange opening a portal to bypass obstacles. AAK provides privacy-compliant ways to measure ad effectiveness and re-engage users, addressing some of the data granularity and retargeting challenges introduced by ATT.

Key privacy features include:

• Privacy-First Design: Maintains user anonymity while providing valuable insights.
• Aggregated Data: Offers campaign performance metrics without individual user tracking.

Tech Setup: Your AAK Implementation Roadmap

Getting AAK up and running isn’t rocket science, but it’s not exactly a walk in the park either. Here’s a quick guide:

1. Update iOS: Ensure your app supports iOS 17.4 or later.
2. Integrate the Framework: Add the AdAttributionKit framework to your app.
3. Configure Ad Networks: Align your ad networks to work with AAK for accurate attribution.
4. Set Up Postback Endpoints: Establish endpoints to receive attribution data.
5. Leverage Developer Mode: Use it for rigorous testing and fine-tuning.
6. Opt-in for Winning Postbacks: Developers can receive copies of winning postbacks by adding the ‘AttributionCopyEndpoint’ key to their app’s Info.plist file. This enables receiving the same postback data that ad networks receive for winning attributions, providing valuable insights into your app’s performance.

The Infinity Stones: What Publishers Need to Know About AdAttibution Kit

• Privacy First: AAK continues Apple’s user privacy crusade. Embrace it or risk being left behind in the digital dust.
• Flexibility is Key: With support for multiple app stores, AAK future-proofs your attribution strategy.
• Re-engagement is Gold: Don’t underestimate the power to bring back lapsed users. It’s like finding money in your old coat pockets.
• Creative Freedom: More ad format support means more opportunities to shine.
• Gradual Transition: Start planning for AAK now, but don’t pull the plug on SKAN overnight. It’s a marathon, not a sprint.

Preparing for the Future

AdAttributionKit isn’t just an improvement over SKAdNetwork – it’s a significant leap forward in mobile ad attribution. It offers publishers and app developers a powerful blend of enhanced insights, improved monetization potential, and advanced tools for privacy-preserving ad attribution, addressing many of the pain points brought on by ATT.

Embracing AAK will be crucial for staying competitive and maximizing ad revenue in the evolving mobile advertising marketplace. As Nick Fury might put it, “You’ve got the tools. Now, show them what you’re made of.” AAK is the next phase in the attribution endgame—time to take charge.

The post What Is AdAttributionKit (AAK): Apple’s Next-Gen Tool for Smarter, Privacy-Focused Ad Attribution? appeared first on AdMonsters.

Floor pricing optimization is making waves again in the publishing world, but have we been overthinking it?

Kean Wang, VP of Product and Strategy at Intowow, dives into the evolution of floor pricing strategies and unveils a refreshing shift from complex mathematical models to the practical realm of behavioral economics. By understanding bidder behaviors and leveraging the strengths of Header Bidding and Google Ad Manager, publishers can streamline their approach and boost revenue without getting lost in the computational weeds.

Floor pricing optimization has regained popularity among publishers. Over the past five years, we have perfected our dynamic floor pricing algorithms. However, it wasn’t until a year ago, as we gained access to more data from major publishers around the world, that I realized we might have been approaching this problem unnecessarily.

For quite some time, we adopted a purely mathematical approach to floor pricing optimization, focusing on determining each price P that maximizes the RequestRPM according to the function 

RequestRPM(P) = SellThroughRate(P) × eCPM(P)

for each infinitesimally meaningful inventory segment. If demand is static, the calculations are straightforward and manageable. However, randomness introduces uncertainty in a more realistic scenario where hundreds of thousands of campaigns run concurrently and complete at different times. This uncertainty significantly complicates the computational process and requires intensive predictive modeling to find an optimal solution.

It turns out that an easy way out is to approach the problem from a completely different discipline – behavioral economics.

A Simple and Elegant Approach

In the real world, campaigns are managed by DSPs who bid for impression opportunities in auctions. These DSPs vary widely in their technical capabilities and operational strategies. So, if we could target the behaviors of different types of bidders and provide the right incentives and signals to facilitate communication and competition among them, we could reach an alternative solution that is more elegant. This approach allows the market to optimize by itself to maximize publishers’ benefits without too much interference and the need for excessive calculations.

Generally speaking, bidders buy through two open auction channels: Header Bidding and Google Ad Manager (GAM), both of which are extensively integrated by most publishers. By analyzing bidding behaviors across these channels, we have consolidated the following best practices:

1. For Header Bidding, set up floor prices low enough on SSPs to encourage bid tendencies but high enough to filter out low-quality ads.
2. On Google Ad Manager, use the winning Header Bidding bid prices and dynamically trigger Unified Pricing Rules (UPRs) to provide competitive price signals through Google Ad Exchange and Open Bidding.
3. Ensure that Header Bidding line items are correctly priced on GAM with your net earnings to facilitate an efficient unified auction.

These best practices take advantage of a key behavioral distinction between these two channels of bidders: 

Google Ad Manager bidders, predominantly Google Ads and DV360, primarily adjust their bids based on floor price signals, whereas Header Bidding bidders make extensive adjustments in response to changes in win rates.

Excessive floor prices do not stimulate Header Bidding bidders; instead, they block their bids and reduce competition. By allowing more Header Bidders to participate in auctions, we maximize the competitive bid signals sent to GAM. On GAM, triggering these signals with UPRs can restore the “last look” advantage for Google bidders, encouraging higher bids that benefit publishers. (Google’s decision to cancel this feature was due to pressure from other SSPs, but this move also negatively impacted publisher revenue.)

With more competitive bids from Google bidders taking over some winning opportunities from Header Bidders and driving down their win rates, Header Bidders are incentivized to adjust their bid prices, which in turn encourages more competitive bids from Google. This fosters a perpetual cycle of healthy competition across these two channels.

For publishers with extensive Header Bidding coverage, these best practices are generally sufficient. Beyond this, additional efforts would likely yield only marginal benefits unless you are determined to invest intensive R&D to further optimize price ranges specifically for Google bidders across each traffic segment, where the benefits could add up to be significant.

More About the Behavioral Distinction

Upon further research and some reverse engineering, we were able to gain a clearer understanding of the factors contributing to such a behavioral difference.

For DSPs, floor prices are one of the pre-auction signals useful for optimizing bid decisions to maximize campaign ROIs. However, for floor prices to serve as reliable indicators, the environment must meet three criteria:

1. Floor prices, along with the associated traffic metadata, should be consistently supported across all stakeholders.
2. Floor prices must maintain their integrity during transmission and should not be lost, overridden, or manipulated down the supply path.
3. To benefit from these signals, bidders must possess powerful predictive capabilities with the technical bandwidth to perform cost-efficient real-time calculations.

Only Google, with its unified and streamlined programmatic ad supply path, meets these criteria across all stages. Information from publisher web pages is collected by the standardized Google Publisher Tag (GPT) library, consistently formatted as ad requests, and transmitted to the centralized Google Ad Manager. From there, bid requests are uniformly assembled and sent to DSPs via a robust server-to-server connection using the Authorized Buyer Real-Time Bidding Protocol.

On the buy-side, Google Ads and DV360, operating with powerful predictive capabilities leveraging Google’s highly integrated cloud infrastructure, can accurately estimate ad performance using real-time client-side signals and determine reasonable bid prices for each impression opportunity before an auction occurs. 

In contrast, for Header Bidding, every bid request is processed by at least two entirely separate parties (e.g., publisher-hosted Prebid.js, vendor wrappers, or SSPs) before reaching DSPs. Even for large DSPs with strong predictive capabilities, such a fragmented supply path makes it difficult to ensure the integrity of information, forcing them to downplay the option of adjusting bid decisions based on real-time sell-side signals. A rather reliable source of information is win rate data, which each bidder processes post-auction but is often delayed and lacks granularity.

These disadvantages of Header Bidders are particularly evident when we compared bid CPM trends from early to subsequent ad refresh instances or across different ad position series. For example, for the same inventory, when comparing the CPM of the first ad to the fifth ad refresh, average bid prices from Google bidders can drop by over 50%.

However, for Header Bidders, the average win bid CPM only decreased by 3%, which falls within the margin of statistical error. Such inability to perform per-impression bid adjustments from Header Bidders can also be highlighted by the fact that, on average, they purchase the same inventory with the same level of CTR and viewability performance at approximately a 35% premium compared to Google bidders.

To further pinpoint the issue, we took the same Header Bidding bidder and compared its bidding behaviors across two different channels: Header Bidding and Open Bidding (a unified server-to-server bidding solution provided by Google).

Under the same floor pricing strategy for the same bidder, through Open Bidding RequestRPM could be improved by 8 to 10%, compared to only a marginal 2% improvement through Header Bidding. This suggests that a fragmented supply path is the primary factor preventing per-impression bid adjustments for Header Bidders, forcing bidders through this channel to forgo floor prices and focus on win rate signals instead.

Future Outlook for Floor Pricing Strategies

Floor prices, like other real-time client-side signals, provide valuable information that encourages bidders to recognize the fine nuances in publisher inventory. However, these signals can only be effective when information integrity can also be guaranteed across the entire supply path.

The above behavioral distinctions between Google bidders and Header Bidders underscore the importance of Supply Path Optimization (SPO) and demonstrate how a more streamlined supply path can encourage bidders to utilize more sell-side signals, ultimately improving efficiency across the industry.

But for now, with these simple yet elegant best practices, publisher ad inventory is effectively categorized into two groups: one with extensive competition from both Header Bidders and Google bidders, and the other with only Google bidders. Publishers with more inventory in the first category can significantly benefit from the competitive cycle facilitated by these simple steps.

However, for publishers whose inventory primarily falls into the latter category, an active floor pricing strategy using a traditional mathematical approach is still necessary to realize the huge growth potential from Google demand.

The post Are We Overcomplicating Floor Pricing Optimization? appeared first on AdMonsters.