Rosa Almarza Del Campo’s Post

🚀 🌟 Encantados de ver cómo un número creciente de marcas y agencias eligen a illuma como su tecnología preferida para una segmentación contextual precisa y de alto rendimiento. ¿Interesado en expandir los contextos de mejor desempeño en tus campañas con illuma Reacts™? ¿O en enfocarte en categorías y subcategorías específicas con illuma Precision™? Envíame un mensaje hoy mismo para descubrir cómo illuma puede elevar tus campañas digitales. #AdTech #AI #ContextualTargeting #DigitalAdvertising #IllumaTechnology

Dove has always been a pioneer in redefining beauty standards, and their latest campaign is no exception. As we approach a future where 90% of online content could be generated by AI by 2025, Dove reinforces its commitment to celebrating Real Beauty. In honor of the 20th anniversary of their iconic “Real Beauty” campaign, Dove is once again leading the way in ensuring that all beauty is genuine and inclusive. This initiative couldn't be more timely and significant. Watch their powerful video here: https://lnkd.in/duFezhvB #Dove #RealBeauty #AI #Inclusivity #BeautyStandards

If you want to understand what are Attention metrics, how they are calculated , and how they influence campaign results, read below ⬇ 🔹 What are Attention Metrics? Attention metrics (or AU- Attention Unit), measure the probability that a media placement will capture and retain viewer attention. Unlike traditional metrics, AU is derived from a machine-learning model that processes full-funnel outcome data, exposure data, and eye-tracking data. This comprehensive approach provides a nuanced measure of media quality. 🔹 How are Attention Metrics Calculated? -Exposure Data: Tracks how often an ad is served and its duration in the viewer's viewport. -Eye-Tracking Data: Measures where and for how long a viewer’s gaze lingers on the ad. -Full-Funnel Outcomes: Integrates data on consumer actions such as brand awareness, recall, and conversions. 🔹 Influence on Campaign Success Attention metrics have proven to be more predictive of advertising success than traditional metrics. Key findings include: -Higher Conversions: High-AU media placements drive significantly higher conversion rates. For instance, high-AU placements can lead to up to 398% better performance in sales conversions for OLV media compared to baseline placements . -Enhanced Brand Metrics: Media with higher AU scores have shown to improve unaided awareness by 53%, familiarity by 53%, and purchase intent by 108% . -Cost Efficiency: High-quality media can also reduce costs. For example, shifting to high-AU placements can result in a 69% lower cost per conversion #DigitalMarketing #AttentionMetrics #MediaOptimization

"Attention Time" Key takeaways extracted from Playground XYZ's recent whitepaper: 🔹 Attention Time Outperforms Viewability: Traditionally, viewability (at least 50% of an ad's pixels in view for one continuous second) has been the standard metric. However, the study shows that attention time (the duration users actually look at an ad) is 7.5 times more predictive for brand awareness and 5.9 times for brand recall. 🔹 Increased Brand Awareness and Recall: Campaigns with any attention time saw an average 25% increase in brand awareness and a 79% increase in brand recall, with individual campaigns achieving up to 117% and 147% increases, respectively. 🔹 Duration of Attention Time: Each additional second of attention increased the likelihood of improving brand awareness by 11% and recall by 7%. This highlights the importance of not just being seen, but actually being observed. These insights push us to redefine our advertising strategies. It's not just about ads being viewable, but about capturing and holding consumer gaze. Source: "Attention Time: Redefining How Marketers Measure and Optimize Ads in Real Time," Playground XYZ. #DigitalMarketing #ConsumerAttention #AttentionTime #AdOptimization #EffectiveAdvertising #MarketingInsights

📺 Content Signal Transparency 📺 One of the key challenges in programmatic TV is ensuring transparency about the context in which ads appear. This is crucial for media buyers who need to understand exactly what channel, genre, or show they are buying into to reach their intended audience effectively. 🌐 The Importance of Contextual Data in Streaming TV Unlike in traditional web and mobile environments where URLs and app bundles provide clear content signals, streaming TV lacks these direct indicators. This can lead to under-bidding due to uncertainty about the content's relevance or desirability. For example, a buyer might trust a popular app like Pluto TV but without specific content signals, might not bid aggressively for premium content that could actually be more valuable. 🔍 How Content Signals Enhance Programmatic Streaming To bridge this gap, the industry has leveraged the IAB’s OpenRTB protocol, particularly with the introduction of version 2.6, which includes the content object for sharing detailed media information. This standard allows media owners to signal key content details within each bid request: Genre, Livestream status,Content rating, Language, Channel, Network and Show-level data (including series name, season, and episode) These signals enable buyers to make more informed decisions, ensuring ads are placed in suitable and effective contexts—like ensuring an ad for unlimited salad appears during a cooking show about salads, enhancing relevance and impact. 🚀 Challenges and Opportunities While the technology and standards for sharing content signals are in place, widespread adoption across the supply chain is necessary to realize their full potential. Media owners have the flexibility to share varying degrees of transparency, from broad genre categories to specific show details, influencing how inventory is valued and transacted. 📊 Normalizing Content Information Consistency in how content information is categorized and described across different media owners remains a challenge. For instance, one media owner might categorize a cooking show under "Food & Drink" while another might use "Cooking." Such discrepancies can complicate targeting and valuation efforts. ✨ Conclusion Enhancing content signal transparency in streaming TV will not only align it more closely with the clarity seen in linear TV but also empower marketers to invest more confidently. This transparency leads to better-targeted campaigns, optimized ad spend, and ultimately, a more engaged audience. #programmatic #CTV #digitaladvertising

🌐 Navigating Carriage and Inventory Sharing in Streaming TV 🌐 Today, let's explore a foundational yet complex aspect of the streaming TV ecosystem: carriage and inventory sharing agreements. These agreements are pivotal for content distribution but introduce intricate challenges that differ significantly from traditional linear TV. 📺 The Complexity of Streaming Agreements In the streaming universe, content owners often enter agreements with various distributors like device manufacturers or free ad-supported streaming TV (FAST) app owners. These partnerships are crucial for extending content reach and involve not just content carriage but also specific rights for advertising inventory. 🔀 Understanding Inventory Splits Inventory sharing can be complex, with multiple parties often having rights to sell advertising on the same program or channel. Common types of inventory splits: - Request Level: Here, inventory within a single ad pod might be split among several parties, increasing the risk of ad duplication and competitive separation issues. - Pod Basis: Certain pods during a streaming session may be allocated to different rights holders, helping manage viewer experience more effectively. - Streaming Session: The most viewer-friendly arrangement where one entity controls all advertising within a session, ensuring optimal frequency and minimal duplication. 🌟 The Importance of Transparency With such diverse access points to inventory, transparency becomes crucial. Buyers need clarity about who holds the inventory rights and the audience their ads will reach. This is essential not just for targeting and brand suitability but also for effective frequency management and measurement. 📊 Enhancing Transparency with Technology The advertising technology community, spearheaded by initiatives like IAB Tech Lab's OpenRTB 2.6, has made strides in improving transparency. The introduction of the inventorypartnerdomain within the app-ads.txt standard, for example, helps articulate who is authorized to sell inventory, aiding buyers in verifying and trusting the source of each ad opportunity. 🚀 Why This Matters As streaming TV continues to evolve, the need for clear, transparent carriage and inventory agreements will only grow. These agreements are not just about ensuring revenue streams for media owners but also about providing a high-quality, trustworthy advertising environment that can scale effectively within the programmatic ecosystem. As industry professionals, understanding and navigating these complexities is key to leveraging the full potential of streaming TV advertising. #CTV #digitaladvertising #programmatic

🌍 Reducing the Carbon Footprint in CTV Advertising Through Ad Podding In our ongoing exploration of Connected TV and programmatic advertising, today we address an increasingly crucial aspect of our industry: sustainability. Specifically, we'll look at how advancements like ad podding in OpenRTB 2.6 are not just streamlining operations but also significantly reducing our digital carbon footprint. 🌱 The Role of Efficiency in Sustainability As the digital advertising industry grows, so does its environmental impact. However, the adoption of more efficient technologies such as ad podding can play a pivotal role in pushing towards net zero emissions by minimizing unnecessary digital waste. 🔄 How Ad Podding Enhances Efficiency Ad podding groups multiple ad slots into a single bid request. This consolidation drastically reduces the number of transactions and, consequently, the energy consumed during data processing. Let's break down the difference this makes: - Without Ad Podding: A typical 120-second ad break might generate upwards of 120 individual bid requests across platforms. Each request requires processing and transmits largely redundant data, accumulating significant carbon emissions. - With Ad Podding: The same ad break, when processed as a single podded request, might result in only 15 bid requests. This not only simplifies data handling but also drastically cuts down on the energy used for processing and transmitting data. 📉 Impact on Carbon Emissions By consolidating bid requests, ad podding reduces the number of transactions and data duplications significantly—by up to 88% in outgoing requests and 59% in returned bids. This reduction in computational demand directly translates to lower carbon emissions, demonstrating a tangible environmental benefit. 🌟 Why This Matters The shift to ad podding isn’t just a technical improvement—it’s a step towards a more sustainable future in digital advertising. By reducing the volume of data processed and the energy consumed, we are not only optimizing operational efficiency but also contributing to an eco-friendlier industry. The path to sustainability is a collective journey, and technologies like ad podding enable us to make impactful changes. As more companies adopt these practices, the potential for significant reductions in the industry’s carbon footprint grows exponentially. #CTV #digitaladvertising #sustainability #programmatic

Let´s continue with this programmatic CTV series. Today's topic is Transparency. 📺 The Importance of Transparency in CTV In the rapidly expanding world of streaming TV, transparency has become a cornerstone for sustainable growth. Marketers seek clarity about the environments in which their ads are placed—similar to the expectations set in traditional linear TV. This includes detailed insights into content type, such as the network, genre, or specific shows, ensuring brand suitability and effective targeting. 🚀 How Transparency Works in CTV Unlike traditional web environments, CTV lacks URLs, using instead app bundles and detailed content objects to convey important contextual information: Genre, livestream status, content rating, language, channel network, and detailed show-level data (including series name, season, and episode) This content object model is part of the IAB’s OpenRTB standard and is crucial for media owners to communicate the specifics of the video stream being consumed. This information goes through DSPs, where buyers can assess the relevance and value of each impression opportunity. 🌟 The Benefits of Enhanced Signal Transparency By improving the granularity of signals passed in the bidstream, buyers gain a deeper understanding of the content their ads may appear within. This prevents scenarios where ads could be placed in unsuitable contexts and allows for more strategic bidding—enhancing the value perceived by buyers and the yields for media owners. For example, understanding the exact nature of content, like a specific show on a FAST (free ad-supported TV) platform, can drastically influence bidding strategies and campaign effectiveness. 🔍 The Future of Transparency in CTV As more streaming platforms adopt advertising and the competition intensifies, the need for refined yield optimization strategies becomes more critical. Embracing transparency not only builds trust but also empowers media owners to showcase their inventory more effectively, facilitating curated deals and targeted advertising strategies that align with advertiser goals. Getting higher transparency in CTV will unlock advertiser spending and build growth in the programmatic ecosystem. #CTV #digitaladvertising #media