Google AdWords, now known as Google Ads, is an online advertising platform by Google. It allows businesses to display ads on Google search results, YouTube, and other sites. Advertisers bid on keywords and pay each time their ad is clicked, using various ad formats like text, display, video, shopping, and app promotion ads.

Bidding Before December 2023

Maximum CPC (Cost-Per-Click) Bid: Advertisers set a max amount for each click.

Ad Rank: Determined by bid amount, ad quality score (relevance, expected click-through rate, landing page experience), and ad extensions.

Automated Bidding Strategies:

• Target CPA: Bids for cost per acquisition.
• Target ROAS: Bids for return on ad spend.
• Maximize Clicks: Aims for most clicks within budget.
• Maximize Conversions: Aims for most conversions within budget.

Changes After December 2023

Enhanced Machine Learning: Improved algorithms predict clicks and conversions better.

Adding new Bidding Options:

• Maximize Conversion Value with Target ROAS: Aims to maximize total conversion value while meeting ROAS targets.
• Seasonality Adjustments: Allows bid optimization for expected seasonal changes.

Refined Quality Score Components: Provides detailed feedback on ad relevance, click-through rate, and landing page experience.

Real-Time Bidding Adjustments: More precise bid adjustments based on user behavior and intent.

Improved Integration with First-Party Data: Better use of advertisers’ data for personalized ad targeting.

These updates enhance bidding efficiency, transparency, and alignment with business goals using advanced machine learning.

What did we observe?

We observed that the exact date for rolling out these changes was 8th December 2023. We wanted to find out how these changes affected the performance characteristics of different publishers. To do that, we analyzed the performance changes in early December across three different inventories:

Case #1: A big news publisher, one of the local market leaders,

Case #2: Another big news publisher, one of the local market leaders,

Case #3: A big global publisher, no Prebid implemented.

On the first glimpse, not much has changed in AdWords’ shares

In all three cases, the share of AdWords in both revenue and impressions remained relatively flat. However, what is more interesting are the changes in its average eCPM.

Side note:

On all eCPM charts, to make the data easier to compare and to protect our clients’ privacy, December 1st is always indexed to 1.0. The following days are then compared to this index. For example, if the eCPM on a given day is 1.2, it means it was 20% higher than on December 1st.

Case #1

We saw a slight increase of AdWords share both total revenue and impressions, however to be completely honest, the change was rather marginal. These fluctuations (especially in revenue) can be however explained by volatile eCPM which noted two peaks in that period:

Case #2

Case #2 showed a similar situation; however, here we saw that eCPM decreases after the 8th of December. We do not show this data here, but in 2024 eCPM started to increase again. But as you most likely all well know, we shall not compare the early new year’s performance to the first weeks of December (or, frankly speaking, rely on period-to-period comparisons unless the change is super evident).

Case #3

This case is quite an eye-opener. Since AdWords is dominating the open market (this publisher is not using Prebid at all), we expected the changes to be most visible on that inventory. However, what we saw exceeded our expectations – the eCPM after December 8th dropped by almost 50%!

Let’s get deeper!

On the first glimpse, if Adwords is not basically the entire Open Market (like in cases #1 and #2) the changes are not very visible. But maybe something changed deeper underneath? To understand that, we verified if AdWords changed the way it reacts to changing the floor prices.

What did we do?

On the same inventories at the same time, we checked AdWords total revenue and impressions on four parts of the traffic that differed in floor price applied. Essentially, we were A/B/C/D testing four different hard floor price levels (each price handled the same percentage of the traffic). This allowed us to see what happened with AdWords bidding on December 8th in terms of two metrics: revenue and impressions.

Side note:
On all charts, to make the data easier to compare and to protect our clients’ privacy, no price applied is always indexed to 100. The higher price levels are then compared to this index. For example, if the revenue on a given day is 80, it means it was 20% lower than on no price applied.

Case #1

The change introduced on December 8th has drastically impacted AdWords’ sensitivity to price changes. Before December 8th, increasing the floor price resulted in higher revenues (even 20% higher than when no price was applied). However, after that day, a higher price led to a loss in revenues. If we compare the high price revenue vs no price applied revenue, we can see that it used to be ~20%, but after the change, it dropped to ~-15%!

This drop in revenue is caused by a decreasing number of ad impressions at higher prices.

Revenues:

Impressions:

Case #2

Case #2 is basically Case #1 “on steroids”. The change introduced on December 8th resulted in a drastic change in AdWords’ reaction to different price changes. Here, the revenue uplift of high price vs. no price applied dropped from +60% to -30%!

Similar to Case #1, the number of AdWords impressions at higher prices decreased, which was the primary cause of dropping revenues.

Revenue:

Impressions:

Case #3

Case #3 confirms that the changes observed in the previous two cases are not by chance. The same mechanics changed, and AdWords’ sensitivity to price changes was similarly affected (here the high price revenue uplift vs. no price applied dropped from ~15% to ~-20%.

Revenue:

Impressions:

So, what’s the conclusion?

To conclude, we must consider our analysis as a whole. It pointed out that after December 8th:

• The share of AdWords in total revenue and impressions remained stable.
• Average eCPMs slightly decreased.
• Revenue began to negatively react to floor price increases.
• AdWords started purchasing more impressions at lower prices.

These changes suggest AdWords began selecting ad requests more carefully and buying cheaper impressions, thereby increasing profitability. It appears AdWords shifted from targeting specific users to buying in bulk, leveraging a better understanding of user behavior to maintain profitability without higher prices.

What should you do as a Publisher?

If you manage your pricing manually, be sure to revise it. Decreasing the price often leads to higher overall revenues (not always, but as a rule of thumb). This might be a good time to automate the price management process to be prepared for similar unexpected changes in the future.

The post How did Adwords change bidding at the end of 2023? (performance analysis) first appeared on Yieldbird - Research Hub.

The Importance of First-Party Data

With less reliance on third-party cookies, first-party data is becoming crucial. It’s valuable because it’s more accurate and comes directly from your audience, with their permission. For publishers, it’s time to focus on collecting high-quality first-party data. This means being transparent about what data you’re collecting and why, and using this data to create a more engaging experience for your visitors.

As we navigate through the transition away from third-party cookies, it’s essential to understand the timeline that outlines the major milestones and phases. This timeline helps visualize the shift towards a more privacy-focused online advertising ecosystem, marking key periods of testing, adaptation, and final implementation of new technologies.

Understanding Privacy Sandbox

For website owners, it’s important to get familiar with how Privacy Sandbox works and what it means for the future of online advertising. This involves exploring new tools and technologies that allow for personalization in a privacy-focused way.

Google has introduced several initiatives under the Privacy Sandbox umbrella. Among these initiatives, the Topics API and Protected Audience API stand out as key components of this new era.

Topics API: This approach enables a browser to determine a user’s interests based on their web browsing activity. Rather than tracking an individual across the internet, it identifies broad interest areas such as “sports” or “cooking.” This helps in delivering ads that are relevant to the user’s interests without infringing on their privacy.

Protected Audience API: Formerly known as FLEDGE, this tool also seeks to deliver relevant advertising by categorizing users into “interest groups” based on the types of sites they visit. It aims to show ads that the user is likely to find engaging or useful, without the need for tracking individual browsing history.

Adapting to this new environment requires a willingness to experiment and learn. Publishers must be agile, ready to test out Privacy Sandbox’s alternatives, and other privacy-preserving technologies as they become available. Engaging actively during the standstill phase will be key to developing effective strategies that minimize disruption to ad operations and maintain, if not grow their ad revenues.

For publishers managing several websites under one brand, the concept of Related Websites has been introduced. This setup allows some cookies to be shared between these websites, almost like they are from third parties. This method allows browsers to grant very limited access to third-party cookies for designated purposes, balancing privacy concerns with functional necessity. Specifically, Chrome utilizes these declared relationships to make informed decisions on when to permit or restrict a site’s access to cookies in a third-party context. For site authors looking to leverage this capability, it’s essential to submit their domains to a set, following the detailed submission guidelines available on GitHub. 

Based on a test conducted by one of our partners, it was observed that for the 0.75% of all Chrome users who no longer had access to third-party cookies but had the Topics API enabled, there was a decrease in Revenue Per Thousand Impressions (RPM) by 35%. However, in a promising turn of events, the win rate for Adx and open bidding simultaneously saw an increase of +4%. 

GAM: Identity insights

Google Ad Manager (GAM) has introduced a useful feature that aids publishers in comprehending the nuances of this transition. This feature offers publishers a detailed breakdown of the current landscape of identifiers, which is crucial for tailoring advertising strategies amidst growing privacy concerns.

This feature meticulously categorizes identifiers into distinct statuses, providing a few options available to publishers. It differentiates between:

• Third-party ID status: This indicates whether third-party cookies or device IDs are available for use in targeting. Given the ongoing deprecation of third-party cookies, understanding their availability becomes essential for publishers looking to adjust their strategies.
• PPID status: PPIDs, or Publisher Provided Identifiers, are first-party identifiers directly sent with the ad request by Ad Manager 360 publishers. This segment allows publishers to see how often these identifiers are being utilized, offering insights into the extent to which first-party data is driving addressability.
• First-party ID status: This involves identifiers confined to a publisher’s own sites or apps, such as “same app key” on iOS or _gads on the web. These serve as fallback mechanisms when third-party cookies are unavailable. It’s crucial to note that this category excludes PPIDs, providing a clear distinction between different types of first-party identifiers.

Additionally, the feature distinguishes between “Active” and “Restricted” statuses for these identifiers. “Active” implies that the IDs were available for personalization, thereby enabling targeted advertising. In contrast, “Restricted” signifies that while the IDs were present, their use for personalization was limited, reflecting the constraints imposed by privacy regulations or browser settings.

Google also added:”Topics status,” which reports whether the ad request included topics for targeted advertising, with values indicating whether topics were provided, empty, or not available due to lack of support or other reasons. Furthermore, “Publisher Provided Signals” offer publishers the capability to enrich ad requests with IAB taxonomy categories, enhancing targeting precision. Other dimensions such as “PPID presence” and “User identifier status” provide further insights into the availability and utilization of identifiers.

In conclusion, the end of third-party cookies signals a new era for digital publishing, marked by a huge focus on privacy and data protection. By embracing first-party data, engaging with emerging technologies, and fostering transparency and trust with their audience, publishers can navigate these changes successfully. The journey ahead is complex, but with the right strategies and a proactive approach, publishers can generate new opportunities for growth in a privacy-first world.

The post Adapting to the New Cookieless reality: An Expert Update for Publishers first appeared on Yieldbird - Research Hub.

What are Prebid Timeouts?

Imagine, as a publisher, you’re hosting a programmatic auction where ads are the star of the show. When a visitor lands on your website, you kick off this auction. It’s not just any auction, though. It’s a speedy, high-stakes game where various SSPs have a limited window to place their bids for the opportunity to display their ads to your users. This is where the concept of “timeout” enters the stage.

A timeout in Prebid is essentially a countdown timer. It tells these SSPs how long they’ve got to throw their hat into the ring. If they don’t bid in time, they miss out, and the auction moves forward without them. The focal point of our discussion, the “Auction Timeout,” is critical because it determines how long this window stays open.

A Word on Our Research Methodology

Our study was a series of A/B tests of various Prebid Timeout settings conducted on four websites differing in structure (ranging from traditional news sites to specialized portals and web application-like platforms), various shares of Directly sold ad impressions(from 8% to 100%), and the share of Header Bidding (from 2% to 22%). This diversity provided a broad spectrum of insights into how different factors influence timeout optimization. It’s important to note we’re discussing client-side Prebid integrations here.

The Quest for the Perfect Timeout Setting

If you’re a publisher who has recently embraced Header Bidding technology through Prebid, you might still be operating with the default timeout settings – typically, an Auction Timeout of 1000 milliseconds (ms) and a Failsafe Timeout of 3000 ms. These settings are a solid starting point and might even hit the mark perfectly right off the bat for some publishers. Our extensive experience working with more than 1000 publishers has taught us that 1000 ms often serves as the minimum threshold for an optimal timeout setting. It’s rare to see the optimal setting to dip below this mark. 

The sweet spot for Prebid timeouts usually ranges between 1000 ms and 2500 ms. However, it’s not uncommon to encounter scenarios where a longer timeout is present, though such decisions should always be backed by extensive testing. 

An exciting aspect of Header Bidding auctions is that the actual wait time may not always reach the maximum set, such as 2500 ms. For instance, if the last SSP in the pool sends back a response and the auction concludes at 1500 ms, there’s no need to wait longer. Thus, the optimal timeout balances two critical factors: the potential for increased revenue from Header Bidding, thanks to SSPs having ample time to respond, and the risk of losing ad requests due to prolonged waiting periods.

Why Timing is Everything

Finding the right timeout setting is crucial for maintaining a fast-loading website while ensuring maximum ad revenue. By carefully increasing the Auction Timeout, you give SSPs a better chance to respond with their bids, potentially driving up your ad revenue by securing higher bid amounts. Yet, there’s a tipping point. Increase the timeout too much, and you might start seeing a drop in user engagement. Visitors are likely to abandon a site that takes too long to load, leading to fewer ad impressions and, consequently, lower ad revenue. The goal is to find a timeout duration that allows for competitive bidding among SSPs without negatively impacting the user experience.

Setting a longer timeout means you might lose some ad requests. This is because making users wait longer can lead to some of them leaving your site before ads finish loading.

This situation leads to a decrease in the number of ads available for display across all advertising channels, including Google AdX, AdSense, and direct ads. It’s important to note that for sites with a tiny share of revenue from Header Bidding (2-3%), the potential uplift in Prebid based revenue is very unlikely to offset the losses from missed ad requests on the entire ad stack. In such cases, if, despite increasing timeouts Header Bidding share remains marginal, it is crucial to investigate the reasons behind the low Prebid revenue share further and look deeper into such matters as limited SSP participation, low demand, inappropriate ad formats, or incorrect settings in Google Ad Manager (GAM), and attempt to improve these aspects. If losses still outweigh gains despite these efforts, discontinuing Header Bidding might be the most logical course of action.

The Fine Art of Timeout Optimization

Finding the right Auction Timeout is not about guesswork but testing. Some website owners employ A/B testing, which involves comparing two different timeout settings to see which one generates the most revenue at the end of the day. This approach allows you to make data-driven decisions that can significantly impact your ad earnings and is generally speaking the most accurate one.

Moreover, diving into your website’s analytics can provide a wealth of insights. Observing how changes in timeout settings affect your site’s traffic and ad revenue over time can guide you to the most effective configuration. Advanced analytics tools can offer a deeper dive, revealing how quickly each SSP typically responds and how different timeout settings impact your overall ad strategy.

Publishers who cannot employ A/B testing may try to select periods with stable traffic and user session data, without significant fluctuations, for testing – utilizing tools like Google Analytics to identify these optimal testing windows. This approach is much less accurate than A/B testing and in case of many publishers does not allow to get conclusive results due to performance fluctuations that result from different things than changed Timouts. 

Regardless of whether you employ A/B testing or period do period comparison having access to Prebid’s detailed reports helps a lot with this. It lets you see how each Bidder (SSP) bidding on your ads is doing, like how quickly they respond and how often they win auctions. This makes it easier to figure out how to get the most out of your ad inventory.

Impact of changing the timeout on your ad stack’s revenue

When you adjust the timeout settings, you might see your earnings go up a bit, but only to a certain limit, especially when your initial settings were on the lower end. It’s important to find that sweet spot where waiting a bit longer for ads to load helps you earn more without going too far. Also, think about how changing the timeout affects your total revenue, including both the ads you sell directly and those sold through the open market. Remember, finding the right timeout setting is a balance – it can help increase your income from ads, but there’s a point where making users wait longer doesn’t pay off anymore.

Revenue vs. Timeout Charts: Differentiating Between Sites With and Without Direct Sale

We looked into what might happen if we changed timeout settings for each ad unit, thinking it could generate additional revenue. To do this, we looked at the revenue generated by the best timeout (as determined on domain level) vs the revenue generated by the best timeout setting for each ad unit independently.. The results, unfortunately, were not super positive. In theory, doing this could make us a little more money, from nothing extra up to 1.8% more. But that’s just in theory. In the real world, based on what we’ve seen, the actual boost in money is usually at least 25% less than what such estimations show
Furthermore, adjusting timeouts settings per device resulted in even lower improvement – around 0.5%.

This small increase is a bit of a letdown because for publishers diving deep into the granularity of adjusting timeouts for individual ad units, the results, though positive, may not always justify the effort (and an increase in the script size to “communicate” to each ad unit the independently set up timeout setting). While each incremental improvement in ad revenue is valuable, publishers must weigh the operational complexities and resource investments against the relatively modest gains achievable through this approach.

Constancy of Optimal Timeouts Over Time

The behavior of optimal Prebid timeouts over time seems steady, constant and predictable. Despite expecting rapid changes in the programmatic world, the ideal timeouts for these auctions don’t vary much as time passes by. We’ve seen it hover around two similar values, with just a few unusual days here and there. Still, generally, it stays the same. This shows that for most publishers, it is completely enough to set an a/b test, determine the optimal timeout and forget about this setting for at least the next couple of months.

How Adjusting Timeouts Affects Ad Visibility and CTR

When we change timeouts for ads to bid in Prebid, it also slightly changes how often people see the ads (Viewability) and how often they click on them (CTR). Making the wait time a bit longer can make a slight difference in making ad impressions more visible by about 1 to 2 percent. But, when it comes to clicks, especially on websites that use a lot of programmatic ads and Header Bidding, increasing the wait time doesn’t really help. In fact, the more we rely on these types of ads and extend the wait time, the less likely people are to click on the ads. This happens because in the world of Header Bidding, the focus isn’t so much on getting immediate clicks. 

Mastering prebid timeouts with Prebid Stack

Our journey through the nuances of Prebid timeouts highlights the importance of adaptability and precision, a process greatly simplified by Prebid Stack. As the programmatic landscape shifts, Prebid Stack provides the tools you need for optimizing and managing prebid, including detailed timeout settings and an advanced A/B testing panel. Embrace the cycle of testing, learning, and iterating with the support of Prebid Stack, and navigate the path to sustainable growth in programmatic advertising.

The post A simple guide to prebid timeouts first appeared on Yieldbird - Research Hub.

User Consent and Ad Blocking Challenges

Privacy Regulations and User Consent: The enforcement of GDPR, CCPA, and other privacy regulations has transformed the digital advertising ecosystem, placing user consent at the forefront. Publishers now face the intricate task of monetizing their digital content without infringing upon user privacy rights.

The Ad Blocking Phenomenon: The widespread use of ad blockers and the increasing preference for non-personalized advertising further complicate the revenue generation model for publishers. This scenario necessitates innovative solutions that respect user privacy while ensuring sustainable ad revenue for publishers.

Prebid Stack offers a promising solution by enabling publishers to display ads even in the presence of ad blockers or when users opt out of certain data processing activities. It allows to implement an anti-ad-block feature, allowing publishers to reclaim potential lost revenues and serve ads that respect user consent preferences.

While some users may provide full consent for all data gathering and usage purposes, others use their right to selectively choose which purposes they are comfortable with. This selection process raises an important question for publishers: How is it possible to serve ads to users who do not give full consent for all the TCF-listed purposes?

The answer lies in the flexibility of header bidding solutions like Prebid Stack, which enable the serving of ads even when users decline consent for certain purposes. The TCF outlines various purposes for data processing, and not all are equally critical for the ad serving process. For instance, Google Ad Manager (GAM) and certain Supply-Side Platforms (SSPs) might require consent for specific purposes to serve ads effectively.

However, when users specifically deny consent for purposes such as 2 (Use limited data to select advertising), 7 (Measure advertising performance), 9 (Understand audiences through statistics or combinations of data from different sources), and 10 (Develop and improve services), serving ads through GAM can become challenging. Despite this, the situation is not entirely bleak. There exists a subset of bidders within the header bidding ecosystem willing to accept ad requests and engage in the bidding process without consent for these particular purposes.

This capability underscores the importance for publishers to understand which SSPs can accommodate such requests. Certain SSPs have clarified in their terms and conditions, or through direct communication, that they do not require consent for these specific purposes to place a bid. This knowledge is crucial for publishers looking to navigate the consent landscape efficiently, ensuring that opportunities for ad serving and revenue generation are maximized even when full consent is not granted by the user.

How does the non-consent traffic monetization feature work?

Upon a user’s arrival on a website, Prebid Stack determines the user’s TCF consent status, categorizing them into one of two groups:

1.Users who give their consent for all the purposes 2, 7, 9, and 10.
2.Users who withhold their consent for any of the purposes 2, 7, 9, and 10.

For the first group, Prebid Stack proceeds as usual, conducting the ad auction and communicating the necessary information to Google Ad Manager (GAM) and the SSPs.

However, for the second group, Prebid Stack adopts a customized strategy. It specifically targets ad requests to bidders that have been pre-identified for their willingness to participate in
such cases – it bypasses GAM.

Automatic and Manual Bidder Selection:

For demand originating from Yieldbird (Yieldbird bidders), this bidder selection process is automated. We have pre-identified bidders agreeable to bidding under these consent restrictions, thereby eliminating the need for publishers to manually adjust settings within the interface.

On the other hand, publishers utilizing their own SSP accounts (Own bidders) will need to undertake this selection process themselves. It’s important for those publishers to review the SSP’s terms and conditions or reach out to them directly to ascertain their stance on bidding without consent.

Conclusion: Balancing Privacy and Profitability with Prebid Stack

In summary, Prebid Stack offers a strategic solution for publishers facing the complexities of user consent under the TCF. By differentiating between users based on their consent for specific purposes and adapting ad requests accordingly, it allows publishers to bypass the constraints of Google Ad Manager (GAM) in scenarios of partial consent. This not only aligns with privacy regulations but also ensures publishers can sustain their ad revenue, highlighting Yieldbird’s innovative approach to overcoming digital advertising challenges.

The post Unlocking Non-Consent Traffic Revenue: Monetize Efficiently with Prebid Stack first appeared on Yieldbird - Research Hub.

Why TCF v2.2 matters in today’s digital landscape?

Launched back in April 2018, the TCF (Transparency & Consent Framework) has really helped make sense of the fast-moving world of digital ads, while also sticking to the tough rules of things like the ePrivacy Directive and GDPR. It’s all about setting a standard that really values user privacy, and now, Yieldbird’s totally on board with this too.

Setting standards: Yieldbird’s commitment to users

By using TCF’s clear-cut way of doing things, we at Yieldbird are making things better for users. We’re all about giving them simple, easy-to-understand choices for their privacy. With integrating TCF’s standardized approach, Yieldbird is enhancing the user experience, providing clear, concise, and user-friendly options for privacy choices. Our goal is to empower users, giving them control over their data and the assurance of privacy.

Our TCF membership signifies our commitment to ethical advertising. We’re now part of a collective that shapes policies and practices, ensuring they align with the evolving digital advertising landscape while keeping user rights at the forefront.

Implementing Practical Requirements: Adhering to TCF v2.2 means Yieldbird is not just compliant but also a forerunner in applying practical requirements that stem from guidelines of Data Protection Authorities.

As we embark on this journey with the TCF, we’re not just advocating for a shift in programmatic advertising; we’re actively participating in a movement that prioritizes transparency, respect, and user privacy. This is not just a compliance milestone; it’s a statement to our commitment to creating a digital ecosystem where user trust is our top priority.

The post Yieldbird embraces IAB Europe’s TCF v2.2 for Enhanced User Privacy and Compliance first appeared on Yieldbird - Research Hub.

In this context, green programmatic advertising is becoming more crucial because it is a powerful tool for businesses to communicate their commitment to environmental responsibility. It allows brands to showcase eco-friendly practices, highlight sustainable products, and share initiatives to reduce environmental impact. This not only resonates with environmentally conscious consumers but also builds trust and loyalty among a growing segment of the market that prioritizes sustainability.

Sustainable Programmatic Deals 

Right now, the need for green advertising is not driven by regulatory requirements. But as we see a broad global shift toward sustainability, it is a strategic imperative for businesses to participate in an environmentally conscious advertising marketplace.

Programmatic deals were always on the leading edge of any changes and developments in the programmatic industry. After all, they allow various additional features to be added by the publishers or the supply-side platforms (SSPs), which are not accessible if the transactions are done on the open exchange.

This includes the shift toward more sustainable marketing practices. By leveraging programmatic deals, advertisers can strategically target audiences on eco-friendly platforms, such as marketplaces for used clothing and accessories. But they can also optimize their media buying process to minimize their carbon footprint. This not only enhances the visibility of sustainable products but also aligns advertising efforts with environmental consciousness.

The essence of programmatic deals lies in their ability to optimize ad placements, ensuring they resonate with eco-conscious consumers. These deals facilitate a seamless connection between advertisers and consumers focused on environmentally friendly products and services. For example, in the case of a news publisher, the programmatic deal can only target specific ad units in articles about sustainability, renewable energy, circular economy, etc. The result is a win-win situation – advertisers reach a targeted audience genuinely interested in sustainable products while consumers discover products aligned with their values.

Furthermore, programmatic advertising allows for precise campaign impact measurement, including calculating carbon footprints. This feature was made available by two leading SSPs Marketplaces: Equativ (formerly known as Smart) and Adform. This level of transparency was unavailable previously. Now, it empowers advertisers to assess and refine their strategies, minimizing environmental impact while maximizing reach and effectiveness.

Carbon Footprint Measurement in Programmatic Deals

Let’s go with an example. The programmatic deal set up in Equativ SSP allows the buyer to calculate and get a report of the carbon footprint of each part of the media delivery process: from the campaign set up to the reporting. Several factors are considered into the carbon emission calculation, such as devices, deal type, creative size, format and duration and much more.

The Equativ Campaign carbon impact report allows us to check which device categories were the most energy efficient or which creatives caused the most footprint. In the preliminary information supplied by Equativ, we see some interesting conclusions. 

There were no surprises regarding the specific creative footprint: the lighter the creative, the less negative environmental impact. The worst were the longer video creatives and the heaviest display or rich media ads. In regards to the device categories, the most efficient were the mobile phones: smartphone < TV < tablet < PC. 

In a first test campaign, Equativ’s methodology reduced the carbon impact by 64%, generating 37g of CO2 emitted per 1k impressions versus 103g as a benchmark. The environmental impact of the campaign represents the equivalent of driving 27.76 kilometers, compared to 76.8 kilometers for a non-optimized campaign.

“In the digital advertising ‘value chain,’ which encompasses the journey from conceptualizing an idea to the moment when an advertisement reaches the user and the subsequent reporting of results to the advertiser, multiple stages are involved. Each of these stages needs the use of energy. Hence, we meticulously examine the entire campaign delivery process to initially quantify the energy consumption associated with each step. Subsequently, we focus on reducing this energy consumption by optimizing the path and refining the underlying processes while guaranteeing the meet of expected performance from our partners.”,

says Marcin CZACHOROWSKI, Senior Manager, Demand Partnerships at Equativ.

For detailed information on green programmatic deals, check the presentation from our recent event.

Go For Responsible Programmatic Advertising

In essence, programmatic deals are not just revolutionizing the advertising landscape but steering it towards a greener, more sustainable future. As brands increasingly prioritize eco-friendly messaging, programmatic deals are a key enabler, paving the way for a more environmentally conscious and responsible advertising ecosystem.

If you’re interested in sustainable programmatic deals, contact our team to get to know the offer. 

The post How Programmatic Deals Are Leading the Way to Sustainable Advertising first appeared on Yieldbird - Research Hub.

Even though you may try your best, you’re only human, and humans make mistakes. But wait a minute, would there be a solution to help me out with this and automate the process of Open Market pricing strategy setup? Here it is – Google Optimized Floor Prices. But the real question is, are you getting the most out of it? What about alternatives for GAM users?

In this article, we delve into the powerful mechanisms of Google Optimized Floor Prices and the other solution – Price Genius, two cutting-edge tools designed to boost your earnings. Let’s explore how these tools work, their similarities, and the key differences that set them apart.

Google Optimized Floor Prices: Unleashing the Power of Machine Learning

Google Optimized Floor Prices is a revolutionary flooring mechanism embedded within unified pricing rules. Unlike traditional floor prices and target eCPM, it harnesses the prowess of machine learning to adjust floor price levels dynamically. The primary objective? Maximizing a publisher’s long-term revenue while preventing excess unfilled impressions.

Key Features

• Automatic Adjustment: Fully automated, it ensures hassle-free operation on any part of the publisher’s inventory.
• Overrides Unified Pricing Rules: Google Optimized Floor Prices take precedence over unified pricing rules, except for advertiser-specific rules.
• Experimentation: Publishers can set up experiments to optimize the performance and maximize revenue potential.

Price Genius: Elevating Revenue with AI-Powered Precision

Price Genius is an AI-powered automatic floor price management tool designed to elevate publishers’ revenue. This solution employs machine learning algorithms to determine optimal daily floor prices for each managed ad unit, ensuring a data-driven approach to maximize earnings.

Key Features:

• AI-Powered Precision: Utilizing machine learning, Price Genius selects optimal floor prices on selected ad units daily to maximize revenue.
• Benchmarking Mechanism: Equipped with a built-in benchmarking mechanism and a dedicated reporting dashboard for performance evaluation.
• Traffic Segmentation: Uses key values to split traffic, aiding in modeling and benchmarking for better decision-making. 

See Price Genius in action:

• How Wallapop Optimized Pricing Strategy and Generated 8.6% Uplift
• How Taboola News Optimized Pricing and Generated 11% Uplift With Price Genius

Can Google Optimized Floor Prices and Price Genius Coexist?

While both tools share similarities, they operate in distinct ways, making simultaneous use challenging. Google Optimized Floor Prices and Price Genius cannot handle the same part of the inventory and traffic concurrently. However, publishers can leverage the strengths of both tools by using Google Optimized Floor Prices on the benchmark part of the traffic, while Price Genius will manage the rest.

Key Similarities:

• Machine Learning-Based: Both tools harness the power of machine learning for automatic floor price adjustments.
• Automation: Fully automated processes ensure efficiency and ease of use.
• No Interference: Both tools will override other rules (except advertiser-based rules) on the traffic they handle.

Key Differences:

• Frequency of Adjustments: Price Genius maximizes daily revenue with frequent adjustments, while Google Optimized Floor Prices prioritize long-term value preservation with less frequent adjustments.
• Reporting and Benchmarking: Price Genius provides a dedicated reporting dashboard and built-in benchmarking mechanisms, whereas Google Optimized Floor Prices relies on the publisher for performance evaluation.

Price Genius is GAM’s Perfect Extension

In conclusion, Google Optimized Floor Prices and Price Genius are powerful tools to maximize publishers’ revenue potential in the dynamic world of digital advertising. 

Do you want to challenge Google Optimized Floor Prices from your GAM? Experience the benefits firsthand by trying a free Price Genius trial. Witness the seamless integration of AI-powered precision into your ad revenue management. Don’t miss out—empower your publishing journey now!

The post Open Market Pricing Strategy: Google Optimized Floor Prices and Price Genius first appeared on Yieldbird - Research Hub.

What does it signify? Join us as we unravel this mystery in the following paragraphs.

Price Genius: The Perfect Extension to GAM

Price Genius is an AI-powered tool designed to enhance programmatic ad pricing strategies. You might be wondering why you need Price Genius when Google Ad Manager (GAM) seems to do the same job. Well, Price Genius is not a competitor; it’s a perfect complement to GAM, making your ad pricing even more effective.

Let’s delve deeper into Price Genius’s inner workings. When publishers set their floor prices at various levels, these prices remain static until someone manually adjusts them in GAM. With Price Genius, this process is automated through machine learning algorithms, eliminating the need for human intervention. Furthermore, it updates floor prices daily, whether on a regular weekday, a weekend, or a holiday – all without delays.

How Exactly Does The Algorithm Work?

Typically, traffic is divided into two segments: 90% is optimized by Price Genius, while the remaining 10% serves as a benchmark for evaluating price strategy efficiency. The percentage of segments can be different, depending on the publisher’s choice. The algorithm aims to identify the optimal floor price that maximizes revenue. When Price Genius spots an opportunity, it adjusts the prices, leading to uplifts.

But What If The Uplift Is 0%?

A 0% uplift doesn’t signify Price Genius’s failure; quite the opposite, in fact. It indicates that your initial prices are already optimal; hence, Price Genius can’t make them any better. Congratulations!

What’s Next?

Even in this scenario, you can still benefit from Price Genius as a cost-free solution. It acts as a guardian for your pricing strategy, always ready to step in if necessary. Keep in mind that once prices are set, they may not remain ideal forever. And here’s something crucial to note: you don’t actually pay for Price Genius. It generates uplifts for you, translating into additional revenue, and we only take a tiny percentage of this extra profit.

Make the Most of Your Pricing Strategy: Try Price Genius

You may believe your pricing strategy is already perfect, but how can you be certain without checking? Price Genius offers the best way to find out. If everything is set up optimally, you’ll see a 0% uplift, providing double verification at no cost. However, if there’s room for improvement, you stand to benefit significantly. Give Price Genius a try today and make the most of your pricing strategy.

See Price Genius in action:

• How Wallapop Optimized Pricing Strategy and Generated 8.6% Uplift 
• How Taboola News Optimized Pricing and Generated 11% Uplift With Price Genius

The post Why Is a 0% Uplift in Price Genius Not a Failure? first appeared on Yieldbird - Research Hub.

Artificial intelligence offers publishers many opportunities, particularly in advertising and programmatic ads. In this comprehensive article, we delve into the realm of AI for digital publishers, exploring its multifaceted role in reshaping the industry, focusing on aspects different than content creation. 

Let’s explore!

What’s Artificial Intelligence?

Everyone talks about artificial Intelligence and how it influences our lives and business, but what exactly is AI? Artificial intelligence refers to the capability of machines, particularly computers, to perform tasks that typically require human intelligence. This includes understanding language, recognizing patterns, solving problems, making decisions, and learning from experience. AI systems are designed to mimic human cognitive functions and can process and analyze large amounts of data to achieve specific goals or tasks.

AI for Digital Publishers – Content Creation

AI has become a widely embraced tool within the publishing sector, primarily for its ability to streamline content creation. It excels in producing automated content, encompassing a spectrum from articles, reports, and summaries to dynamic visual elements like graphics and videos. Beyond content generation, AI also lends its prowess to tasks like editing, proofreading, and seamless text translation. Furthermore, it plays a vital role in optimizing content for search engines. Notably, AI extends its influence to distribution by identifying optimal channels and timings, ensuring precise delivery to the intended audience.

Source: The Role of Artificial Intelligence in Publishing 

But how does AI relate to programmatic advertising?

AI’s Role in Digital Publishing Beyond Content

1. The Power of AI in Ad Viewability

As we’ve shown in our previous article on How to Improve Programmatic Ads Viewability Without Affecting User Experience, viewability is vital in the ad world. AI-powered solutions enable publishers to enhance ad viewability by analyzing user behavior and engagement patterns. By understanding when and where users are most receptive, publishers can strategically place ads for optimal visibility. This boosts ad performance and fosters a positive user experience, as relevant and well-placed ads seamlessly integrate into the user’s browsing journey.

AI has the capability to manage the strategies that enhance viewability, and these strategies are actively employed. This functionality can be accessed using Viewability Tools designed for mobile devices. Publishers have the flexibility to decide the extent to which they want their ads to be visible on their website based on their desired impact on user experience. The system automatically enables particular viewability-boosting strategies only if they actually increase viewability.

2. Data-Driven Insights

AI-driven programmatic ads provide publishers with invaluable data insights. Publishers comprehensively understand their audience’s behaviors and interests by analyzing user interactions and preferences. With this information, publishers can fine-tune their content and ad strategies, delivering personalized experiences that resonate with users on a deeper level.

3. Consistency Across Platforms

AI bridges the gap between advertising channels, ensuring a cohesive and integrated user experience. Publishers can leverage AI to manage cross-channel campaigns, tailoring content and ads to suit each platform’s unique characteristics. This consistency enhances brand identity and user engagement, reinforcing the publisher’s presence across multiple touchpoints.

4. Automated Brand-Advertiser Alignment

This multifaceted process involves leveraging advanced AI algorithms to seamlessly match advertisers with content that resonates most effectively with their target audience. By adeptly aligning content and advertisements, digital publishers can not only enhance user engagement but also unlock substantial revenue potential. This innovative technique ensures that suitable ads are seamlessly integrated into the proper context, delivering a more coherent and engaging user experience. 

5. Floor Prices Optimization

Programmatic advertising flourishes through real-time data analysis, with AI serving as its core engine of efficiency. AI-driven algorithms consistently monitor the ad environment and estimate the best floor price at a particular moment. This immediate optimization guarantees publishers extract optimal value from their inventory and generate higher revenue. 

A prime illustration of this capability is Price Genius – a fully automated solution. Whether it’s a regular workday, weekend, or holiday, the product employs machine learning algorithms daily to establish the best floor prices. Moreover, unlike manual work, it can react in seconds in case of any errors and handle them equally fast.

6. Predictive Analytics for Future Success

AI-powered programmatic advertising is not just about the present—it’s about predicting the future. AI-enabled predictive analytics empower publishers to anticipate market trends and user behaviors. By identifying emerging patterns, publishers can make informed decisions about content creation, ad placement, and audience targeting, positioning themselves for long-term success.

7. Ensuring Brand Safety

AI can continuously monitor digital platforms in real-time, instantly flagging content that may pose a risk to brand safety. This allows for immediate action, AI can automate the process of blocking content or websites that do not meet brand safety standards and whitelisting those that are safe. This ensures that ads are placed only in brand-safe environments.

A real-world solution working this way is a tool designed by GeoEdge. The company’s machine learning models look into creatives and landing pages for malicious characteristics to prevent or stop the execution of malvertising in real time. It allows AI to secure the user experience with malvertising prevention.

8. Fake Traffic Detection

Imagine AI as the Sherlock Holmes of the digital world, constantly on the lookout for suspicious characters. AI uses algorithms to spot strange behavior patterns, like mouse movements, keyboard inputs, and more. It’s possible because artificial intelligence can watch your every move on websites and apps. It knows that real people scroll, click, and chat with content, while bots tend to act like they’ve had too much espresso, going click-click-click at warp speed. Once AI spots a bot, it doesn’t hesitate to stop it and block it from future violations.

The AI Revolution in Ads Monetization

As you can see, artificial intelligence has revolutionized the digital publishing landscape by offering solutions beyond content creation.

This paradigm shift empowers publishers to fashion ad experiences that are not only captivating but also engender a deeper, more profound resonance with their target demographics. By embracing the full spectrum of AI’s capabilities, publishers have a potent toolset to craft narratives that seamlessly meld with their audiences’ preferences and aspirations.

Experience firsthand the potential that lies within the realms of AI-driven solutions. Get in touch now to begin your thrilling journey with the Yieldbird Platform’s AI-driven solutions. Check how others used the power of artificial intelligence in programmatic advertising by reading our case studies, or dive in hands-on with a complimentary 30-day trial. Join the AI revolution and supercharge your ad monetization today!

The post AI for Digital Publishers: Elevating Advertising Strategies first appeared on Yieldbird - Research Hub.

In this article, we will explore the sources of troubleshooting revenue drops and discuss how to differentiate between general market trends and areas that need improvement. Let’s start by highlighting some crucial principles to remember during the troubleshooting process.

Keep reading if you’re eager to take charge of your ads revenue and find practical solutions for revenue fluctuations in programmatic advertising. Let’s dive into ad troubleshooting and learn how to safeguard your revenue from pitfalls.

Sources of revenue troubleshooting

Before we can conclude that the drop in revenue is dictated by reduced spending by most advertisers, we need to check several elements to be sure that technical errors – in the form of both reduced ad request issuance and declines on the part of a specific revenue source [i.e., reduced spending on the part of Ad Exchange, Open Bidding, Header Bidding] or even a particular SSP – are not responsible for the sudden drop in revenue.

Before we go through the potential causes of the declines, let’s introduce some important principles:

1. General to Specific Approach: Start by narrowing down the responsible area as much as possible. Determine whether the drop is related to desktop or mobile, below-the-fold or above-the-fold units, anchors, video, or interstitials. The more detailed the identification, the quicker you can pinpoint the issue in your inventory range.

2. Distinguish Anomalies: Understanding the setup of your ad space is crucial to know what behaviors are not standard for our settings. Be aware of whether you expect high occupancy with direct campaigns, Header Bidding involving multiple SSP platforms, or a video player with an ad block option.

3. Consider Seasonality: Seasonality declines are due to the cyclical nature of demand and consumption of a given service. Thus, we know that TV programs experience increases on weekends and holidays (when else can you manage to sit in front of the TV, right?), while news services, on the other hand, experience declines on weekends. For this reason, we expect increases during Black Week, decreases after Christmas Eve, and a mediocre Q1 simultaneously, but it all depends on the type of publisher and its content category.

Reasons for revenue drops and how to deal with them

Once we know where it fell, what fell, and that it was not due to seasonality, we can consider the most important thing: WHY DID IT FALL?

We’ll start with the most trivial issue (and we won’t dwell heavily on it):

1. Traffic

We are examining whether the magnitude of the revenue declines is related to the decline in the volume of advertising requests per unit and the decrease in pageviews on the site. Here, it is essential to consider an implication, meaning the occurrence of these two factors simultaneously; otherwise, we will encounter a problem.

Revenue is generally expected to decrease less than advertising requests and page views. This is attributed to the fundamental economic law of supply and demand, where a decrease in resource availability generates a higher demand for them. A well-adjusted pricing policy is necessary to achieve this relationship, maximizing revenue by “squeezing” as much as possible out of advertisers, considering the lower volume of potential ad impressions.

If the requests experience a different trend than the page views, it indicates a technical problem, and some collision is likely preventing the ad requests from reaching the ad server. In such cases, we need to consult with an AdOps specialist who can review the technological elements and identify any conflicts that might be blocking the broadcast of the requests.

However, let’s assume that traffic has increased, but revenue continues to fly downward. In that case, it’s a good idea to focus on the next aspect of…

2. Changes in specific emissions

This one is much more complicated, and here’s why: within the Open Market, a drop in one impression usually leads to drops in subsequent ones. This happens due to the competition between impressions and the aggressive behavior of advertisers. When Ad Exchange buyers generate a lower eCPM (and consequently lower prices in programmatic), they pull other competitors to do the same.

However, let’s start with something easily and quickly checked (if you have the right technology). We need to verify whether all intended platforms in Header Bidding have access to buy space. This can be achieved with a tool that pins bids with SSPs, ideally in real-time. Google Ad Manager doesn’t handle this issue very well, as it not only has reporting delays but also incompletely reflects the actual performance of platforms. Here, tools with direct connections to SSPs, such as Prebid Stack, come to the rescue. Such a tool’s reporting system can swiftly identify which platform has stopped bidding for a given space due to changes in ads.txt. In more complicated cases, the customer service of a particular SSP can assist.

Once we are certain that all SSPs are bidding as they should, we can move on to checking Open Bidding. Here, we need to ensure we haven’t lost any Yield Partners from the auction. Finally, we can proceed to examine Ad Exchange. Open Bidding was intentionally only briefly mentioned because I haven’t encountered a case that significantly influenced monetization.

Ad Exchange, on the other hand, poses quite a challenge to troubleshoot because we are treading dangerously close to a critical boundary: DEMAND. However, at this point, we won’t delve much deeper into that complexity. We’re still a little way off from crossing that line!

What predominantly impacts Ad Exchange buyer traffic is clicks (AdSense’s legacy lives on forever), and the CTR ratio should be the first thing to check. If the ratio of clicks to impressions has dropped, it indicates that AdWords has also experienced a decline and possibly led to more advertisers withdrawing from the Authorized Buyers pool. When we identify that the CTR is responsible for this turmoil, we have several approaches to attempt to remedy the situation: 

• Check the ad grid: Drops in CTR are often linked to reduced visibility (the chicken and egg dilemma). This indicates changes in the ad grid, such as moving units to less accessible places for users or element collisions on the page (i.e., some panel obstructing access to the ad unit). Improving the ad grid can lead to a return to normal revenue levels.
• Verify with Google: Google may have subjected the unit to a click filter. This can happen when the CTR exceeds 1% due to the unit being too close to clickable elements on the page. This process may take some time. Improving the page layout and requesting a re-review from Google’s side could lead to revenue recovery in approximately two weeks.

If the CTR turns out to be normal, we need to delve deeper into the buyers. Analyzing advertisers will answer whether the declines were influenced by one particular buyer setting the tone for space sales or if it’s a collective trend of reduced spending. In the latter case, we can confidently attribute it to a decrease in demand. Our options are limited then, and we simply have to wait for better times.

IMPORTANT: It is crucial not to switch these steps, as a decrease in CTR will also lead to a corresponding drop in traffic from advertisers. These two different causes of revenue declines must be distinguished.

3. Distribution of guaranteed emissions

The last aspect is more advanced, as it involves the distribution of guaranteed emissions, which complicates the picture we outlined earlier. When analyzing declines in programmatic, we are operating in two dimensions of advertising requests and revenue:

• Overall: the total volume of advertising requests and the total revenue generated.
• Programmatic: what programmatic had a chance to tap into by the existence of direct campaigns, and with them, it almost couldn’t fight for space (for less complication, let’s leave out the existence of First Look and Standard campaign options, which can admit bids from Open Auction).

This is because the budgets of direct and programmatic campaigns are usually separate (although one impression sneaks the other the opportunity to buy space). Because of this, we encounter sudden drops in programmatic revenues due solely to an increase in the share of direct campaign impressions. This has the effect of reducing the space for programmatic impressions.

Exploring the hidden dynamics of revenue fluctuations in programmatic advertising

Examining revenue fluctuations in programmatic, particularly in the context of the Open Market, poses challenges due to the dynamic nature of demand. Differentiating between aspects we can improve and the overall market trend requires a comprehensive analysis of several key elements. Investigating whether the sudden drop in revenue results from technical errors, such as reduced ad request impressions or decreased spending from Ad Exchange, Open Bidding, Header Bidding, or a specific SSP, is crucial.

Understanding the underlying causes of these declines demands a detailed analysis and a deep understanding of various aspects of the advertising market. To achieve this, utilizing tools that aid in monitoring changes and quickly identifying errors becomes essential. Often, distinguishing these declines from changes in demand is not easy. Gaining proficiency in analyzing selected elements will help prevent jumping to hasty conclusions.

LET’S GET IN TOUCH!

Bartłomiej Oprządek
Regional Growth Director
publishers@yieldbird.com

[contact-form-7]

The post How to Deal With Troubleshooting Revenue Fluctuations in Programmatic Advertising? first appeared on Yieldbird - Research Hub.