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Advertising | Supporter Spotlight

Ad ops is too complex for generic AI—here’s what works

Digital publishers are under the gun to scale faster, monetize smarter, and do it all with fewer resources. The knee-jerk reaction? Automate everything. But here’s the kicker: not all automation is created equal. Off-the-shelf AI tools promise quick fixes. However, they often fall short when it comes to optimizing the complex Order-to-Cash (OTC) process in ad operations.

Despite widespread investments in AI, our research has found that 69% of ad operations professionals still struggle with ineffective or outdated tools​. The fact is that simply automating isolated tasks doesn’t solve the whole problem. In an increasingly competitive landscape, relying on generic solutions is no longer enough. Digital media companies must rethink how to strategically align automation with ad operations to drive real growth.

It’s time for the media industry to embrace a purpose-built, integrated approach that transforms ad operations from a cost center into a dynamic growth engine. It starts with a focus on refining the OTC process in ad operations. This way, you can identify—and rectify—inefficiencies that can lead to lost revenue and missed opportunities.

The OTC process: breaking down complexity

The OTC process—which spans pre-sales, pre-launch, post-launch, and post-campaign—often suffers from a variety of inefficiencies. Fragmented data, multiple platforms, and siloed teams lead to misalignment and delays, which can impact both client satisfaction and revenue. Our research revealed that 55% of ad operations professionals cite inefficient workflows as a major barrier to scaling operations. The need for a sophisticated approach to ad operations automation is clear. It must addresses the intricacies of each stage of the OTC process to drive efficiency, scalability and growth.

The automation gap: why generic solutions miss the mark

The buzz around AI is hard to ignore. Therefore, many publishers start their automation journey with basic AI tools. These tools can deliver quick wins, but often fall short when it comes to optimizing the entire OTC process. Our research found that 79% of ad operations teams say that their current tools don’t scale to meet today’s fast-paced demands.

This challenge is compounded when companies rely solely on internal RPA teams to handle everything. They may automate isolated ad operations tasks, but they’re not equipped to align automation across all stages of the OTC. To do that effectively and maximize ROI, publishers need to embrace a strategic, integrated approach.

Beyond AI hype: leveraging an integrated technology framework for real impact

A truly effective strategy requires an integrated technology framework that brings together multiple capabilities to optimize the entire OTC process.

  1. RPA handles repetitive, rule-based tasks freeing up valuable time for more strategic tasks.
  2. Machine Learning analyzes data patterns to optimize campaigns on the fly.
  3. Large Language Models (LLMs) extract insights and streamline content moderation.
  4. Generative AI drives hyper-personalization and predictive analytics for better targeting.

Each of these technologies play a specific role in optimizing a specific part of the OTC process. This transforms ad operations into a more efficient, scalable system. By using an integrated toolkit that is purpose-built for the OTC process, publishers can streamline workflows, reduce errors, and improve overall campaign performance.

It doesn’t stop there though. The real power of purpose-built automation lies in its ability to shift ad operations from being reactive and cost-driven to proactive and growth-focused.

From cost center to revenue driver: the impact of purpose-built automation

By integrating these technologies into a cohesive framework, companies can turn the OTC process into a data-driven system that not only reduces operational costs but also unlocks new revenue streams.

For example, companies that have adopted advanced RPA for trafficking have cut manual workloads by 30-50%, depending on the complexity of trafficking instructions. This shift not only speeds up processes but also frees up teams to focus on higher-value strategic work that drives revenue. The result is faster campaign execution, fewer errors, and a significant boost in revenue recognition.

This kind of tailored automation strategy enables digital publishers to optimize their existing resources, turning ad ops from a cost-heavy department into a powerful growth engine. But this transformation doesn’t happen overnight. It requires a thoughtful and strategic approach.

Scaling smart: a practical blueprint for automation success

The benefits of automation are clear, but successful implementation demands more than just adopting new technology. For digital media executives, it’s about aligning automation with long-term business objectives to achieve sustainable growth. Here’s how to do it effectively:

  • Audit your existing OTC process: Review your workflows to find bottlenecks and inefficiencies, especially where manual processes slow down pre-sales or post-campaign tasks. Focus on areas where automation can have the most impact.
  • Prioritize a unified automation strategy: Focus on creating a cohesive strategy that integrates automation seamlessly into your existing processes. Instead of layering on disparate tools, look for solutions designed to address the unique challenges within your ad operations. The goal is to streamline workflows, reduce manual tasks, and optimize performance holistically, rather than piecemeal.
  • Start with targeted pilot projects: Start small by automating high-impact tasks like reporting or trafficking. Test the results, refine based on feedback, and scale up gradually to ensure smooth adoption and visible ROI.
  • Align automation with long-term goals: Focus on sustainable growth, not just short-term fixes. Ensure automation aligns with your strategic objectives to enhance scalability and unlock new revenue streams.

The future of ad ops is purpose-built, strategic, and scalable. By embracing a holistic automation approach, publishers can transform their ad operations into a true growth engine. The message is clear: scale smart, automate strategically, and turn your ad operations into a competitive advantage.

Advertising | Supporter Spotlight

The rise of Attention Metrics brings new opportunities for publishers

Capturing consumer attention has always been fundamental to effective advertising. In its 2003 Media Model, the Advertising Research Foundation (ARF) emphasized “Advertising Attentiveness” as a critical step between “Advertising Exposure” and “Advertising Communication,” highlighting attention’s essential role in advertising’s communication objective. 

Recently, attention measurement has gained significant momentum. Media agencies are partnering with attention data suppliers and brands are integrating attention metrics into their media planes. Publishers are using this data to assess inventory quality and, in some cases, guarantee campaign performance. Outcome-based attention metrics in particular have gained traction given their direct connection to the bottom-line results marketers care about—and their ability to help media owners increase revenue. 

What are attention metrics?

In advertising, attention measures a person’s focus on a creative message and is driven by three key inputs: media quality, creative relevance, and audience. Attention metrics focused on media quality offer advertisers a tool to plan and optimize campaigns for better outcomes while providing publishers a way to accurately evaluate and price their inventory. 

However, attention metrics weren’t always effective in delivering meaningful results. It wasn’t until fourth-generation attention metrics emerged that they became powerful tools for media buying, trading, and optimization. 

The evolution of attention metrics

Generation one: verification metrics

    The shift from analog to digital media introduced a vast array of new channels, causing significant variance in media quality. Advertisers could no longer rely on the relative homogeneity of TV, radio, and print. Viewability and VCR became the standards for measuring online media quality. 

    However, treating all viewable impressions as equally valuable led to cluttered web pages and an influx of low-quality inventory, including Made for Advertising (MFA) sites. Premium publishers struggled to differentiate themselves, as an ad could be 100% viewable on both a cluttered web page and a high-quality environment. 

    Generation two: enhanced viewability 

      The second generation added nuance with metrics like “viewable duration,” which tracks time-in-view beyond one second and incorporates engagement data. 

      In “politely interruptive” formats like skippable YouTube ads, enhanced viewability reflected the duration of attention and provided insights into creative resonance. However, most environments don’t offer viewers control over the ad experience. Therefore, these metrics incentivized unpleasant formats like interstitials with countdown timers. 

      They also struggled in untagged channels like Walled Gardens and environments unsuited to viewability measures, such as TV, audio, and cinema, where ads can’t be scrolled out of view. 

      Despite offering more granularity, enhanced viewability still lacked a direct connection to outcomes and provided little guidance on driving real-world impact.

      Generation three: gaze duration

        Advertisers sought to predict attention beyond politely interruptive formats, leading to gaze duration metrics. Sometimes called duration-based metrics (DBAMs), they use eye-tracking and viewability data to predict how long people will look at any ad. 

        DBAMs were the first metrics consistently tied to outcomes, as demonstrated by companies like Parsec, Lumen, Amplified Intelligence, and Playground XYZ.

        However, several issues emerged when using them for buying and optimizing media:

        • Paradox of Attentive Audiences: Gaze duration is heavily influenced by audience demographics, risking biases toward older or overexposed audiences. Research by Meta showed that people over 25 spend 70% longer viewing ads than younger audiences.
        • Creative Incentives: Creative that captures the most attention may be entertaining or sensational but not necessarily aligned with a brand’s objectives. 
        • Platform Variability: Three seconds of attention on YouTube isn’t the same as three seconds on Facebook; context affects how attention translates into outcomes.
        • Diminishing Returns: The first second of attention often delivers the most value. Chasing longer durations may lead to inefficient spending without proportional returns. 

        The biggest challenge with DBAMs arises with guarantees. Due to the link between gaze duration and creative quality, third-generation metrics can’t serve as reliable media currencies. No publisher can (or should) guarantee that their audiences will pay attention to ads. 

        Generation four: outcome-driven measurement

          Fourth-generation metrics use attention as one input among others—such as placement position, page velocity, and clutter— to measure any placement’s probability of attention. Crucially, they tie this to business outcomes like ad recall, consideration, and sales, enabling advertisers to optimize for results, not just attention itself. 

          Key differences in fourth-generation metrics include: 

          • Attention as an Input: Attention is one part of a broader algorithm designed to optimize for outcomes—not the dependent variable. 
          • Placement-Level Measurement: Focusing on placement quality gives buyers and sellers a shared understanding of media value before an impression is served, facilitating more strategic trading.
          • Focus on Media Quality: By isolating media’s impact from creative and audience, fourth-generation metrics provide a consistent quality measure to guide investment decisions, avoiding the pitfalls of maximizing attention duration across all inputs. 

          Attention: How publishers use fourth-generation metrics to fuel demand 

          Publishers are increasingly adopting attention-based media quality metrics instead of viewability and gaze duration measures. This approach offers media owners a trusted way to demonstrate media quality to clients and price inventory accordingly, without being held accountable for factors like creative.

          Examples of how publishers leverage these metrics include:

          • Understanding Quality: Through comprehensive inventory audits, publishers can evaluate ad slots and placements to identify those most likely to drive outcomes.
          • Campaign Measurement and Optimization: Leveraging attention data, publishers can design campaigns designed to achieve client goals or optimize in-flight to improve performance.
          • Programmatic Monetization: Creating high-attention packages within supply-side platforms (SSPs) allows publishers to charge premium prices for top-quality inventory.
          • Attention Guarantees: Publishers can guarantee that campaigns will perform above industry attention benchmarks, thanks to insights into their inventory’s ability to deliver attention and impact.

          As advertisers leverage attention metrics to measure media quality and justify investments in more premium placements, demand for high-quality media will rise. Publishers can charge more for their premium inventory and clearly illustrate its value. In the long term, this shared understanding of quality fosters a more equitable and transparent market, leading to business outcomes for all. Outcome-based metrics represent a significant step forward, aligning media quality measurement with real-world results. 

          Innovation | Supporter Spotlight

          How AI is helping media companies attract and engage audiences

          In today’s rapidly evolving media landscape, publishers and broadcasters are increasingly looking to AI to reach new audiences and build loyalty. At Arc XP’s recent Connect London event, a panel of industry experts—Lisa Anzinger, Enterprise Lead at Echobox; Aliya Itzkowitz, Strategy Manager at FT Strategies; and Madeleine White, VP of Marketing at Poool—shared their insights on how AI is reshaping engagement strategies for media companies.

          How AI can help reach new audiences

          One of the biggest challenges for media companies is breaking through the noise to reach new audiences. AI offers a way to do this by understanding and targeting specific audience needs. As Anzinger from Echobox explained, “AI can help you counteract [algorithm changes] and actually still reach those audiences, maybe even reach them through wider channels… whether that’s social media, newsletters, or even others.” AI’s adaptability is crucial in an environment where third-party platforms frequently adjust their algorithms.

          Itzkowitz from FT Strategies highlighted the importance of multi-format engagement: “One of the most exciting things for me is there’s so many new ways that we can engage with readers, especially thanks to the generative AI boom.” For example, she shared that the Financial Times (FT) has been experimenting with a feature called “definitions,” which helps younger readers understand financial terms by offering definitions that pop up on hover. This is one way AI makes news content more accessible and appealing to younger audiences, who may find certain jargon intimidating.

          Personalization and community building

          AI’s ability to personalize experiences was another focus of the discussion. Anzinger explained that Echobox has seen significant engagement improvements through AI-driven personalization in newsletters, saying, “We have the ability to personalize each newsletter to the individual reader… [our client] Group Sud Ouest managed to increase open rates by 53% and click rates by 42%.” This tailored approach ensures that readers receive content that matters to them at just the right time.

          Beyond personalization, White from Poool highlighted the growing importance of community building, emphasizing that AI’s real value lies in connecting people. “With AI, content can be created so easily that anyone can create content… community is what’s going to set you apart.” AI can help facilitate these communities, for example, by using AI-driven chatbots to engage users in conversations and provide a space where people with shared interests can connect and interact. White mentioned that some Norwegian publishers have even used AI chatbots that act as virtual community members, creating a more dynamic and interactive environment.

          Ethical considerations and transparency in AI

          Implementing AI responsibly is a priority, and transparency is essential in building trust. White believes in the value of being open with audiences: “Always be transparent…if a reader believes they can trust you and they know when you are being honest about whether AI’s being used, I think that really helps.” On the other hand, Itzkowitz offered a nuanced perspective, suggesting that over time, labeling every AI-driven feature may become unnecessary as AI becomes more integral to everyday processes.

          However, when AI is directly engaging with audiences, clear communication is critical. Itzkowitz shared an example from a project involving synthetic voices: “If you’re using the voice of a journalist or cloning a voice…then you need to disclaim that.” AI’s role in generating content is likely to be acceptable for readers as long as they feel informed about when it’s being used.

          Legacy media vs. new brands: Who has the AI advantage?

          An interesting debate arose around whether legacy media brands have an advantage in AI-driven engagement due to their extensive archives and resources. While Itzkowitz acknowledged that larger companies have more data and resources, she pointed out that smaller publishers can often innovate faster due to less bureaucratic red tape. “Smaller publishers…may just say, let’s try full automation. Let’s see how it goes,” said Anzinger, highlighting the experimental edge smaller companies can bring to the table.

          For smaller teams, adopting hybrid models that combine ready-made AI solutions with limited internal development is a viable approach. As Itzkowitz noted, “AI has somewhat changed this…you no longer need huge development teams to build something.”

          Overcoming fear of failure with AI

          The panelists acknowledged that trying new technologies like AI can be intimidating, especially with the risk of projects not meeting expectations. However, Anzinger advised companies to take a “trial and error” approach, iterating based on results. “Just keep trying… you can’t be afraid of something going wrong, just need to keep trying,” she said.

          White underscored the importance of resilience and learning from failure, recalling the New York Times’ bold decision to implement a paywall in 2011 despite criticism. “Trying and failing means you are potentially going to succeed and be ahead of anyone else,” she said, stressing that bold moves can often be the most rewarding in the long term.

          What’s next for AI in audience engagement?

          Looking to the future, the panelists shared their excitement about the new ways AI can enhance engagement. Anzinger is optimistic about using AI to produce dynamic content, such as automated videos from articles, which will help media companies engage younger audiences on platforms like TikTok and Instagram. Meanwhile, Itzkowitz is eager to see AI-driven creativity blossom: “What’s that killer app for generative AI, or how are we really going to change the news experience so that young people and audiences in general really want to engage with news again?”

          White emphasized the potential to create individualized experiences: “Instead of creating this one-size-fits-all model… [AI means] giving them a unique experience that the reader next to them isn’t going to get.” This hyper-personalized approach has the potential to make each reader feel valued and understood, a key to building loyalty.

          Finally,the panelists concurred that AI is not here to replace journalists but to augment their capacity to connect with readers in increasingly meaningful ways. As AI tools evolve, media companies that embrace experimentation, prioritize transparency, and stay committed to creating genuine connections will be best positioned to thrive.

          Advertising | Supporter Spotlight

          How publishers can avoid the high cost of inaccurate data

          Every year, poor data quality costs organizations an average $12.9 million, according to Gartner. These companies are actively looking for ways to eliminate that waste and the market has responded. Gartner also reports that by 2025, 90% of data quality technology buying decisions will focus on ease of use, automation, operational efficiency, and interoperability as the critical decision factors to mitigating the data quality problem.

          Data accuracy is the lifeblood for digital publishers

          Losing revenue due to inaccurate data is particularly painful for digital publishers and media companies whose business models largely depend on their ability to leverage high-quality data to deliver for advertiser outcomes. Fresh, accurate data is the foundation for effective segmentation, targeting, and business intelligence. It’s essential for optimizing content, improving user experiences, and ultimately maximizing revenue.  

          Inaccurate, poor-quality, older data can lead to missed opportunities and loss of credibility among ad partners. These costly mistakes show up in multiple ways, including:

          • Misaligned content strategies: Targeting the wrong audience can result in wasted resources and low engagement.
          • Ineffective advertising campaigns: Poor data can lead to targeting errors, resulting in lower click-through rates and conversions.
          • Poor user experience: Inaccurate data can lead to personalized recommendations that are irrelevant or worse, driving users away from your content.

          Data accuracy is directly tied to revenue

          Inaccurate data can have a direct and significant impact on a media company’s bottom line. Decreased ad revenue from targeting errors can result in lower ad impressions and clicks. A poor user experience due to inaccurate data can lead to subscriber churn. Without an ability to harness fresh data, publishers may not be able to quickly capitalize on the news cycle by jumping on trends and creating new opportunities to serve advertisers. Publishers will also have challenges measuring the ROI of their campaigns and reporting results to advertisers. 

          Know the signs of data accuracy issues

          Even publishers who think their data is accurate, know that data quality can deteriorate over time. This is due to various factors, including human error, system failures, and changes in data sources. It’s well worth the time and effort to conduct periodic checks to make sure your data pipeline is running smoothly and your data is as accurate as possible. 

          Here are five signs to watch for:

          1. Inconsistent or conflicting data

          One of the most common signs of data accuracy problems is inconsistencies or conflicts between different data sources. For example, you may find discrepancies between data from your first-party systems, analytics tools, and ad platforms. These inconsistencies can make it difficult to get a clear and accurate picture of your audience, campaigns, and performance.

          2. Missing or incomplete data

          Another red flag is missing or incomplete data. This can occur due to data collection errors, system failures, or changes in data ingestion methods. 

          3. Outdated data

          Data can become outdated over time, particularly in rapidly changing industries like media. Using outdated data can lead to inaccurate insights, ineffective targeting, and wasted resources.

          4. Data quality issues

          These issues can arise due to errors such as gaps, inconsistencies, problems with validity, latency, or a lack of data normalization across systems.  

          5. Lack of data governance & reliability

          Without proper policies and procedures in place to manage data, it can become fragmented, inconsistent, and unreliable. In addition, media companies may be working with dozens of first-party and third-party systems that organize data differently. A single source of truth is essential to truly optimize campaigns. 

          Limited engineering resources exacerbate data quality problems

          Despite the critical importance of data accuracy, some media companies simply don’t have the engineering resources to ensure their data is consistently accurate. With millions of pieces of data constantly arriving from dozens of sources, managing that data at scale all day, every day becomes a monumental, ongoing challenge. 

          Traditional data engineering approaches often fall short in meeting the specific needs of publishers. These challenges typically fall into some combination of these categories: 

          • Data Silos: Data is often pulled directly from content platforms and dumped into data warehouses without proper structuring or enrichment, making it difficult to use immediately.
          • Manual Reporting: Some publishers still rely on manual methods like exporting data to Google Sheets for reporting, which is time-consuming and error-prone.
          • First-Party Data Integration: Integrating all first-party data securely and efficiently into the data pipeline is painful. 
          • Prioritization: Revenue teams often struggle to get their data engineering needs prioritized. In many cases, data engineers simply don’t want to perform the mundane tasks associated with maintaining and updating APIs. Revenue teams  are then forced to create DIY workarounds that are time consuming, frustrating and not as effective as they hoped. 

          Selecting a data operations platform

          The current demand for skilled data engineers far exceeds supply, making it difficult to allocate sufficient time and expertise to data quality initiatives. Media companies that leverage no-code or low-code data engineering tools that can automate data pipelines and workflows, can drastically reduce the need for extensive engineering expertise. 

          Here are some qualities to look for and questions to ask when selecting a data operations platform to overcome your data quality issues:

          • Automation and Speed: Look for platforms that offer automated data pipelines and ETL processes that can streamline data management and reduce manual effort.
          • Scalability: Make sure the platform can handle large volumes of data and scale as your needs grow.
          • High-Quality Data: Find out how the platform ensures reliable and trustworthy data. Do they consolidate data from various sources, (e.g. first-party data, agency data, and data from different channels).
          • Domain Expertise: Is the platform backed up by a team of experts in digital publishing who can ensure that data is handled and transformed correctly for optimal analysis?
          • Customization: Can the platform be customized to meet your specific requirements and business logic?
          • Comprehensive Measurement: Are your essential metrics built in for effective campaign evaluation?
          • Privacy and Security: What kinds of security measures have been taken to protect your sensitive data and ensure adherence to data privacy rules and regulations? 

          Publishers and media companies simply cannot ignore the impact that inaccurate data has on their bottom line. By investing in data operations platforms, quality control processes, and governance, you can unlock the full potential of your data and drive sustainable growth for your business.

          About the author

          Manny Balbin, a seasoned veteran with over 15 years in digital media and advertising, currently shapes vision and strategy for BI products at Switchboard Software. Switchboard’s data engineering automation platform aggregates disparate data at scale in real-time for better business decisions. Prior to Switchboard, Manny led Product, Ad Technology, and Revenue Operations at Freestar, PMC, and Quantcast.

          Perspectives | Supporter Spotlight

          Reimagining the place of news in social media

          As with many of you, the 2024 Reuters’ Digital News Report continues to give us much to reflect on even months after its release. While there are many learnings from the report, the one chart that stood out to us was the “proportion that used each network for news.”

          While the major drop for Facebook was anticipated (given Meta’s strategy to deprioritize news), we found ourselves asking: has the news hit its high-water mark on social media, and if so, how do we adapt?

          Since 2018, algorithm changes on social media have prioritized user content over public content, including news. This shift has reduced the benefits for news organizations on these platforms. We asked the report’s author Nic Newman for his reflections. He explained that the decline in the “post and refer” model is due to platforms favoring formats like video that keep users engaged within the platform. In this blog, we’ll explore how publishers can navigate and maximize their presence on social media in this changing landscape.

          Leveraging video

          Posting videos on social media has grown to be a strong means of developing brand awareness and engagement with audiences. However, production costs (financially and operationally) have historically been much higher than text-based content. Monetizing video on social media also proves to be a challenge with video requirements being strict and unrewarding unless scale is achieved:

          Source:YouTube Partner Program, YouTube CPM,  TikTok Creator Program, Facebook Partner Monetisation Policy

          Aside from these requirements, social media platforms require videos to fall within their community guidelines and be “ad-friendly”. TikTok has a much more controversial requirement that videos must not contain sensitive or controversial topics (which are rather loosely defined). Videos that have been deemed controversial are either faced with being demonetized or being placed with limited visibility on user feeds. In other words, unless a user is actively looking for news publisher content, it will only rarely appear on their feed. Nieman Lab conducted a study which found that TikTok’s algorithm made it unlikely and difficult for news organizations to reach users.

          Engaging your audience

          While social media no longer drives the same traffic as it did, it still serves as a powerful avenue to connect and engage with your core audiences away from your website which has become increasingly important as platforms promote posts which users engage with more. While a more tailored engagement varies per platform and your audience.

          Videos are currently the strongest way to engage an audience on social. While they are difficult to monetize, they are effective in capturing the audience’s attention and driving curiosity to read more. The challenge is capturing your audience within the initial five seconds before they scroll along in the ether of social media. You can do this by showing preview clips of the video or providing a quick summary at the beginning to engage your audience right away.

          Another effective practice is featuring your journalists. This provides a face for your audience to interact with and helps them develop a deeper relationship with your publication. Putting them on explainer videos showcasing their expertise, retweeting their tweets to provide them a platform or having them directly communicate with interested audiences through Whatsapp or Instagram broadcast channels can help develop deeper levels of engagement.

          Lastly, developing interactive content such as polls or gamification features is a strong opportunity to get your audience to interact with your social media page and open themselves to conversations and discussions.

          These practices may change as time passes and algorithms evolve. But traditional news publishers have lots to learn from social media first publications who have already navigated social media through experience such as Brut (French), Will Media (Italian), and dw_berlinfresh (Germany).

          Each platform plays a different role

          The social media landscape continues to evolve and constantly presents itself with new challenges. However, we cannot deny the importance of the platform in reaching audiences who may not otherwise engage with the news. With Facebook still boasting over 3 billion active users and Instagram, YouTube and TikTok around 1.5-2 billion active users, social media is still an invaluable tool in connecting and engaging with your core audience while reaching new audiences as well.

          However, given recent changes, now is the time to reimagine the role of each platform in the context of news:

          • Facebook – given its large reach – is still helpful in slowly converting new audiences into regular readers through peer-to-peer exposure of your content by focusing on virality, as Facebook itself has stated.
          • YouTube, on the other hand, offers a strong opportunity for monetization. Publishing long-form video content on the platform can help diversify revenue streams for your publication with the right audience. 
          • Instagram and X may still hold opportunities for traffic even though a much stricter link placement policy has taken hold (though lenient in comparison to Facebook’s policy). However, it is important to recognize that both platforms serve distinct needs:
          • TikTok remains an unknown quantity. However it is a platform worth exploring, especially for those trying to reach younger audiences. 55% of TikTok’s user base in the United States are those aged 18-34 and the global average watch time of short-form video is 95 minutes a day. 
          • WhatsApp has seen growth as a popular platform for news distribution particularly in countries where mobile usage is high and access to traditional media is low such as Brazil, India, and South Africa. WhatsApp can potentially be used to directly connect and engage with audiences given its nature as a message platform, allowing journalists to develop a more personalized connection with your audience.

          These are only a few strategies you can employ in each platform, but understanding their strengths and leveraging them towards your publication may help bring added value.

          Advertising | Supporter Spotlight

          How publishers can drive direct-sold advertising success

          Direct-sold advertising is a win-win for publishers and advertisers. Publishers get more control of how to package and price their inventory without as many intermediaries taking a cut. And advertisers know exactly where their brand will be featured and not be as creatively hemmed in by standard programmatic ad units. Media sales teams that proactively drive the ad sales process with their clients stand to more effectively generate revenue, versus a reactive approach. 

          Here are three strategies that publisher ad sales teams can use to make their teams and processes more effective in order to drive direct-sold revenue. 

          Curate ad formats and features that match campaign goals 

          When brands or agencies issue a request-for-proposal (RFP), they are in essence asking publishers to propose the advertising solutions that will best align with their marketing goals. But too often, brands ask publishers to supply responses via a templated, “fill-in-the-blank” spreadsheet listing ad units, pricing, and impression volumes. 

          Smart publisher sales teams dig in and ask questions about an advertiser’s campaign goals, creative direction, budget, and success metrics to help shape their response. Direct sold ads allow for far more campaign customization, and RFP responses should reflect that. 

          Publishers must take the time to consider the advertiser’s stated goals. Then, they need to select not just ad formats but specific creative features within the ad that will deliver engagement and results. This way, they can deliver a unique, tailored campaign plan that goes well beyond programmatic. 

          Here are some ways publishers can optimize their offerings:

          • If the campaign is focused on a date (movie or show premiere, product availability, registration deadline), include a live countdown in the ad creative.
          • If the campaign can be localized, show how you can include features in the ad creative like embedded maps, language detection, or dynamic text.
          • If you as a publisher can bring a unique data angle to the campaign, show how your data and the advertiser’s can be combined to develop more personalized, more impactful creative. Show dynamic elements within a creative execution that can change at run time based on data like geography, demographics and more.

          Of course, this all assumes that a publisher has a suite of ad products with robust features and the ability to quickly rotate in new options without a lot of technical upheaval. That’s one reason having the right creative platform partners is crucial. 

          Rethink your approach to RFP responses

          It’s true that many RFP responses require a set format when submitted (and unfortunately that format is too often an Excel file). However, publishers should still take the opportunity to propose ideas that could be a bit out-of-the-box. 

          An accompanying presentation (and not just a completed spreadsheet of line items) can bring a proposal response to life. It should describe the basics like campaign flight dates, impressions, and costs But most importantly, it should showcase what you’re selling.

          Publishers who include live ad mocks of creative executions in their proposals make it easy for advertisers to say, “Yes, I want that.” Even better: use the advertiser’s existing assets in mocks and run them on test pages, so they can truly see the power of their brand in your execution and see real examples of how their brand would look on your site(s). 

          A presentation is also a means to opening up a further conversation with a brand or agency. That’s where creativity can really start flowing. Maybe a proposed mock won’t be exactly what they are after, but it can spark an idea that leads to something greater. 

          Sometimes ad sales teams can find themselves out of their depth technically and unsure whether an idea for an ad unit is truly feasible. Publisher sales teams should always be sure that they’re getting the help they need from their partners to help them manage these questions so that they can close business and be successful.

          Sell synergistically across your revenue sources

          Sales teams that think holistically across revenue streams can be successful by packaging multiple opportunities into one proposal and creating synergies between different products. When pitching an advertiser on branded content partnerships, like a podcast or video, include display ads in the overall proposal that excerpt the content to play directly in the ad unit and drive users to the campaign landing page. Create additional value for event sponsorships by including ad units that livestream keynotes or sessions from the event directly within the ad. 

          The key thing to keep in mind is that brands and agencies are seeking out your publication because of its premium status. If you lack premium inventory, premium ad products, and premium service, they will believe that they are better off rolling the dice on impressions on the open programmatic marketplace. Showing advertisers that you have multiple avenues for their campaign to reach your highly coveted audience helps reinforce the value you offer.

          Direct-sold advertising is challenging, but worth it

          Direct sales certainly isn’t as turnkey as placing all your ad inventory up for bid programmatically, but the gains are worth it. Higher CPMs and more control of inventory and advertiser relationships are better for publishers over the long haul. And while ad sales teams may have been breathing a sigh of relief earlier this year after yet another delay in the deprecation of third party cookies, ad operations teams are still planning for the continued decline of options to target audiences in ways that aren’t owned by the publisher or advertiser directly. 

          A strong direct sold advertising sales team works collaboratively with its internal colleagues, external partners, and its advertisers to develop creative solutions that go beyond the most basic requirements of an RFP. The right mix of inventory, audience, data, and creative formats – packaged and presented elegantly and in concert with other products – will help publishers maintain premium, preferred status among advertisers.

          Advertising | Industry-Research | Supporter Spotlight

          Audiences value news. Here’s why advertisers should too

          For publishers and media outlets, the stakes have never been higher. You carry the torch, delivering trusted journalism, which safeguards democracy. But there’s a challenge we all face: How do we ensure that advertisers and agencies recognize the critical role your media platforms play, not just for society but for their brands?

          The truth is, news outlets are more than just another space to place ads. They offer something everyone is looking for in today’s fragmented digital landscape: trust, attention, and real brand outcomes. And it’s up to us, as media executives, to make sure the advertising community understands that value.

          A new study from Teads and Lumen Research underscores what many of us already know—advertising alongside quality journalism isn’t just the right thing to do, it’s also the smart thing to do. Yes, supporting the health of the media is important. But the news and information created by trusted media brands provides an excellent environment for advertisers.  

          Why news platforms outperform social media

          When advertisers invest in news platforms, they’re getting more than just impressions. They’re tapping into the power of focused attention and outcomes. Our study found that ads placed within trusted news environments drive a 77% increase in brand recall. That’s compared to other channels where distractions are plenty and attention is fragmented.

          Consumers who come to news platforms are there to engage deeply. And that translates directly to the brands that advertise alongside this content. The study showed that news platforms have a higher attention coefficient. Publisher inventory running through Teads’ premium supply, for example, outperformed social media with a focused attention rate of 16.9%, compared to the social average of 15.8%.

          This means that ads placed within trusted journalism aren’t just seen—they’re remembered. And in a time when attention is the most valuable currency, that’s a huge differentiator for brands.

          Building trust and driving brand safety

          Here’s the thing: brands seek to build trust and credibility. And where better to find that than in the trusted, carefully curated content that news outlets provide? Consumers have shown they trust brands that advertise alongside credible journalism. In fact, the study found that 94% of U.S. consumers have a neutral or positive perception of brands that advertise in quality news content.

          For advertisers, that’s reassurance. It means their brand isn’t just safe—it’s enhanced by association with journalism that audiences trust. And the fear that placing ads next to serious news will hurt their brand? The study debunked that myth too. 69% of consumers report that advertising alongside serious or even “disconcerting” news doesn’t negatively impact their perception of the brand.

          This is critical as we talk to advertisers. News outlets offer an unparalleled level of trust and brand safety, providing the very context that advertisers need to not only protect their reputation but to strengthen it.

          Helping advertisers understand the value

          Let’s be clear: this isn’t about asking advertisers to just do the right thing by supporting journalism. This is about showing the real, measurable value that news platforms provide. Quality journalism delivers. It offers attention, trust, and results that many other channels simply can’t.

          But we’re also facing a growing challenge—news outlets are under immense pressure. Since 2005, more than 43,000 journalist jobs have been lost, and the U.S. is seeing an alarming rise in “news deserts” as more than 1,700 communities are left without local news sources. These closures don’t just hurt journalism, they hurt society. And as misinformation spreads and trust in media declines, it becomes harder for quality journalism to survive without the support of advertisers.

          We, as media executives, are in a unique position to bridge that gap. By helping advertisers understand the tangible benefits of supporting news outlets we can drive the investment that quality journalism needs to thrive. And the value for these advertisers is clear: higher brand recall, stronger consumer trust, and a brand-safe environment.

          A call to action for media leaders

          This is about more than just placing ads. It’s about the future of public discourse. Quality journalism is a pillar of democracy, and without it, we risk losing an informed and engaged society. But the future of journalism is at risk if advertisers continue to look elsewhere.

          Our role is to ensure they see the value in supporting trusted journalism. The Teads and Lumen Research study proves that investing in news platforms isn’t just good for society—it’s good for business. But it’s up to us to ensure that message gets through.

          Now more than ever, media executives have the power to lead the charge. It’s our responsibility to communicate to advertisers and agencies that their dollars aren’t just buying ad space—they’re investing in the future of a free press, trusted information, and an informed democracy.

          We must continue to show the value of these partnerships, both for the brands that advertise and the journalism that drives societal progress. After all, it’s not just about what’s good for business. It’s about what’s good for the world. 

          Study Methodology: commissioned by Teads, Lumen Research conducted an online survey with 900 respondents from the U.S., age 18+. The study measured against prompted brand recall, brand choice and brand perceptions to further explore the impact of ad exposure on brand outcomes. One group saw traditional news and one group read soft news content to understand how the news content affected attention to the ads. Lumen’s patented eye-tracking platform was enabled as respondents scrolled from the front-facing smartphone camera to measure the visual attention data of how each consumer read the news and paid attention to the ads. A control group of 250 people matched on demographics took part in the survey component of the study. Fieldwork was undertaken between May 2 and May 24, 2024.
          Innovation | Supporter Spotlight

          Balancing efficiency and authenticity in journalistic use of AI

          Two small letters – AI – are proving major disruptors for the digital news industry. Already, 70% of journalists and newsroom leaders use generative AI in some capacity. While we hear much excitement over its potential, opinion remains divided. Senior editorial representatives at titles such as UK newspaper The Sun have expressed concern that AI in journalism will deliver a “tsunami of beige” content. And readers say they prefer AI-free news.

          As such, there is a balancing act to be performed for AI and human input in newsrooms, pitting its potential to revolutionize the industry against the risks of over-reliance and ethical concerns to find a middle ground that benefits all. Against a backdrop of budget and team cuts, however, it’s crucial to realize that AI can enhance workflow efficiency, allow journalists to do more with less. That, in turn, can allow teams to focus on what matters the most: creating human-centered, authentic content that engages the reader.

          Using AI to bridge the gap with users

          Primary motivations among journalists for using AI technologies include automating mundane tasks. But as Ranty Islam, Professor of Digital Journalism, pointed out at the 2024 DW Global Media Forum, this isn’t the be-all and end-all of AI. The key lies in integrating AI into a holistic strategy that brings journalism closer to readers. Using AI to perform necessary but time-intensive tertiary tasks means that journalists—notably those in local newsrooms with smaller budgets and teams—can get more actual journalism done. They can get out there to connect with real people and stories.

          Moreover, as audience needs change, AI can help newsrooms track and enhance the stories and formats that perform for them in an audience-first strategy. Using AI alongside tracking means newsrooms can harness suggestions about when to write stories and the kinds of topics and formats that audiences want to see. Content suggestions can be made based on AI tagging systems,while algorithms that suggest stories based on user behavior or interests can help tailor content for different audiences. This enhances the reader experience for greater engagement and retention while also helping boost subscription offerings through data-driven personalization.

          Doing more with less

          There is a wealth of opportunities for newsrooms to use AI to help with everyday tasks, and the benefits for understaffed newsrooms are clear. The local news sector has particularly felt the impact, losing nearly two-thirds of its journalists since 2005. AI can serve as a helping hand for tasks that would otherwise require multiple staff members. We have integrated AI into our liveblogging software, for example, to enable users to generate liveblog content summaries in seconds, assimilate live sports results, adjust tone and language, create social media posts, and generate image captions for enhanced SEO.

          AI’s potential to localize content and engage new audiences is widely recognized. FT Strategies has highlighted AI translation as “truly disruptive in the context of news”, particularly for multilingual communities or multinational publishers seeking to replicate regional content across multiple sites.

          Indeed, AI excels at summarizing and extracting information, making it extremely useful for summary generation. While most reporters aren’t keen on full AI copy generation, enabling teams to recycle their content quickly and easily or suggest headlines based on keywords can be a huge help. Moreover, since the training data is their own, the summaries reflect the author’s original, authentic style.

          This summarization can be carried through to data analysis to create charts and infographics. AI can even create text descriptions for supporting media to help with search engine optimization or social media posts and matching hashtags to promote stories. Journalists aren’t typically SEO or social media managers, but small teams sometimes need to wear multiple hats. Using AI as a virtual assistant allows reporters to focus more energy on their reporting.

          AI can also be harnessed to support a journalist’s development or to augment collaboration or brainstorming that might once have been done in a newsroom among a large team. AI can be used to create identify story gaps or flaws, a tool to suggest improvements, or to proofread, make composure suggestions, and adapt tonality to the situation at hand. This is particularly useful when wanting to address various user needs or even different age groups.

          Liveblogs offer a prime example of how AI can be harnessed to enhance reporting, helping manage and update live content in real-time, automatically pulling in relevant information, images, and social media posts. This allows journalists to focus on providing valuable insights and context, delivering a dynamic and engaging experience for readers.

          Trust and transparency while using AI in journalism

          Using AI behind the scenes in this multitude of ways chimes with reader comfort levels. The Reuters Institute for the Study of Journalism revealed that while readers are skeptical of AI-generated content, they are generally happier with AI handling behind-the-scenes tasks under human supervision—as long as newsrooms are transparent about AI usage.

          While AI can help to detect disinformation, fact-check, or moderate online comments, adding to the integrity of journalistic content, its tendency to hallucinate and invent also means that human oversight is vital. We must train journalists to work alongside AI, using it to enhance, not supersede, their skills, striking a balance between AI utility and the preservation of the human element in news reporting. 

          AI is a tool, not a substitute. It can automate mundane tasks, save time and assist research and brainstorming processes, but its power lies in complementing human effort, not replacing or overshadowing it.

          The future of journalism lies in a hybrid approach in which AI supports, not replaces, the essential human touch that defines quality journalism. Whatever the medium—print, online, liveblog—by fostering collaboration between technology and editorial expertise, newsrooms can navigate this evolving landscape, ensuring that AI enhances, rather than diminishes, the integrity and creativity of journalistic work.

          Advertising | Supporter Spotlight

          Why adtech loves fragmentation–and why publishers shouldn’t 

          The advertising landscape is fracturing due to third-party signal loss, causing targeting scarcity for publishers and advertisers. Half of users have “disappeared” from digital advertising by using browsers and devices that limit tracking and preserve privacy. 

          While Chrome supports cookies, 40% of users modify privacy settings or browse incognito to block ad tracking. We’ve seen this reflected in our publishers’ audiences, where 70% of the internet is now invisible to adtech, leaving only 30% addressability. Chrome’s ‘reject all’ buttons in Europe and the proposed American Privacy Rights Act of 2024, or APRA, will further reduce data visibility and increase the signal loss we see today.

          As businesses seek ways to address signal loss, solutions flood the market that are short-term workarounds that lack interoperability. The result is a fragmented, chaotic adtech ecosystem.  

          Publishers have not experienced signal loss because they have first-party signals. This means publishers can append signals to 100% of impressions, whether an ID for users that authenticate or a cohort based on known attributes and behaviors collected on the user. The challenge for publishers is to build signals that the buy side will want to buy. This requires navigating fragmentation and having the tools to connect data and collaborate with the ecosystem on their signals.

          Connecting disparate data

          According to a recent AdMonster’s Publisher Pulse survey, monetizing audience data is seen as a significant opportunity for growth for 50% of publishers, and 33% plan to leverage audience data to create new revenue streams.

          Consolidating their data asset is critical for publishers as it offers an opportunity to leverage  a lot more dimensions to build the audiences that advertisers want to buy. Publishers can augment the first-party signals they collect across their digital properties with additional attributes such as demographic, preference, and intent data. However, today’s fragmented data ecosystem makes it challenging to harness the full potential of this information. Data often sits in silos across different environments, different teams and consolidation requires data engineers, complex workflows, and significant time investment. 

          Greater connectivity creates seamless, integrated systems that efficiently combine all data assets. This empowers publishers to activate their data without the burden of engineering-heavy processes.

          Greater connectivity not only simplifies data management but also enhances its effectiveness. By consolidating various data sources, publishers can build a richer, more comprehensive profile of their audiences, allowing for better targeting and more personalized advertising experiences. This, in turn, creates new revenue opportunities and strengthens relationships with advertisers.

          Collaboration reimagined

          The challenges don’t end with data consolidation. Retargeting, once a cornerstone of performance marketing, is losing its effectiveness as signal loss impacts the scalability of traditional tactics. Advertisers are now struggling to achieve scale and performance, with all buyers competing for the same shrinking 30% of the internet’s inventory. This lack of scale and poor performance leaves many legacy retargeting strategies falling flat.

          Data collaboration holds the potential to solve these challenges, but the current state of the Data Clean Room (DCR) space presents two significant hurdles:

          1. Fragmentation and operational complexity

          The DCR space is highly fragmented, making data collaboration cumbersome and resource-intensive. Moving data between systems requires complex operations, lengthy data processing agreements (DPAs), and heavy legal and operational overhead. This complexity not only delays campaign execution but also reduces return on ad spend (ROAS). Advertisers and publishers are left with inefficient processes that yield minimal performance gains.

          2. The wrong signal to activate against

          For companies that manage to navigate these hurdles, the results often don’t justify the effort if the signal that is activated is a matched record. Match rates between data sets are typically low. However a seed of matched records is a great asset to help a publisher identify which of their signals might have the highest propensity to hit advertiser KPIs or the highest affinity towards the brand.

          The industry must move towards streamlined connectivity and more efficient data collaboration to overcome these challenges. By creating unified environments where data can flow seamlessly between publishers, advertisers, and platforms, we can reduce the operational burden. This would allow for more effective use of first-party data, greater scale in retargeting efforts, and better performance across the board.

          Embrace collaboration & connectivity 

          This reimagined approach to data collaboration and connectivity allows publishers to unlock the full potential of their data, creating better consumer experiences, more opportunities for growth, and a stronger, more transparent ecosystem where data works for everyone.

          Advertising | Supporter Spotlight

          Publishers take note: AI is bringing contextual to new heights

          The journey toward third-party cookie deprecation has been long and challenging, punctuated by Google’s recent shift to a consumer-led cookie opt-out model. Still, publishers need to keep moving. Actions by browsers, regulators, and consumers all bring them to the same inevitable outcome: cookies will end.

          For publishers, the loss of cookie data is a problem. Cookies remain a cornerstone of audience insight and targeting. In fact, in a survey we ran a few years ago, half of publishers polled expected layoffs due to the revenue loss from cookies going away. We see that playing out now, as digital media is more challenged than ever.

          Some argue that publishers are rich in first-party and logged-in data to make up the difference. However, for the vast majority, first-party data is scarce as authenticated traffic across the open web has topped out at around 20%. 

          In response, alternative identifiers have emerged, allowing for continued behavioral targeting and addressability. However, we can’t, once again, put all of our eggs in one basket. So, publishers must diversify their adtech toolkits. This involves leaning on behavioral targeting, in whatever form it exists, and integrating other approaches.

          Enter contextual technology. The journey toward third-party cookie deprecation has been long and challenging, punctuated by Google’s recent shift to a consumer-led cookie opt-out model. Still, publishers need to keep moving. Actions by browsers, regulators, and consumers all bring them to the same inevitable outcome: cookies will end.

          For publishers, the loss of cookie data is a problem. Cookies remain a cornerstone of audience insight and targeting. In fact, in a survey we ran a few years ago, half of publishers polled expected layoffs due to the revenue loss from cookies going away. We see that playing out now, as digital media is more challenged than ever.

          Some argue that publishers are rich in first-party and logged-in data to make up the difference. However, for the vast majority, first-party data is scarce as authenticated traffic across the open web has topped out at around 20%. 

          In response, alternative identifiers have emerged, allowing for continued behavioral targeting and addressability. However, we can’t, once again, put all of our eggs in one basket. So, publishers must diversify their adtech toolkits. This involves leaning on behavioral targeting, in whatever form it exists, and integrating other approaches.

          Enter contextual technology. Recent advances in artificial intelligence and machine learning have dramatically enhanced contextual targeting, making it more capable – and timely. As a result, every publisher needs to take advantage of the technology.

          The rise of contextual 2.0

          Historically, contextual targeting involved analyzing a publisher’s content and aligning it with a broad set of content categories. This was tied to the technology’s limitations and made early contextual fairly imprecise – like using a meat cleaver instead of a scalpel. This would result in mismatched ads and content. However, as AI has evolved, so has contextual targeting.

          Today’s technology can analyze millions of articles spanning years. Natural language processing (NLP) now works across many languages, greatly expanding contextual targeting’s reach. Computer vision can even evaluate images and videos in real-time. The vast amount of content available for AI analysis has fundamentally transformed what contextual can do, allowing the targeting to go well-beyond simple keyword matching or broad categories. It can be based on more nuanced categorization aligned with IAB taxonomies.

          Additionally, AI provides deeper insights into content consumption patterns and engagement. It can uncover advertiser interests in content that might not be immediately obvious to humans and predict future content patterns and consumption trends. These capabilities enable publishers to better monetize their inventory and audiences, identify and sell high-performing or in-demand inventory at a higher CPM, and expand interest with non-endemic advertisers.

          Pairing contextual with behavioral targeting

          With all of that in mind, contextual and behavioral targeting should not be considered rivals, though it’s easy to think that way. Ultimately, they’re complementary tools. When used together, they can address signal loss that threatens publisher addressability and monetization.

          Publishers can create more effective audience segments—whether through available identifiers or AI modeling—by combining it with contextual data about their inventory and content. This integration creates a two-way benefit: audience segments are matched with relevant content, while content is tailored to specific audiences through contextual targeting. This approach allows publishers to better align their content with interested audiences, improving targeting efficiency and ad relevance.

          For example, a luxury skincare brand promoting a new anti-aging serum might use contextual targeting to place ads on beauty and wellness websites. However, this approach alone could attract too broad an audience, even with more nuanced categorization. Behavioral targeting can refine this focus by looking into the characteristics of those with a history of buying premium skincare products or visiting luxury brand sites. Further, behavioral targeting can reveal new clues about potential customers to inform incremental reach via contextual targeting. This combined approach, blending the two types of targeting, ensures ads reach those most likely to be interested in and able to afford the product, as well as new prospects never before considered. This, of course, benefits both the publisher and the advertiser.

          The surge in programmatic curation is also expanding to contextual and behavioral targeting, thanks to new technologies. New AI-driven machine learning models analyze historical online content consumption to identify audience trends. Then, a recommendation engine identifies high-indexing affinity groups for predictive contextual targeting. These affinity groups are then mapped to specific content categories, curated across premium inventory, and packaged into Private Marketplace (PMP) deals. This takes the synergy between contextual and behavioral to a whole new level. Plus, it’s SSP and publisher-led versus DSP and demand-led, a welcome shift for publishers..

          AI and a brighter contextual future

          To fully leverage these opportunities in contextual and contextual + behavioral targeting, publishers must invest in robust data infrastructure and collaborative capabilities. It’s crucial to identify gaps with partners and enhance existing systems. This proactive approach will help publishers adapt to the ever-evolving digital landscape, maintaining relevance and gaining a competitive edge in an ecosystem that often seems to work against them.As a result, every publisher needs to take advantage of the technology.

          The rise of contextual 2.0

          Historically, contextual targeting involved analyzing a publisher’s content and aligning it with a broad set of content categories. This was tied to the technology’s limitations and made early contextual fairly imprecise – like using a meat cleaver instead of a scalpel. This would result in mismatched ads and content. However, as AI has evolved, so has contextual targeting.

          Today’s technology can analyze millions of articles spanning years. Natural language processing (NLP) now works across many languages, greatly expanding contextual targeting’s reach. Computer vision can even evaluate images and videos in real-time. The vast amount of content available for AI analysis has fundamentally transformed what contextual can do, allowing the targeting to go well-beyond simple keyword matching or broad categories. It can be based on more nuanced categorization aligned with IAB taxonomies.

          Additionally, AI provides deeper insights into content consumption patterns and engagement. It can uncover advertiser interests in content that might not be immediately obvious to humans and predict future content patterns and consumption trends. These capabilities enable publishers to better monetize their inventory and audiences, identify and sell high-performing or in-demand inventory at a higher CPM, and expand interest with non-endemic advertisers.

          Pairing contextual with behavioral targeting

          With all of that in mind, contextual and behavioral targeting should not be considered rivals, though it’s easy to think that way. Ultimately, they’re complementary tools. When used together, they can address signal loss that threatens publisher addressability and monetization.

          Publishers can create more effective audience segments—whether through available identifiers or AI modeling—by combining it with contextual data about their inventory and content. This integration creates a two-way benefit: audience segments are matched with relevant content, while content is tailored to specific audiences through contextual targeting. This approach allows publishers to better align their content with interested audiences, improving targeting efficiency and ad relevance.

          For example, a luxury skincare brand promoting a new anti-aging serum might use contextual targeting to place ads on beauty and wellness websites. However, this approach alone could attract too broad an audience, even with more nuanced categorization. Behavioral targeting can refine this focus by looking into the characteristics of those with a history of buying premium skincare products or visiting luxury brand sites. Further, behavioral targeting can reveal new clues about potential customers to inform incremental reach via contextual targeting. This combined approach, blending the two types of targeting, ensures ads reach those most likely to be interested in and able to afford the product, as well as new prospects never before considered. This, of course, benefits both the publisher and the advertiser.

          The surge in programmatic curation is also expanding to contextual and behavioral targeting, thanks to new technologies. New AI-driven machine learning models analyze historical online content consumption to identify audience trends. Then, a recommendation engine identifies high-indexing affinity groups for predictive contextual targeting. These affinity groups are then mapped to specific content categories, curated across premium inventory, and packaged into Private Marketplace (PMP) deals. This takes the synergy between contextual and behavioral to a whole new level. Plus, it’s SSP and publisher-led versus DSP and demand-led, a welcome shift for publishers..

          AI and a brighter contextual future

          To fully leverage these opportunities in contextual and contextual + behavioral targeting, publishers must invest in robust data infrastructure and collaborative capabilities. It’s crucial to identify gaps with partners and enhance existing systems. This proactive approach will help publishers adapt to the ever-evolving digital landscape, maintaining relevance and gaining a competitive edge in an ecosystem that often seems to work against them.

          Advertising | Supporter Spotlight

          Aligning news publishers and advertisers in election years

          News publishers are an invaluable part of our society, providing essential information and fostering public discourse. But as we’ve seen so far in 2024, contentious election cycles can increase challenges for digital advertising in the news sector.

          Despite the audience engagement that comes with news content, brands sometimes choose to avoid advertising next to volatile topics altogether. Advertisers still benefit greatly from the audiences of established news outlets – even during election years, when volatile content is more commonplace. This is why it is in both sides’ best interests to revise their approach to brand suitability in 2024 and beyond.

          Election season dynamics: Impact on buyers, sellers and audiences

          Divisive rhetoric and misinformation surge during election years and make online audiences more sensitive to the content they consume. Despite this, publishers still need to juggle the unique requirements of each buyer with a fast-paced and unpredictable 24-hour news cycle. This often requires continuous adjustments to keep up with demand and maintain brand suitability alignment.

          Advertisers face their own election-year challenges. Our research emphasized this with a survey revealing that two out of three consumers would likely stop using a brand or product if it appeared next to false, objectionable or inflammatory content. Some brands respond to this increased wariness by tightening their avoidance measures, which can inadvertently reduce campaign reach and negatively impact news publishers.

          Instead of both sides continuing to miss out on vital opportunities to generate revenue and reach engaged audiences, there is an opportunity to improve the way we communicate and transact with each other. A few small adjustments can make a big difference with the right tools and tactics.

          Best practices to consider during election seasons

          These best practices are a great starting point for brands and publishers to manage brand suitability requirements and reduce brand suitability violations. They can also make longer-term partnerships more sustainable once a contentious election cycle is over.

          1. Advocate for context-based suitability measures over keyword-based ones

          More nuanced brand suitability settings offer smarter and more sophisticated protection than simple keyword-based tools. They enable buyers to make decisions based on the actual content and its sentiment. Some solutions allow advertisers to go deeper than a “yes or no” approach to the screening process by letting buyers choose their own levels of risk tolerance toward certain types of content, such as whether it is user-generated or more authoritative.

          2. Promote use of inclusion lists as a trusted publisher

          Some brands and advertisers are mindful of the value that trusted news sources provide in any climate, both to their own goals and to society at large. These buyers might be more open to tools like inclusion lists, which can preserve their access to compatible news publisher inventory no matter what their brand suitability settings might be. The only hurdle is that they might not be aware of their existence. Publishers can take this as an opportunity to educate their partners and ensure their placement on any inclusion lists that they eventually develop.

          3. Facilitate open and frequent communication with partners

          Advertisers and publishers should work together and troubleshoot potential delivery issues as quickly as possible. Even small problems can significantly impact campaign performance if they are left unchecked for long enough. For example, is your team asking partners to update their keyword lists regularly? Do they understand the partner’s sensitivities enough to align their ads on the appropriate pages? Questions like these, alongside a consistent reporting schedule are a helpful way to ensure that partners are on the same page and budgets are spent effectively on the right audiences.

          Brand suitability: Advancing the conversation

          News is one of the most engaging forms of media. And during an election cycle, audiences seek out information to inform their decision making. Despite its potentially polarizing nature or volatility, news can provide a highly engaging context for brand placements. However, to ensure that the context is ultimately suitable, it is important to engage in nuanced decision making and to keep the conversation going.

          Advertising | Supporter Spotlight

          Is your programmatic data strategy missing the commerce connection?

          Commerce data unlocks rich insights into consumer behavior, preferences, and purchase patterns, meaning brands can now craft programmatic strategies with more precision than ever before. The integration of commerce data with programmatic represents a new inflection point for digital advertising, arming brands with a competitive advantage that drives more personalized campaigns and better results. For media companies, this means partnering with the right DSP so that you can access and leverage these data signals to help your advertising partners reach their goals.

          Why traditional data providers are falling behind

          The first question you might be asking is: Why does the industry need commerce data at all? Don’t we already have plenty of data providers in market right now?

          In short, yes. But not all data is created equal.

          Traditional data providers have historically faced significant challenges which, over time, have undermined the quality and effectiveness of their insights.

          Here are a few examples of areas where current data models fall short [FH1] [GI2] from the demand-side perspective:

          Data quality and accuracy

          • Incomplete data. Modeled data often leans on partial information, leading to inaccurate insights.
          • Outdated data. In fast-changing markets, stale data can lead to ineffective targeting.
          • Data silos. Data often lives in silos, making it hard to create a cohesive model that works across platforms.

          Algorithmic bias and transparency

          • Biases in data. If the data used to build models is biased, it can lead to skewed targeting that reinforces biases.
          • Discrimination. There’s a risk of unintentionally excluding or targeting specific groups, leading to unfair practices.
          • Transparency. A lack of transparency around how models operate can erode trust from consumers and regulators.

          Introducing commerce data—a new approach to data-drive marketing

          In today’s digital world, commerce is everywhere.

          Shoppers generate a wealth of information at every touchpoint, from their first product search to their final purchase. This rich data reveals not just what people buy but how and why they make decisions.

          Commerce data combines consented purchase and intent signals, built on real-world consumer behaviors. It covers everything from demographics and location to product views, last purchases, offline sales, and ad clicks. Layer in some AI, and patterns begin to emerge which can supercharge audience targeting and ad strategies.

          In the context of programmatic, commerce data is made up of various events and signals based on consumer behavior, including:

          • Product views and cart additions
          • Purchases and ad clicks
          • Contextual data like URLs, categories, and keywords
          • Product details like categories, SKUs, prices, and descriptions
          • Identifiers like hashed emails and visitor IDs
          • Offline sales

          Putting commerce data to work

          When combined with commerce-focused AI, commerce data powers some of the most effective advertising strategies for today’s modern marketer. That includes:

          • Identifying in-market consumers: Knowing who’s ready to buy and the best time and place to reach them.
          • Product recommendations: Suggesting products and bidding based on the value-to-cost ratio of each impression.
          • Audience building: Creating lookalike audiences to find new prospects and zero in on people actively shopping for specific products.

          How to get the most out of commerce data

          When you’re evaluating a commerce data provider, asking the right questions is essential to getting the best results. You’ll want to dig into a few key areas to make sure you’re making the right choice.

          First, consider data collection and sources. It’s important to understand where the provider’s data comes from and how it’s gathered. Is it collected directly, such as onsite, or inferred through modeling? Knowing this helps gauge the reliability of the data you’re working with.

          Next, think about data quality and accuracy. You’ll want to ask how they ensure their data is accurate and complete. Are they refreshing it in real-time, daily, or on another schedule? Consistency here can make or break the effectiveness of your campaigns.

          Then, there’s data segmentation and customization. How is the data broken down, and what criteria are used for segmentation? Can the provider integrate data across multiple devices and channels? Flexibility here can be a big win when you’re targeting your audience across platforms.

          Of course, data privacy and security is the cornerstone of any digital activation. You need to know how your provider protects the data they handle. Is personal data anonymized or pseudonymized, and can they offer you transparency into how they’re collecting and processing that data?

          Time to leverage the commerce data opportunity

          There’s no doubt that commerce data presents a huge opportunity for brands to enhance targeting, personalize messages, and drive better results. By working with the right DSP, you can quickly and effectively leverage these new data signals in order to help your advertising partners more effectively achieve their objectives.

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