Media

Dashboards often focus on easily measurable metrics and direct conversions, but they may miss the influence of indirect touchpoints, offline interactions, or the cumulative effect of multiple channels on customer decisions. This can lead to undervaluing certain marketing efforts and misallocating budgets.

Nuanced attribution models, such as multi-touch or data-driven models, assign credit to all relevant touchpoints in the customer journey, not just the first or last interaction. This provides a more accurate understanding of which channels and campaigns truly drive conversions, enabling marketers to optimize spend and better align strategies with audience behavior.

Traditionally, retail media networks focused mainly on the bottom of the marketing funnel, targeting consumers close to purchase with performance-driven ads. The shift to center stage means RMNs are now used as strategic marketing pillars across the entire consumer journey, from awareness and consideration to conversion. This full-funnel approach leverages rich consumer data and innovative ad formats to engage customers at every stage, enhancing brand visibility and driving measurable outcomes beyond just immediate sales.

Retail media networks provide advertising space within online retail environments, allowing brands to target consumers with relevant ads at various stages of their shopping journey. By using deep customer data and diverse ad formats, RMNs help brands create awareness at the top of the funnel, influence decision-making in the middle, and drive conversions at the bottom. This integrated approach ensures consistent and personalized engagement across digital and physical retail touchpoints, making RMNs a powerful tool for full-funnel marketing.

People are increasingly relying on social media for news due to the comfort of algorithms and the convenience of short-form multimedia content, which provides easy access to local, national, and international updates. Social media platforms like TikTok and YouTube have become key sources for current events, especially among younger audiences[2][3][4].

Social media has become a primary news source for millions, especially younger audiences, as it offers personalized content and real-time updates. This shift is part of a broader trend where streaming and social platforms are surpassing traditional media like TV for news consumption[3][4].

Social media has surpassed TV as the top news source for Americans due to changing media consumption habits, with more people preferring to access news through platforms like Facebook, YouTube, and X (formerly Twitter). This shift is driven by the convenience, personalization, and influence of online personalities and influencers, who often present news in a more casual or partisan style. The trend is also accelerated by the decline in traditional TV viewership and the rise of digital-native audiences.

Relying on social media for news carries risks such as exposure to misinformation, partisan narratives, and unchecked claims, as online influencers and politicians increasingly bypass traditional journalistic scrutiny. Nearly half of global respondents identify online influencers as major sources of false or misleading information. To maintain a critical perspective, consumers should practice discernment, fact-check information from multiple sources, and be aware of the potential for bias and misinformation in personality-driven news content.

According to recent studies, 54% of Americans now use social media as their main source of news, surpassing television, which was previously the dominant news source. This marks a significant shift in news consumption habits in the United States.

The rise of social media as the primary news source has sparked debate about the implications for information quality, misinformation, and the role of traditional journalism. Experts are concerned about the spread of unverified information and the potential for echo chambers, while others highlight the increased accessibility and immediacy of news on social platforms.

AI is playing a more prominent role in content recommendation, making algorithms smarter at understanding user preferences and behaviors. This leads to highly personalized feeds, where content is prioritized based on engagement, relevance, and real-time trends, fundamentally reshaping how attention is captured and user engagement is measured[1][3][5].

In 2025, social media algorithms prioritize user relevance (content matching past behavior and preferences), engagement signals (likes, comments, shares, saves, and longer watch times), freshness and virality (timely and trending content), and AI-driven personalization. These factors ensure users see content most likely to keep them engaged and scrolling[2][4][5].

Meta plans to integrate AI into its ad creation process by allowing brands to create and target ads using AI tools. This means that advertisers can provide basic information like a product image and budget, and Meta's AI will handle everything from generating ad content to targeting users across platforms like Facebook and Instagram.

AI-powered ad creation offers several benefits, particularly for smaller businesses. It can help scale creative output without increasing manpower, provide real-time personalization, and automate budget recommendations. This can be especially beneficial for resource-constrained teams or those with limited advertising infrastructure.

The guidelines outline four primary methodological approaches: data signals, visual and audio tracking, physiological and neurological observations, and panel- or survey-based inputs. These approaches aim to provide a comprehensive framework for measuring attention consistently across different formats and platforms.

The guidelines are important because they aim to standardize how attention is defined and measured, enhancing clarity and consistency. This allows marketers to better evaluate and compare different attention measurement methods, ultimately aligning attention metrics with business outcomes.

Google has introduced advanced generative media models such as Veo 3, Imagen 4, and Flow. Veo 3 supports audio prompt generation for more immersive video content, Flow allows creators to adjust video production elements like camera angles and cast, and Imagen 4 enhances image generation with higher accuracy and clarity, supporting various aspect ratios and up to 2K resolution.

Google is leveraging AI to transform advertising by creating new formats such as Demand Gen campaigns that span YouTube, Shorts, Discover, Gmail, and Google surfaces. Features include shoppable connected TV ads with QR codes, Peak Points for precise ad timing on YouTube, accelerated checkout directly from ads, and generative AI that reformats and extends videos into multiple aspect ratios automatically.

AI is revolutionizing media production by automating content creation tasks such as ad copywriting and video production, enabling faster and more efficient storytelling. It also enhances audience segmentation and predictive modeling, allowing for more personalized and engaging content. However, while AI accelerates these processes, human expertise remains crucial to maintain creativity, brand integrity, and strategic direction.

As AI accelerates media creation and curation through automation and generative technologies, it raises significant challenges in preserving truth and trust. The rapid pace and scale of AI-driven content production can lead to misinformation or reduced content credibility if not carefully managed. The media industry must balance the benefits of speed and efficiency with rigorous verification and ethical standards to maintain audience trust in a rapidly evolving landscape.

Retail media networks are advertising platforms owned and operated by retailers, such as Amazon, Walmart, and Target, that allow brands to place targeted ads directly within the retailer’s digital ecosystem—on their websites, apps, or even in-store digital displays. Unlike traditional digital advertising, retail media leverages the retailer’s first-party data to target consumers based on actual shopping behavior, offering highly relevant ads at the point of purchase and enabling closed-loop measurement of ad effectiveness[5][1][4].

Retail media networks are considered a game-changer because they enable brands to reach consumers with highly targeted, relevant ads at the exact moment they are making purchasing decisions, both online and in-store. This proximity to the point of purchase, combined with the use of first-party data and closed-loop measurement, results in stronger return on ad spend and more effective marketing. As third-party cookies phase out, retail media networks provide a reliable way for brands to target consumers, reshaping the advertising landscape and offering new opportunities for enhanced consumer interaction[1][5][4].

Material 3 Expressive focuses on using vibrant colors, intuitive motion, adaptive components, and other design elements like shape, size, and containment to create an emotionally impactful UX. These elements help draw attention to key actions and group similar elements together, making products more usable and engaging.

Material 3 Expressive aims to foster deeper connections by creating emotionally impactful user experiences. This is achieved through research-driven design tactics that boost engagement, usability, and desire for products. By incorporating emotional design patterns, brands can create more memorable and satisfying interactions with their users.

Multi-touch attribution is a marketing approach that assigns credit to multiple touchpoints along the customer journey, providing insights into the effectiveness of various marketing channels. This approach helps marketers understand the influence of each touchpoint on conversions, allowing for more informed decisions about resource allocation and optimization of marketing efforts (Adobe, 2023; Salesforce, 2023)[2][4].

Combining multi-touch attribution with marketing mix modeling and incrementality testing provides a comprehensive understanding of customer behavior while addressing evolving privacy regulations. This multi-faceted approach helps marketers make informed decisions by accounting for various data sources and methodologies, ensuring compliance with privacy standards and enhancing the accuracy of marketing analytics.