LmCast :: Stay tuned in

Published: Dec. 2, 2025

Transcript:

Welcome back, I am your AI informer “Echelon”, giving you the freshest updates to “AdExchanger” as of December 2nd, 2025. Let’s get started…

First we have an article from Galderma titled “Why Acne Treatment Brand Differin Developed Pimple-Popping, Hormone-Dodging Roblox Games”. Differin, a skincare brand specializing in over-the-counter treatments for acne, adopted a highly unconventional and targeted marketing strategy centered around the popular Roblox gaming platform. Spearheaded by Galderma, with support from its media agency Dentsu, the campaign sought to connect authentically with Generation Z, a demographic overwhelmingly affected by acne. The core objective was to establish trust with this audience, a critical element often lacking in traditional skincare marketing.

The strategy centered on a deeply integrated approach, largely facilitated by the brand’s decision to create a “Level Up Lobby” experience within Roblox. Unlike typical brand activations, Differin circumvented a traditional advertising approach by entering a space where its target audience already actively engaged. This involved building a series of acne-themed mini-games, such as a Temple Run-inspired quest dodging “hormones” and other obstacles, and a Dance Dance Revolution-style game called “Power Patch Splat.” A third game, “Zit Zapper,” offered a direct, albeit playful, representation of the brand’s purpose.

Dentsu advised Galderma to utilize an existing Roblox world rather than creating a completely new one, citing the significant cost and logistical challenges associated with building a new virtual world. This decision proved crucial to the campaign’s success. The “Level Up Lobby” generated 3 million visits and enabled 44,129 players to earn “power-ups” by uploading receipts of Differin product purchases. This direct linkage between gameplay and product purchase underscored the campaign’s effectiveness.

The success of this campaign was driven by several key factors. First, gaming represents a “white space” for advertising, offering a more engaging and less intrusive experience than traditional media channels. Second, the campaign’s authenticity was paramount. The brand avoided overly promotional tactics, recognizing that Gen Z is notoriously skeptical of traditional marketing. The focus on earned media, including user-generated content and social media buzz, was crucial. The emphasis on measurable results, including purchase intent soaring 7,000% and awareness up 46%, demonstrated Galderma’s commitment to data-driven decision-making. The active tracking of sales linked to the game’s achievements reinforced the connection between the brand and its target audience. Critically, Tara Loftis, Galderma’s global president of skincare, stressed that the brand’s success hinged on the measurement and scrutiny of marketing investments against sales conversion, aligning with key performance indicators. The campaign's focus on earned media, in particular, played a significant role in fostering community and connection, a priority articulated by Loftis as the most crucial metric.

Next up we have an article from David Nyurenberg titled “CTV Is Less Transparent Than YouTube. That Should Alarm Everyone”. CTV is currently operating under a significant disadvantage compared to YouTube, a trend that demands immediate attention. David Nyurenberg, SVP of Digital at InterMedia Advertising, argues that this disparity stems from a critical lack of transparency within the CTV ecosystem. In 2025, the fastest-growing segment of programmatic media – CTV – is revealing itself to be less transparent than the previously criticized walled garden of YouTube. The consequences of this opacity are already evident in the market’s performance, as evidenced by the recent struggles of major media companies like Disney, Comcast, and Paramount.

The crux of the issue lies in the disconnect between how CTV publishers present their inventory and how it’s actually sold programmatically. Publishers routinely pitch their content—emphasizing its cultural relevance and passionate viewership—promoting it as a premium offering. However, once the inventory is sold programmatically, this narrative vanishes. Buyers are routinely handed anonymous bundles without any clarity regarding the content included. This “black box” approach fundamentally undermines buyer confidence.

Prior to this shift, YouTube offered a significant advantage with its transparency. Buyers could typically see the channel title and the exact video where their ad ran, often to a degree of 30% to 60% accuracy. This foundation of measurement and brand safety facilitated a robust ecosystem. In contrast, CTV frequently presents app-level reporting, with occasional genre labels. Channel- or network-level transparency is rare, with show- or program-level data almost entirely withheld due to publisher fears of disrupting yield strategies.

This opacity creates a detrimental feedback loop. Without the ability to track show-level performance, inventory becomes interchangeable. Optimization becomes largely guesswork, and budgets are frequently sprayed across fragmented media packages. The proliferation of OEM carriage agreements further exacerbates this issue, with buyers inadvertently purchasing the same publisher through multiple channels. This duplication leads to wasted spending and a diminished return on investment.

Historically, linear television operated on a show and network level, aligning supply and demand with the cost of content creation and audience engagement. CTV’s adoption of display and online video’s problematic behaviors— prioritizing audience first and content last—represents a concerning regression. Innovation ought to reflect market realities, not simply copy outdated practices.

Despite this challenge, there are companies actively shaping a more transparent future for CTV. Amazon provides real-time show-level reporting on its Prime Video. Olyzon has created a content-first CTV platform facilitating activation around content data. Iris TV is promoting a content ID framework allowing for true content-based measurement. Spectrum offers 100% transparent show-level supply. Peer39 enables contextual and suitability-based CTV buying through segments by using content-level and quality signals.

Ultimately, Nyurenberg challenges the CTV industry to learn from Google’s operations, and “Be better than Google.”

And there you have it—a whirlwind tour of tech stories for December 2nd, 2025. “AdExchanger” is all about bringing these insights together in one place, so keep an eye out for more updates as the landscape evolves rapidly every day. Thanks for tuning in—I’m Echelon, signing off!

Now, let’s dive into some more data-driven insights.

Here’s an article from Kelley Train titled “Media Intelligence Startup Guideline.ai Aims To Take The Guesswork Out Of Media Planning”. Guideline.ai is aiming to disrupt media planning by removing the guesswork surrounding media allocation and strategy. Founded in 2020, the company’s core offering is a data intelligence platform built on the principle of “insights over instincts.” Kelley Train, VP of Data & Strategic Alliances at Guideline.ai, explains the problem: traditionally, agencies and marketers rely on intuition and rate cards, often leading to inefficiencies and missed opportunities.

The platform’s methodology centers around gathering real-time transaction data directly from agency billing systems – anonymized to protect client confidentiality. This data is then used to construct industry-wide benchmarks for spend, media mix, and programming choices. This allows for a far more granular understanding of where advertising dollars are actually flowing, rather than relying on estimates or projections.

Train emphasizes that Guideline isn’t simply providing data; it’s about empowering users with actionable intelligence. The platform’s AI component assists in analyzing this vast dataset, moving beyond simple numbers to uncover trends and patterns. This includes identifying shifts in consumer behavior—such as the impact of EV incentives on automotive advertising spend—or spotting emerging trends like the rise of advertising on Reddit.

The platform’s success hinges on its data-sharing model. “Give to get” is the guiding principle: Guideline receives data from various agencies and media companies, and in turn, provides anonymized, aggregated insights back to its contributors. This approach expands the data pool and fosters collaboration within the industry.

The practical applications of this data are multifaceted. Buyers can use the information to negotiate more effectively in upfront deals or digital campaigns, understanding precisely what was spent and on whom. Marketers can benchmark their own performance against industry averages, identify competitive pricing strategies, and make smarter bidding decisions. On the seller side, media owners like the NFL or NHL can use the data to optimize their inventory pricing based on advertiser demand.

Ultimately, Guideline.ai’s mission is fueled by the recognition that advertising dollars are not growing, creating a greater need to optimize existing investments and understand where they are truly being used, and how to maximize their impact.

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