This recent political season has been full of twists and turns, and that doesn’t seem to be changing anytime soon. We’re all looking for ways to contextualize this presidential election cycle to keep our heads wrapped around what’s happening. 

A recent analysis by GumGum sheds light on how the media portrays Vice President Kamala Harris and Former President Donald Trump in political discourse. Utilizing their advanced contextual advertising tool, GumGum examined over 5 million pages of political content from late July to uncover trends in media coverage and sentiment. 

The findings reveal a significant disparity between mentions and sentiment: the media mentioned Donald Trump more than 1.7 million times, while Kamala Harris was mentioned around 1.36 million times. Notably, Kamala Harris’s media presence surged by 388% on the day of her candidacy announcement.

We chatted with Hailey Denenberg, VP of Strategic Initiatives, Data at GumGum, to explore these findings and how contextual advertising can help political advertising campaigns.

Leveraging Contextual Analysis for Political Campaigns

Andrew Byrd: Why did GumGum decide to conduct this study? What were your goals, especially in relation to your contextual tool?

Hailey Denenberg: Our contextual technology, developed over the past 15-16 years, uses computer vision and natural language processing to understand content as a human would. With the upcoming political campaign, we wanted to leverage this technology to track trends over time.

Given our extensive programmatic integrations with large platforms like DSPs and SSPs, we have a vast amount of classified content at our disposal. Following Kamala’s presidential announcement, we found it particularly interesting to analyze how the open web and editorial content discussed Kamala versus Trump over the last two weeks in July. We focused on mentions and sentiment, aiming to understand how each candidate is perceived and discussed in content.

Understanding how different editorial voices and platforms perceive and talk about each candidate, especially in terms of sentiment, provides valuable insights into the broader public discourse. Our goal was to use this analysis to uncover trends and patterns in how content about these candidates is produced and consumed, ultimately offering a deeper understanding of the political environment through the lens of digital content.

AB: The upcoming election is full of unexpected developments, especially after Kamala’s recent announcement and the campaign’s strategic execution. Given this dynamic, can you explain how contextual analysis helps understand political coverage?

HD:  Contextual analysis has many applications, especially in presidential campaigns. Advertisers should stay updated on how content is trending, positively or negatively. Think of it as playing offense and defense. For instance, if mentions of Kamala’s opponent, Trump, spike negatively, her campaign can play offense by surrounding that content with positive messaging about Kamala.

On the other hand, they might want to avoid any negative mentions of Kamala to keep the messaging streamlined, which is more of a defensive strategy. Understanding these content trends as different announcements unfold allows campaigns to use advanced contextual targeting. They can target all positive or negative content that mentions specific candidates, leveraging this analysis to shape their messaging effectively.

AB: How can publishers benefit from these technologies?

HD:  Yes, publishers can significantly benefit from contextual technologies. For example, news publishers can strategically package their inventory by grouping positive political news and offering it to brands comfortable with political content but wanting to avoid association with sensitive issues like abortion or immigration. This allows them to monetize content that aligns with the advertiser’s brand safety requirements.

Advanced contextual technologies not only understand the sentiment of the content but can also identify and filter out specific sensitive topics that an advertiser may want to avoid. This capability is crucial for maintaining brand safety while still allowing advertisers to participate in positive, relevant conversations.

Addressing Brand Safety Concerns

AB: At our last conference, there was a significant discussion about brand safety. Advertisers naturally want to avoid being associated with certain content, but publishers face revenue challenges due to these restrictions. How does GumGum approach brand safety, especially in contextual advertising?

HD:  Brand safety has become even more critical recently, especially with the news around the dissolution of VM and GARM. While we align with the GARM framework, we’ve also developed our custom threat categories beyond the usual “Dirty Dozen” like violence.

This is important because while positive contextual targeting aligns with preferred categories, there’s a strong demand for blocking or negatively targeting specific categories. Where GumGum stands out is in video analysis. For instance, in political advertising, which is huge for TV and CTV, most providers struggle to analyze what’s happening within the video content. 

However, our advanced video contextual technology allows us to explore the complete audio transcription and perform scene-by-scene or frame-by-frame analysis. This helps us determine what parts of the video are brand-safe or suitable according to our established threat levels, ensuring more precise targeting and reducing over-blocking.

Reaching the Right Audience and Environment

AB: How does contextual advertising help political ads reach the right audience in the right environment?

HD: At GumGum, we focus on placing ads where they are most relevant, using a deep understanding of content. For example, if someone is reading an article about the election and sees an ad with positive messaging about a candidate like Trump, it aligns with what they’re already thinking about. 

This increases the ad’s effectiveness, especially when combined with geo-targeting in swing states. It’s all about reaching consumers at the moment they’re considering a topic, which can influence their actions.

AB: Does your approach to contextual advertising change depending on whether it’s on mobile, desktop, or other platforms? Or is there generally much overlap?

HD: Generally, there’s significant overlap, as reaching the consumer in the right mindset is often based on the content itself, which tends to be consistent across devices like mobile and desktop. However, attention models can vary depending on the environment. We have different panels for mobile and desktop, and we’re beginning to explore video. While I don’t have definitive data yet, it will be interesting to see if there are differences in optimal attention times across these environments.

AB: What final advice would you give to advertisers and publishers considering political advertising and contextual targeting?

HD: I advise them to gather as much data and insights as possible from various sources. This will help you uncover unconventional ways to position your brand or supply. Instead of sticking to obvious choices, explore new audience segments. For instance, while Nike might seem like a fit for sports content, insights might reveal it’s also popular in Home and Garden content due to a current trend. By broadening your perspective, you can enhance your targeting strategy and reach new audiences more effectively.

The post How Contextual Analysis Shapes Political Campaigns: Insights from GumGum’s Hailey Denenberg appeared first on AdMonsters.

At this point, it’s not news that years of ongoing changes in data privacy regulation have created massive amounts of change in the way that data is being used (or not used) across the advertising industry.

As IAB Tech Lab CEO, Anthony Katsur, often says, “Just like energy, finance, or healthcare, advertising is now a regulated industry.” As part of this trend, publishers face challenges in creating sustainable revenue growth.

Navigating Data Privacy in Advertising

Whether it’s the continuing decline in ad revenue that digital publishers are grappling with or the never-ending struggle that the streaming television industry is having to reach profitability it’s clear that owners and publishers of media are feeling the effects of these changes.

One of the areas where these changes are most visible is within the publisher’s data technology stacks. Increasingly, publishers are centralizing the many data sources they need for monetization within their data warehouse. While this evolution brings the promise of insights and connectivity, publishers also need a purpose-built application layer to help them activate and get the most value from their data.

DMPs: From Central Role to Obsolescence

For years publishers relied on DMPs to be at the center of their monetization efforts. As cookie-based monetization becomes less and less dependable and publishers’ distribution channels continue to fragment outside of the web these systems have failed to develop new solutions for key functions like app and historical data collection, 2nd-party audience enrichment, and programmatic activation.

This leaves most legacy DMPs relegated to web-based data collection, audience segmentation, and simple ad-serving activation. Additionally, traditional DMPs were not built with important capabilities such as data clean rooms, identity resolution, and PETs which are extremely important in our privacy-centric world.

Data Warehouses: A New Hub for Monetization

Many DMPs have responded by integrating large data sets through mergers and acquisitions to help fill gaps around identity, some are playing catch up by trying to build more privacy-centric features like identity and clean rooms, and others have decided to completely go out of the business. A response to this lack of innovation by DMPs in recent years has been more organizations investing in their data warehouse to centralize their various audience data sources. The question is, is the data warehouse alone enough?

The Missing Piece: Purpose-Built Applications

As we talk to customers in the market it’s clear that they need applications that can work with their data warehouse to create efficiencies and grow their revenue. One of the biggest challenges is actually activating data.

Data warehouses often rely on applications and integration providers to make data more actionable which leaves publishers building expensive custom solutions and navigating complicated operations.

Similarly to how the Composable CDP movement has stepped up to help marketers evolve how they activate data in their warehouse, media owners and publishers (and new companies like retail media) need solutions that are purpose-built for both the era of privacy as well as ad monetization use cases.

Embracing the Future of Audience Monetization

Audience monetization platforms of the future need to be able to combine the streamlined audience building and activation (in both programmatic and direct)  that legacy DMPs relied on, while also allowing for more complex tasks like normalizing various data sources, running complex identity resolution models and collaborating within data clean rooms.

As free and scaled 3rd-party cookie data goes away the monetization is shifting to the publishers and media owners who are investing appropriately in their 1st-party-data, and there’s a major opportunity to create profitable growth. Investing in technology to help power this growth is crucial and will separate the winners from the losers during this period of change.

The post The Data Warehouse Has Replaced Many DMP Functions, but Is It Enough for Publisher Data Monetization? appeared first on AdMonsters.

Media and ad tech conferences have been dominated by discussions about AI and cookie deprecation over the past couple of years. These are important topics, but one equally important topic gets less attention: data strategy.

Everyone wants the mystical data lake that will solve all their data needs and finally make them “data-driven,” the thing everyone claims to be but few actually are.

A data lake can be a great thing but, not unlike a normal lake, it can also be filled with toxic waste and be more like a dump than a beautiful lake anyone wants to touch. Just putting your data in a data lake doesn’t actually fix anything. A data lake is just a fancy marketing term for a database. 

The key to enabling your organization to make data-driven decisions is to make the data accessible to the whole organization and different stakeholders, including those who don’t have a computer science or data science background. 

For example, your ops team may want to know the latency of ad loading or be able to see how many impressions an ad unit generated for a certain audience. They shouldn’t need to know SQL to achieve that.

A SQL prompt (even though it is powerful and one of my favorite tools) won’t help, and a static dashboard won’t help either because you can only think of and design so many things ahead of time. You need something else.

3 Steps to Unlock Data for the Entire Publisher Organization

So, how do you make your data truly accessible — and understandable — to every relevant person within your organization?

1. Ensure you have a solid ETL pipe. You want all the data in one place, but more importantly, you want it normalized across your sources so you are actually comparing apples to apples when reporting. A business user shouldn’t need to know how Magnite or Index Exchange defines their ad types. Their tools should account for these differences.
2. Make the data accessible. Enable the data to be queried with easy-to-use tools that take care of the logic in the background. People are strapped for time, and if it is a hassle to get to the data — maybe they have to submit a ticket to the data science team and wait two weeks to hear back — they are probably not going to do it.
3. Monitor the data for accuracy. One thing that will definitely kill a data strategy is inaccurate or out-of-date data. If users can’t trust the data, they will not use it, instead retreating to manual Excel spreadsheets or other less effective methods.

A data lake won’t make publishers data-driven. But getting all their data in one place is indeed the first step to more efficient, data-driven decisions.

Normalizing the data, making it easy to query, and shoring up its accuracy will help publishers get the rest of the way so that “data-driven” is a real way of doing business and not just a nice-sounding slogan.

The post Data Lakes Won’t Make Publishers Data Driven. Here’s What Will appeared first on AdMonsters.

Algorithms shape our world in ways we never imagined, influencing everything from the news we consume to our political beliefs. In this article, Søren H. Dinesen, Co-founder and CEO of Digiseg explores critical trends in digital media through a curated list of must-read books, exploring the intricate relationship between algorithms, media, and privacy. These works dissect the creation of echo chambers, the rise of filter bubbles, and the profound impact these phenomena have on political discourse and societal polarization.

The Algorithmic Influence: Shaping Our News and Beliefs

Dinesen reviews key titles that unravel how algorithms narrow our perspectives and distort our cultural and democratic frameworks. By examining these influential books, he provides a comprehensive overview of the challenges and implications of living in an algorithm-driven world, urging us to rethink how digital media shapes our reality.

Let’s dive in.

The Media Manipulation Machine: A Closer Look

In their book The United States of Distraction: Media Manipulation in Post-Through America, Dr. Nolan Higdon and Mickey Huff tackle urgent questions facing America. Key among them: How did we get to the point where citizens decide what’s true based on the number of people who believe it, rather than facts or reality?

Consider some of the outlandish things Americans believe:

• Three in ten believe the 2020 presidential election was stolen from Donald Trump
• About 25% say there is at least some truth that COVID was planned
• 22% believed that the “storm” predicted by QAnon would occur
• 7% believe chocolate milk comes from brown cows

One would think it’s easy enough to dispel such ridiculousness … isn’t that what the media is supposed to do? Provide reality checks for a reading public? Sadly, vetted journalism isn’t convincing anyone because distrust of the media is at an all-time high.

Echo Chambers: The Rise of Filter Bubbles

How did we arrive at such a sad state of affairs? Over the past 10+ years, numerous scholars have concluded that the root of all our troubles begins with the deployment of algorithms. Algorithms are designed to present us with the information we have shown a propensity to consume, which in turn, limits the range of information we see and what we believe.

The Impact of Digital Isolation on Democracy

In 2011, Eli Pariser coined the term “filter bubbles” in his book, The Filter Bubble, What the Internet is Hiding from You. Two years earlier, Google had deployed AI to its search results, narrowing sources to just those the algorithm predicts will interest the user.

To Pariser, it was the dawn of the great dumbing down of people as the range of information they were exposed to was limited. We thought we had access to the full spectrum of global information — that we were traveling along a great information superhighway — but with algorithms deciding what we see, that is no longer the case. An important onramp — Google Search — merged with its algorithmic offramp, unbeknownst to us.

Radicalization Through Algorithms: How Big Tech Went Wrong

But that was only one of the challenges. The other was how it groups us into clusters of people who think like us, trapping us into “filter bubbles.” Today we call those bubbles echo chambers.

All of this narrowing and grouping is fueled by AI — algorithms that track our online activity and preferences, then selectively show us content that aligns with our existing beliefs and interests. With enough positive reinforcement from others on the internet, it comes as no surprise people can fall for conspiracy theories.

It’s one thing to believe that chocolate milk comes from brown cows (when I was young I thought trees made wind by bending back and forth). But when AI-curated content leads someone to ransack a Target outlet or bring a gun to a pizza joint to free kids held as slaves, it’s time to admit we have a problem.

This is a theme that is echoed by Stanford University Professors Rob Reich, Mehran Sahami, and Jeremy M. Weinstein in their book, System Error: Where Big Tech Went Wrong and How We Can Reboot. They argue that when Big Tech’s hyper-focus on a single metric — say YouTube’s decision to prioritize time spent consuming videos — bad things happen. They’re not wrong, as it’s now understood that the tuning of an algorithm to prompt binge-watching leads to the radicalization of users and the spread of conspiracy theories.

Memes as Propaganda: The Online Battle for Truth

In the book Meme Wars: The Untold Story of the Online Battles Upending Democracy in America, Harvard’s Joan Donavan and others argue that memes serve as the bedrock of conspiracy theories, helping to make the unbelievable believable. Clever, and easy to go viral, memes like “Stop the Steal” can move entire cohorts of people to act violently and in anti-democratic ways.

Conspiracy Theories and Algorithmic Fuel: A Dangerous Mix

Memes are particularly effective at swaying people and recruiting them to extremism (the book Accountable: The True Story of a Racist Social Media Account and the Teenagers Whose Lives It Changed describes how Googling “black on black crime” will lead people down a white supremacist rabbit hole). 

One of the reasons why memes are effective at converting people is that they break down offensive and outlandish beliefs into bite-sized and memorable riffs. They serve as a starting point to a process that slowly eases people into horrific belief systems. Recruiters of extremist beliefs understand this, and they’ve honed their skills. This, by the way, isn’t just an American problem; it’s global. 

Cultural Flattening: Algorithms Blunting Creativity

In her book, The Next Civil War, Stephanie Marche also warns that algorithms make us more extreme, but she says they add another wrinkle: they make it easy for racist and violent people to find one another. In the days before the Internet, a disaffected youth would have trouble joining a white supremacist or neo-Nazi group because such people were  underground. Today, social media algorithms will recommend them as something users may be interested in.

But it’s not just the worst parts of society that algorithms distort. Algorithms are also blunting the best parts of cultural life, as Kyle Chayka’s recent book Filterworld: How Algorithms Flatten Culture clarifies.

He warns that algorithms have effectively constricted our access to information, serving the lowest common denominator of content because such blandness will appeal to the largest number of people. This results in books, music, and even physical spaces such as cafes, all reading, sounding, and looking alike because algorithms have taught us to expect no better. Groundbreaking ideas are penalized because they don’t have the same level of virality as the more market-tested ones. Put another way, algorithms are flattening the global culture.

A Broken Promise: The Internet’s Failed Information Superhighway

The internet was supposed to be an information superhighway, providing unrestricted access to ideas to anyone with an interest in learning. But instead of providing easy access to ideas and information, the algorithms that now rule the Web are shrinking what we’re exposed to, and in many ways, our free will. Instead of being presented with new ideas, we are grouped into cohorts of people who amplify our beliefs and prejudices, setting the stage for outlandish, post-truth beliefs.

The Path Forward: Reclaiming Our Shared Reality

Shared truths are essential to functioning societies—- this candidate won an election, the Earth is round, not flat, and climate change is an urgent issue that needs addressing. Without a basic set of facts we can all believe in, humanity will only get more mired in the deception and misinformation that is propagated by AI, with people of differing opinions retreating further into their corners.

It’s our duty to prevent that from occurring.
___

This is the third article in Digiseg’s Privacy Series. The first, Privacy Signals, AI in Advertising & the Democratic Dilemma, takes a broad view of the issues of private signals and one-to-one signals as we see them. The second, Surveillance Capitalism 2.0, examines how emerging privacy-centric solutions track user behavior just as much as cookies ever did.

The post Echo Chambers & Political Discourse: Essential Reads on Media Manipulation and Algorithms appeared first on AdMonsters.

Given the breakneck speed of digital innovation nowadays, publishers need a competitive advantage. Standing out comes from the power of rapidly and accurately analyzing data. Take The Weather Company, for example, the global titan in weather data and forecasting, is supercharging their mobile analytics game after joining forces with Lotame and AWS Clean Rooms.

This powerhouse collaboration has slashed insight generation time by an eye-popping 98% and boosted query efficiency sevenfold, enabling The Weather Company to deliver data that’s not just fast but razor-sharp and hyper-relevant to its clients and partners. AWS Clean Rooms facilitates this by providing a secure environment where companies can collaborate on datasets without sharing or copying the underlying data, enhancing data privacy and compliance.

But let’s talk specifics. By digging deep into the behaviors and preferences of their travel audience, The Weather Company unlocked insights that go beyond the surface, fine-tuning strategies for travel advertisers. For instance, by analyzing user interactions on The Weather Channel mobile app, they can distinguish between frequent and infrequent travelers and preferences toward air versus land travel. This granular insight has allowed The Weather Company to craft finely tuned, targeted, and effective advertising strategies that deliver exceptional results for their advertising partners.

In our exclusive Q&A, I spoke with Dave Olesnevich, Head of Data & Advertising Products at The Weather Company, to unpack the technical challenges and victories of the integration. We explored how AWS Clean Rooms enhances data privacy and compliance, tackles the unique hurdles of mobile data, and shapes the future of ad targeting and campaign efficiency.

Lynne d Johnson: Given the increased scrutiny on data privacy and compliance, how does the AWS Clean Room technology help The Weather Company navigate these complexities? How has this transformed your day-to-day operations?

Dave Olesnevich: AWS understood the assignment when it came to creating a privacy-forward environment where multiple parties can collaborate with data quickly and easily. CISO’s office is more amenable to the clean room environment versus moving data out of house for engagements.

The AWS Clean Room isn’t magic though — participants have to bring high-quality data to the table in order to create insights that become actionable. We can control what data is accessible on a case-by-case basis, which is a table-stakes feature. The Weather Company now has a new way of working with our customers to create value. We’re still in the earlier days of utilizing data collaboration platforms for advertising engagements at scale, and I expect a lot more usage in the future.

LdJ: With the new system reducing the insight generation time by 98%, could you discuss how this acceleration has transformed your approach to ad targeting and campaign efficiency? How quickly can changes in weather patterns now influence ad placements?

DO: Time to value is going to change when we fully operationalize the system. The value is first to our customer, we can help them achieve their desired outcomes with a reduced number of hops in the process. The LOE to produce actionable insights for the C-suite is at our fingertips, so it’s not just paid, but owned and earned for the CMO and BPO, with opportunities for the CFO and COO as well. As weather becomes increasingly more impactful to the bottom line, we can help leaders harness weather intelligence for use across their business.

LdJ: How have these faster insights already impacted a campaign or strategy? What have been the most significant impacts on your business and client interactions?

DO: Now more than ever, we’re able to develop what we call a Weather Strategy for our customers across the enterprise, with less time blocking and tackling and more time spent unlocking the value of the insights to drive desired outcomes for advertisers across their entire media mix. Like many in our ecosystem, we’ve been working with Lotame and AWS for a long time. We’re all leaning in to build the next generation of advertising.

LdJ: Looking forward, how does The Weather Company plan to further leverage this enhanced data processing capability? Are there new types of data analytics or services you’re aiming to explore that were not feasible before?

DO: We’re just getting started. Targeting, measurement, attribution. We’re working with our customers to help them understand how weather impacts their customer behaviors and their business operations. End-to-end weather impact in advertising, from planning through activation and measurement is the future state.

The post Weathering Data Storms: How The Weather Company, Lotame, and AWS Clean Rooms Supercharge Mobile Analytics appeared first on AdMonsters.

Generative AI (Gen AI) is revolutionizing digital advertising by enhancing efficiency in media buying and publisher monetization, enabling businesses to achieve unprecedented levels of efficacy and efficiency and optimized operations.

As the Director of Advertising at a forward-thinking media company, I’ve seen firsthand how this technology reshapes audience connections and drives business growth. Here are some tips and considerations for businesses to incorporate this technology and amplify growth.

Personalization That Truly Resonates

One of the most exciting aspects of Gen AI is its ability to create hyper-personalized ad experiences. We’re moving beyond basic demographic targeting by using AI to analyze massive amounts of user data. This reveals insights that allow us to tailor ads and content recommendations to individual interests, behaviors, and real-time contexts. Dynamic creative optimization, powered by AI, is a secret weapon. It autonomously generates and adjusts ad variations based on user interactions, making every impression relevant and engaging. This leads to higher click-through rates, improved conversions, and stronger ROI for our clients.

Media Buying, Supercharged

We’ve implemented AI-powered media buying platforms for real-time bidding, placement, and budget adjustments. This allows our team to focus on strategy and creativity, while the AI optimizes our campaigns for maximum efficiency and impact. Google Ads Gen AI tools now enable media buyers to create and test templates without the need for a designer and offer predictive analysis for informed decisions on churn, seasonality, ad performance, LTV, and pricing fluctuations. These advancements significantly improve campaign efficiency and effectiveness.

Agility in Real-Time

The digital world moves fast, and Generative AI gives us the agility to keep up. We can now adjust our ad and content strategies on the fly, reacting to the shift in user behavior or emerging trends in a matter of hours or even minutes. This level of responsiveness is essential for staying ahead of the competition and delivering results that consistently exceed expectations.

Overcoming Challenges, Embracing Opportunities

Of course, there are challenges to address. Organizations often face skill gaps, so investing in upskilling and reskilling programs and fostering a culture of learning and experimentation is crucial for Gen AI adoption. Ensuring data quality and compatibility with existing infrastructure is also essential. Despite these hurdles, the potential rewards are huge.

Ethical considerations are also very important. As publishers, we must responsibly steward user data, protect user privacy, be transparent about how the information will be used, and operate within ethical and legal guidelines to build trust with our audiences.

The Road Ahead

Generative AI is still in its early stages, but it’s already clear that it’s a game-changer for digital advertising. As we continue to explore its capabilities, I’m excited about the possibilities it holds for creating more meaningful connections with audiences, improving efficiency, driving business growth, and shaping the future of our industry. It’s time for all businesses to assess their current processes, stay informed about Gen AI advancements, and foster industry collaborations to maximize the benefit of utilizing this technology.

The post Generative AI: A Game-Changer for Digital Advertising appeared first on AdMonsters.

The Trade Desk’s recent release of its Top 100 List has sent ripples through the digital advertising ecosystem, sparking more drama than the latest season of your favorite reality show. 

Known for its influential role in the ad tech ecosystem, The Trade Desk’s move to create this list has sparked a range of reactions from industry experts, publishers, and advertisers alike. I sat down with Albert Thompson, Managing Director of Digital at Walton Isaacson, to get the lowdown on what this really means for publishers and advertisers.

Unveiling the List

Thompson, a seasoned professional with over 20 years of experience in advertising and marketing, provided an in-depth analysis of what The Trade Desk’s Top 100 List means for the industry and whether it reshapes the narrative of what constitutes “premium” in the digital advertising space.

“The idea of a premium internet, according to who? Premium according to what?” he questioned, pointing out that this feels like a throwback to the ComScore ratings. Remember when anything under a million visits was considered chopped liver? Yeah, we’re not trying to go back there.

The Debate Over “Premium”

Thompson didn’t mince words, emphasizing that the concept of premium status is subjective and often exclusionary. The list is as subjective as picking the best pizza in NYC. “It lacks culture, which is intrinsic to what people desire the most,” he said. While intended to highlight quality, it misses considerable cultural and contextual factors that define digital consumption today. The Top 100 might have the heavy hitters, but it’s missing the soulful, indie corners of the internet where niche communities thrive.

Attention vs. Viewability 

In the age of TikTok and viral cat videos, capturing attention is the real MVP. Thompson emphasized that we’re in an “attention era,” not just about how long an ad is seen but how deeply it engages. The Top 100 List might focus on viewability, but where’s the love for those who master the art of holding our ever-fleeting attention spans?

Thompson underscored a crucial shift: from valuing ‘viewability’ to ‘attention.’ In an era where capturing consumer attention is paramount, a list based solely on viewability metrics might not accurately reflect the real engagement levels and consumer decision paths. This shift raises questions about the relevance and utility of the Top 100 List for marketers who must navigate an increasingly fragmented digital landscape. 

Exclusions and Their Implications

Let’s talk about the elephant in the room – the glaring omissions. Critics argue that by not including diverse and culturally rich platforms, The Trade Desk might be inadvertently sidelining important voices. “Where are the Web3 platforms, AI-powered sites, luxury brands, women-centric brands, BIPOC communities?” Thompson asked. This exclusion could divert precious ad dollars away from these crucial players, leaving them out in the cold.

The Concept of Gated Communities

Thompson made a poignant point about the creation of digital gated communities. “Are we looking at an agenda where those with the most resources define what’s best for everyone else?” It’s a digital class divide, and those not on the list might feel like they’re on the wrong side of the tracks.

Global Implications 

The discussion expanded to the global stage, where Thompson questioned the relevance of an American-centric ‘premium’ concept for international audiences. “What do we say to the global internet? Is this the version of premium for people in Nigeria, the UK, or Africa? Because contextually, there’s a lot missing from this conversation.” He pointed out that the list lacks context for non-U.S. markets, potentially alienating global players.

The Role of Code on Page Intelligence 

Another key focus of the conversation was the significant shift in how digital activity is monitored and analyzed. Forget cookies; it’s all about code-on-page intelligence now. Thompson explained this as the new gold standard for measuring user engagement. It’s like knowing what people actually do with their food instead of just knowing they’ve got it on their plate. This approach could redefine what makes digital real estate valuable.

Code-on-page intelligence is a more accurate measure of user engagement and content relevance than traditional session data and cookies. This approach offers deeper insights into consumer behavior, potentially redefining what constitutes valuable digital real estate.

What Should Publishers Do? 

Thompson’s advice to publishers? Double down on your unique value and communicate that to brands. “Assert your will and relevance,” he urged. Focus on verticalization – organize content and advertising strategies by specific market segments (e.g., automotive, beauty, finance). Cater specifically to niche markets and make your unique content indispensable to brands. his method could help publishers carve out a niche and remain competitive, even if they don’t make it onto lists like The Trade Desk’s Top 100. 

Adapt and Innovate

Thompson reassured that while lists like The Trade Desk’s Top 100 have their place, they should not be the sole determinant of a digital platform’s value. “Highlighting top players is fine, but to suggest they are the only ones that matter is ludicrous,” he concluded. 

The conversation underscores the need for a more inclusive and contextually rich understanding of ‘premium’. In a digital world as diverse and dynamic as ours, success will come to those who adapt, innovate, and stay true to their unique value propositions.

The post Albert Thompson, Digital Innovator Reacts: The Trade Desk’s Controversial Top 100 List appeared first on AdMonsters.

In the introduction of this series, I raised the concern that the targeting, measurement and attributions arising in the wake of cookie deprecation won’t meet the consumer’s expectations of privacy. It’s a hugely critical issue, and one worth exploring in depth. This article does just that.

The Rise of the New Tracking Cookie

In the early days of digital advertising, nearly all ads were contextual; Google AdSense assessed web page content and if it matched the topic of an ad creative, Google would fill the impression. The challenge was that contextual targeting back then was rudimentary, leading to horribly embarrassing and often brand-safe placements. A few memorable ones include:

• A “put your feet up” ad for a travel company appeared next to an article titled “Sixth Severed Foot Appears Off Canadian Coast” on CNN.

• VacationsToGo.com banner ad over a photo of a cruise ship that sank in Italy

• Aflac, a service for employee recruitment and whose mascot is a duck and has a tagline of “We’ve got you under our wing” appeared next to a story about anatidaephobia, a disease where people believe they are being watched by a duck.

Marketers naturally wanted better tools for targeting, and deservedly so. By the mid-2000s, Web 2.0 was in full swing, with consumers increasing the amount of time they spent online and on social media, generating vast amounts of data. For marketers, it was the start of the data-driven revolution.

That revolution was powered by private signals, which are any and all signals that are tied to an individual allowing the industry to follow consumers as they go about their digital lives, whether that’s surfing the web, using apps on their mobile device, or streaming content via their smart TVs or radios. 

Initially, the main tracking device was the third-party cookie; little snippets of code dropped into the browser, unbeknownst to the user, so their every move could be logged and their future behavior monetized.

Ad tech companies and agencies retrieved that data from the consumers’ browsers and used it to make assumptions about people: users who visited a parenting site were women aged 25-to-35; users who read about new automobile models are actively in-market for a new car.

Here’s a true story about an American on the Digiseg team: She signed onto her health insurance account to check on something. Later, she saw an ad on Facebook that said something like, “Dr. Smith is in your healthcare network, schedule an appointment today.” This was far from a unique event.

For everyday citizens the message was clear: We’re watching you. For many, installing ad blocking software was an act of desperation. Such software didn’t end tracking, but at least they weren’t reminded of how much they were under the microscope of entities they didn’t know.

Consumers complained, of course. More importantly, they demanded regulators in their home states to end tracking. For the industry, that meant finding a replacement for third-party cookies, but not for tracking users.

But — and it’s a big one — blocking cookies and ceasing the tracking of users in this industry seem to be two different things, though why that is the case is beyond us. Users still emit private signals as they go online, and the industry is still collecting them. Consumers still have no control over the matter, which means brands and ad tech companies still follow them around, whether they like it or not.

The new crop of tracking signals stems from the user’s device or the single consumer, such as hashed emails, and CTV device IDs. Worse, they’re making it more difficult for users to protect their identity from prying eyes. 

Identity Resolution Graphs

Identity resolution graphs are seen as an important step forward in consumer privacy protection, but whether or not they respect a user’s desire for anonymity is up for debate. These databases are built on vast identity signals: email, device ID, cookie data, CTV ID, work computer, home computer, and even physical address. An identity resolution graph connects all known signals to a single ID that typically represents individual consumers.

The benefit of ID graphs is to allow marketers and data companies to “recognize” users across multiple IDs. Let’s say a site invites users to register for a free account and the site collects the user’s email address (i.e. first-party data). Next, the site purchases an ID resolution graph to recognize users when they visit the site via a mobile device or computer from work.

Are there benefits for the user? Yes, because it allows the site to know the user and display content of interest. But wouldn’t it be better to ask the user to sign in or register on the device? Or during the initial registration process, ask permission to recognize them on other devices? This is the type of behavior that got the industry in trouble before. How hard is it to request permission?

In worst-case scenarios, the site allows advertisers or partners to target those users across their devices — without the user’s permission or input.

The Myth of Anonymity

Signals can be anonymized; emails can be hashed, device IDs can be hidden in data clean rooms, but how relevant is that anonymity if the signal can still be used to track users without their permission and for purposes they never agreed to? We forget that cookie data was also “anonymized” but the consumer still complained vociferously about being tracked.

The new private signals don’t even guarantee anonymity. Take hashed emails, which aren’t so private when everyone has the same key. That key allows anyone to recognize a hashed email as a consumer who, say, purchased this dog food or subscribes to this streaming service.

As I mentioned in the first article in this series, this level of tracking is all in pursuit of one-to-one marketing, which itself is a bit of a myth.  

Digital as Mass Media

We’re pursuing a find-and-replace option for cookies, and in doing so, we are ignoring effective and truly privacy-compliant options in front of us: one-to-many ad campaigns. Two of those options include:

Contextual targeting, which has come a long way since the days of Google AdSense. We have numerous AI solutions to help avoid brand unsafe placements, including natural language processing, sentiment analysis and computer vision that can assess the true content of an article, and place ads accordingly. This segmentation method is inherently anonymous, eschews every form of tracking, and can achieve massive scale with the right approach.

Another option is using offline demographic data, that is collected, verified and anonymized by national statistics offices, ensuring it is both accurate and privacy compliant. Going further, with modern modeling and methodology, entire countries can be segmented into neighborhoods of as few as 100 households.

Ultimately, the evolution of digital ad tracking reflects the ongoing tension between technological advancements and privacy concerns. As the ad tech industry continues to innovate, the challenge lies in balancing effective marketing strategies with the imperative to respect user privacy. By embracing more privacy-compliant options such as advanced contextual targeting and offline demographic data, the industry can pave the way for a future where digital advertising is both effective and ethical. As we navigate this new era of surveillance capitalism, the need for transparency, user consent, and robust privacy protections has never been more critical.

The post Surveillance Capitalism 2.0: The New Era of Digital Ad Tracking and Privacy appeared first on AdMonsters.

Reaching the right audience at the right moment is critical to business success. However, if the company’s budget is limited, this is not easy. Modern technologies, such as programmatic buying, may come to the rescue, allowing small and medium enterprises to advertise cost-efficiently and reach their marketing goals faster.

Why Programmatic (and What’s That, Really)?

Traditional advertising was mainly manual: it required finding a website where you’d like to place your banners and communicating with its owners. It was a time-consuming and suboptimal solution, plus often, it was expensive. Today, there’s a modern alternative called programmatic advertising. The word “programmatic” refers to how it operates: instead of people, the algorithms buy and sell ad space.

But it’s not just automation that makes programmatic so enticing – it’s also relevance. This technology can ensure your ads will be seen by the right audience in the right context. For instance, you’ve seen programmatic ads when shopping on Amazon. This company shows customized ads to visitors based on their purchasing and browsing history. 

Programmatic advertising revolutionizes the industry, making it easier for global companies and small and medium businesses to access large target groups.

Let’s look at some data to prove this point. According to Statista, in 2023, global spending on programmatic advertising reached $558 billion. By 2026, this number will likely grow to $700 billion. Also, the share of programmatic advertising in digital spending worldwide has increased since 2020. In 2020, it constituted 77.04%, in 2023 – 81.06%, and in 2029, it’s projected to reach 84.92%.

In 2021, 45% of small business respondents in the USA said they paid for digital advertising. They spent $534 monthly on average, and 93% of small companies planned to increase this amount.

So, programmatic advertising is undoubtedly the future. And it’s worth delving deeper into the subject. 

A Closer Look at Programmatic Advertising and Its Benefits 

Basically, the programmatic advertising process looks like this:

• A company creates an ad campaign, describes the audience (demographics, location, etc.), and decides on a budget.
• The ad is downloaded to the programmatic exchange via a dedicated platform. The exchange conducts auctions for ad space: who’s willing to pay more for showing their ad wins.
• A user visits a website participating in the exchange and sees the winning ad. 

Companies can use various types of programmatic ads, such as video ads, audio ads, and out-of-home (OOH) ads on billboards and displays. Today, video format is the most popular option, although OOH usage is growing in retail and other industries.

Every business, regardless of size, can experience the advantages of programmatic buying. But what are the benefits, exactly?

1. Cost-Effectiveness – Programmatic buying allows SMEs to control their budget and spend exactly as much as they are ready. Moreover, you can always be sure your ad will reach the right audience, so your money won’t go to waste. Companies often spend a significant share of their marketing budgets on traditional ads, like in magazines or on TV. Sure, many people will see this campaign, but how many belong to your target audience? With programmatic buying, every time your ad is displayed, counts.

According to Google News Initiative, direct ads cost two to four times more than programmatic ads. The numbers are $10-20 per thousand impressions for direct ads and $1-5 for programmatic ads.

For example, capturing the right audience’s attention was challenging for the Canadian clinic Whistler Medical Aesthetics. They tried traditional advertising channels like radio and magazine ads but weren’t satisfied with the cost-efficiency ratio. The company wanted to attract new customers, but the conversion rates were low. 

Eventually, with the help of consultants, they changed the strategy and focused on several types of programmatic ads. After the first month, traffic to their website grew by 60%. The clinic spent 50% less but got 25% more in return.

2. Advanced Targeting – Programmatic buying helps small and medium businesses identify and reach the right target groups, which is challenging with traditional advertising. The secret behind it is the approach’s core: focus on the audience, not the website. After all, for a company, it doesn’t really matter where the potential customer sees its ad. What matters more is what happens next. The programmatic approach suggests delegating the choice of websites to algorithms. 

So, to run an efficient campaign, a company needs to target the audience appropriately. It can choose among multiple targeting options and combine several in one campaign. The most popular criteria are demographics, interests, and online behavior. The better you describe the audience, the higher the engagement and conversion rates.

For example, a non-profit organization, The Amanda Foundation, wanted to speed up the process of adopting homeless cats and dogs. They decided to try programmatic advertising to reach people interested in hosting pets. Advanced targeting helped the organization identify the right audience and create a profile for potential pet owners. For instance, young people prefer more active dogs, while older people would likely adopt a calmer animal. The campaign’s results exceeded expectations: all the pets found new homes.

3. Real-Time Optimization – Programmatic campaigns are more flexible than traditional advertising. Moreover, the ad’s performance is measured in real-time, and the campaign can be adjusted on the go to get better results. Data on total views, conversions, impressions, etc., is recorded all the time while the campaign is running.

Here are the mechanisms enabling real-time optimization:

• Bid adjustments allow changing the frequency of ad showings depending on the device, location, time of day, etc. There’s also an option to adjust bids based on performance metrics. This helps a company maximize its return on investment in advertising.
• Ad placement optimization. Programmatic buying allows placing ads in the best possible location on the web pages and changing it if performance metrics aren’t good enough.
• Audience retargeting. Programmatic advertising aims to increase conversion rates. Hence, it can reach people who have already interacted with a company. It helps to engage non-customers and offer something new to existing clients.

4. Flexible budgeting – Programmatic advertising allows companies to set a spending limit, and they can decide whether it will be per day or for the whole campaign. Small and medium businesses often can’t afford pricey advertising, so it’s crucial to keep the budget under control. Luckily, programmatic buying helps prevent overspending.

As you can see, the benefits of programmatic advertising are convincing. Now, let’s determine how to achieve the best results using this technology.

Better Targeting for Better Outcomes

Programmatic buying isn’t a magic wand. Companies still need to do their homework to make it work, i.e., get to know their customers. With demographics, it’s not hard; you just need to analyze the data you already have. But if you aim for advanced targeting, you’ll have to discover more: what are your customers’ interests? What do they do in their spare time? What do they value the most? Consider conducting an online survey or interviewing your customers to collect this data.

The more you know about your audience, the more targeting options you can use. Among them:

• Behavioral Targeting – This option focuses on customers’ online activity, such as visited websites, cart abandonments, purchases, etc. A simple example is ads with a particular product you start seeing after visiting an online store and looking at similar products. If implemented correctly, behavioral targeting helps companies increase sales and conversion rates. For example, the children’s clothes brand Sunuva couldn’t afford a big sales team but needed a sales boost. Behavioral targeting was the answer: the company focused on cart abandonments and offered visitors relevant product recommendations. It helped increase turnover by 8.9% since the first day.

• Contextual Targeting – This option allows ads with relevant content to be placed on websites. For example, watching an interview with a famous athlete on YouTube and seeing the ad for a brand-new sneakers model results from contextual targeting. Marketers discovered that such advertising increases conversion (and irritates the audience less). Some more examples are knife ads on the website with recipes and sports equipment ads next to the article about the exercise routine.

• Geotargeting –  is a location-based option. Simply put, you reach customers in a certain geographical location and show them your ad. For instance, a restaurant may target people within a 3-block radius. Another popular tactic is to target customers in locations (country, state, city, ZIP code, etc.) that have already shown high conversions. For example, the cider maker Marners launched a campaign in four UK cities to sell tickets to its sponsored events. The company chose the locations where, according to the data, people were the most likely to buy tickets spontaneously. The campaign was a huge success: not only were all the tickets sold, but people also ended up on a waiting list.

For small and medium companies, a wise ad placement strategy requires considering relevance, time, and exposure. This is when targeting options come into play. Any business can choose one of them or combine a few to ensure the best possible result.

Managing Campaigns with Programmatic Platforms

Modern programmatic platforms offer small and medium companies practical solutions to manage their ad creatives effectively. For instance, businesses can:

• Customize Their Digital Ads – If a company has done its homework, it knows much about target groups. So, it can customize ads for different customer segments and channels. After all, the “one fits all” approach rarely works; you often need more than one creative option.

• Conduct A/B Testing – A company can test two or more different ad creative options and compare the results. For example, a clothing store can experiment with different images and offers, monitor their performance and draw conclusions. This helps make more informed decisions, reach the right audience with the right message, and, eventually, save money.An impressive example here is Lacoste. A designer brand decided to boost its summer sales in France, the UK, and Germany and turned to programmatic advertising. First, they conducted audience analysis and defined customer profiles. Then, they harnessed the power of A/B testing: they ran various versions of creatives, adjusted them, and tested new ads. The process continued until the company reached the best results. The campaign resulted in 19,749,380 impressions and 2,290 new sales.Sure, small and medium companies’ budgets are much more humble than Lacoste’s, but they still can analyze data and experiment with A/B testing.

• Use Dynamic Creative Optimization –  Since the programmatic approach includes permanent performance tracking and users’ behavior, it allows companies to adjust creatives, such as images and messages, in real time to ensure their ads are as relevant as possible for viewers. How does it work? A company must create a flexible template with elements that may change, such as calls to action (CTAs), images, etc. Then, track the metrics of each option, if necessary, eliminate inefficient versions, and add new ones.

Creatives play a vital role in engaging customers and increasing conversions. So, one of the company’s primary tasks is to ensure they deliver the message to the right audience in the best possible way. 

Customer segmentation is essential to achieving outstanding results. Dynamic creative optimization will be the most efficient if you’ve identified critical segments of potential clients. You can divide them by demographics, interests, or online behavior. Later, it will help you connect to each segment with the most relevant version of the ad. 

Last but not least, programmatic platforms measure ad performance and create sophisticated reports that companies can use to improve their decisions. The typical set of metrics includes clicks, impressions, conversions, and return on ad spend (ROAS). This data is enough to evaluate the campaign results and compare your expectations with reality.

Today, programmatic buying is changing the market rules. With its cost efficiency, budget allocation flexibility, advanced targeting, and real-time optimization features, small and medium businesses can achieve the success they could only dream about, reach a wide new audience, and learn much more about existing clients. So, why not try it as part of your marketing strategy?

The post Smarter Advertising: How Small and Medium Businesses Can Harness the Potential of Programmatic Buying appeared first on AdMonsters.

Is Google Search broken? Definitely not, and that’s the wrong question to ask.

Two weeks ago, a beleaguered product review site, HouseFresh, dropped an update about their battle, and frustration with Google Search and the state of SEO competition overall. Similarly, SEO experts like Lily Ray and Glenn Gabe have been documenting the rollercoaster of the latest algorithm updates that have walloped publishers across the board.

We are all used to competition and tough days, but it is impossible to prepare for the agony of a foundational shift. The speed of change is accelerating, so it may be helpful to climb a bit higher on the mountain and gain some perspective. Here are my takeaways:

1. Don’t hate the player, hate the game 
2. Unless the player is Google (They’re playing a different game) 
3. Play a different game

And that’s the hardest part. Everything that publishers have held holy about traffic is suddenly upside down. The traffic walls are closing in, and few exit routes are available.

Don’t Hate the Player, Hate the Game

For most of us, the rules of the traffic game are set by the search and social platform giants. We spent countless hours obsessing over the Facebook and Google Search “guidelines” to find exploitable advantages. When the preferences of these benefactors shifted whimsically, publishers were quick to adapt, pivot, or die.

Publishers learned early that building empires on borrowed traffic was risky, but compared to social media’s turbulence, Google felt like a predictable bedfellow. Conquering search was hard but meaningful. It was an artful dance of scaling quality content, or at least being of enough quality to win a coveted first position on a search results page. It was one of the few areas in digital publishing where your “library” held a residual value.

We live in a relatively free capitalistic society, and as such, anyone is allowed to enter the ring and play the game. Kudos to my publisher comrades for finding that angle, that wedge, that thing that made your scale possible. Perhaps we were jealous of those gains, but we were never mad at you for doing them.

As Brian Morrissey so eloquently put it, everything in media is a “racket.”  The pursuit of traffic is a hustle, and the monetization of that traffic is a racket. It’s survival of the fittest, but fitness is subjective to the rules of the game.

Desperate for success and strong on the scent of promised pageviews, publishers doubled down on perilous traffic games. Some flourished in influencer videos, native articles, social games, or long-tail SEO. Some ended in a bang, but many are bleeding out from a thousand paper cuts. Yet, scores of publishers find a way to carry on.

This publisher resiliency, however, comes at the price of polluting our resources. The distribution platforms (search, social, reader apps) are curators of content, responsible for delivering its customers an experience far from the garbage of the web. But we can’t have nice things, and we tilted these platforms beyond their intended capabilities. So did we break Google Search? Not really, but kinda.

The Pandemic broke Google Search. The recent SEO content explosion and resulting chaos were a perfect storm that Google wasn’t ready for. The surge in online shopping meant the world needed a gazillion more “best of” articles, and every gadget needed a review. Simultaneously, scaled SEO tools were being democratized, enabling just about anyone to find the right topics and keywords for expansion or attacking competitors. Throw in some zero-interest financing, AI-powered content generation, SEO-threadbois and a social-traffic squeeze, and you get an unmitigated overgrowth of…crap.

Google Is Playing a Different Game

The Tsunami of Crap, however, is part of a much larger weather pattern known as “the Enshittification.” By that logic, Google’s prerogative to dominate the ads business may summon its demise. The internet’s recent pivot to SEO ushered in hundreds of high-powered competitors of varying quality to the SERP battles. Content volume was metastasizing at unfathomable rates, and it was harming the utility of Google’s search results. Searchers resorted to manually adding “in Reddit” to queries or leaving Google entirely and using non-Google AI chatbots. I don’t think Google panics very often, but I’m pretty sure someone said, “I think we have a problem.”

In typical fashion, the Google Search team rolled out a steady barrage of ranking algorithm updates. They prioritized Reddit and Quora posts and other pre-filtered sources like Instagram and Twitter. Google launched Search Generative Experience(SGE) to stem the tide and flex some anti-competitive muscles. In their latest rounds of updates, Google handed out the dreaded manual action to publishers, a certain death knell for most.

Unphased, Google marches forward, as if this were all part of the plan. Amidst the chaos, the revenue teams were busy carving up the SERP page. Ads, SGE, and more ads.

Google is playing a fundamentally different game than everyone else in Search. Without a doubt, Google’s top prize is its “Google Ads” products. They are optimizing for their outcomes, which explicitly do not include sending traffic to publishers.

If you think that Google is optimizing for making revenue on your GAM account, think again. In 2023, Google pulled in 56.9% of its ad revenue ($175 BILLION) from “Google Search & Other,” and only 10.2% of revenue ($31 billion) from the Network.

They gained $12.6 billion in Search revenue YoY, and only lost $1.5 billion in the Google Network. Google footnotes that the “The overall growth [in Search & other revenue] was driven by interrelated factors including increases in search queries resulting from growth in user adoption and usage on mobile devices; growth in advertiser spending; and improvements we have made in ad formats and delivery.” (emphasis mine)

Got that last part? You’re not going crazy. Total search queries are up, traffic to publishers is down, and Search revenue is up. Google is here for the ads, their own ads.

Google Ad’s job is to sell products and take credit for it. Google will fight relentlessly to disintermediate anything that stands between a user and the product Google is promoting on behalf of a paying client. Generative AI searches will do this very well, as the service will synthesize myriad sources (including Reddit) to return a sourceless result and a well-placed carousel of ads. Raptive posits that SGE will cost creators  $2B in revenue,  and others predict traffic losses of 10%-25% and higher by 2026. 

In a recent interview on Bloomberg Originals, Google CEO Sundar Pichai discusses the shift in search and answers if Google Search is broken. Spoiler alert: he doesn’t think it’s broken but he does acknowledge that the AI boom created a glut of content and it’s Google’s responsibility to provide users with a quality experience.

Play a Different Game

It’s not about digital web publishers. Consumers are Google’s customers, and advertisers are their clients – everyone else is just expendable. HouseFresh is right about many things, especially that “Google doesn’t owe HouseFresh traffic.” Despite all that publishers, content creators and the general public have given to Google (ahem, structured data), Google doesn’t owe any of us anything.

A media company’s mission is to create and monetize their intellectual property. Under that lens, there is so much possibility, but it all starts with good IP and a plan. Endurance comes from adaptability. And that’s the hardest part.

It’s not easy to just uproot your business and find a new game to play, let alone be successful at it. Not everyone can be a direct traffic destination, and not everyone has the momentum to upstart a newsletter business. Sure, there are successful media companies that found a different game at their inception, but it’s unreasonable to expect that everyone can find a new niche.

I am truly empathetic for brands that will not survive, and I know that journalism, fair democracy, and so much more are at stake here. The internet is forever, but how it works will always change. But humans crave information and diversion, and thus media will endure.

As creators, we must always look forward and anticipate change. Make unforeseen pivots a planned part of the business plan. We must make investments based on their future return value, not based on justifying the sunk costs of our past. Let it go.

It’s time to take a long look out on the horizon and start to chart your new course. It’s out there for us, but we must work hard to find it. Godspeed to all.

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