Top trends and news about paid media and growth through the lens of industry leaders
PostHog and the Future of Product Analytics
PostHog has consolidated into a single open-source product the capabilities that previously required four or five vendors, and the capital market has validated this with a $1.4 billion valuation. The model works especially well for technical teams in the startup and SMB segments, but the "total consolidation" thesis has real limits.
Google Meridian Model for MMM studies
Marketing mix modeling has evolved rapidly, driven by open-source tools that combine econometrics, machine learning, and accessibility. In this context, Meridian, developed by Google, stands out for its Bayesian modeling approach and transparent implementation. But it’s not alone: models like Robyn (from Meta), LightweightMMM by PyMC Labs, and other custom libraries are also part of this wave of analytic democratization. Each has pros and cons depending on use case, team maturity, and available resources—and often the choice simply comes down to technical stack compatibility or internal team preferences.
Infillion acquires Catalina: real purchase data now in Programmatic
Infillion acquired Catalina, the world's largest source of deterministic purchase data: 130 million households, 70 retail banners, $600 billion in annual spending tracked. The data will be exclusive to the Infillion platform. For the US programmatic ecosystem, this consolidates verified purchase data within a single DSP. For LATAM, where that infrastructure doesn't exist at comparable scale, the measurement gap widens while retail media investment grows at 28% annually.
Manus AI in Meta Ads Manager: the platform now also operates your account
Meta integrated Manus AI into Ads Manager as an autonomous agent available to all advertisers. Current capabilities are reporting and analysis, not campaign execution. But the conflict of interest is structural: the platform that sells your inventory now also controls the intelligence layer inside your account.
Omnichannel Studies make a comeback powered by Machine Learning
The proliferation of media channels and the increasing importance of data privacy are driving marketers to seek alternative strategies for measuring and optimizing their advertising efforts. In this new context, traditional Marketing Mix Models (MMMs) emerge as a viable alternative to address these challenges, aiding marketing professionals in making data-driven decisions with confidence.
ChatGPT Ads Are Now in Testing: An Operational Update
OpenAI started testing ads in ChatGPT for free tier users in the US. Ads appear below responses without influencing content. There's no buying platform and no performance benchmarks. The honest move today: awareness, not resource investment.
Mobile Attribution in 2026: Beyond the MMP
Before App Tracking Transparency (2021), Mobile Measurement Partners (MMPs) operated with deterministic precision. The IDFA allowed for a direct connection between an ad impression, an install, and an in-app event, making MMPs the "single source of truth". Post-ATT, with IDFA opt-in rates hovering between 15-30%, that model collapsed. MMPs migrated to probabilistic attribution using IP addresses, timestamps, and device characteristics to make "educated guesses". Meanwhile, SKAN (SKAdNetwork) offers deterministic but aggregated data, often with 24-48 hour delays that hinder daily optimization. The result: The "Big Five" dominant MMPs (AppsFlyer, Adjust, Branch, Kochava, Singular) now primarily display what ad networks send them. Real attribution is now performed by Meta, TikTok, and Google using their own proprietary models.
ChatGPT Ads: The Rise of Answer Engine Marketing (AEM)
The launch of ads in ChatGPT marks the transition from Search Engine Marketing (SEM) to Answer Engine Marketing (AEM). This shift will impact performance measurement, especially in Latin America, where deployment is expected by late 2026. Success will depend on brands' ability to become the suggested answer, prioritizing semantic influence over traditional click bidding.
Are Marketing Mix Model Guidelines Applicable to my Team?
Marketing Mix Models are powerful tools that provide valuable insights into the effectiveness of marketing activities. While high-level MMMs offer a broad overview, adapting these studies to address specific segments, markets, or tactics can yield more actionable insights. By conducting segmented analysis, collecting granular data, and performing deep dives, brands can optimize their marketing efforts to meet the diverse demands of their consumers. It’s a tool that will help marketers and C-Level executives to understand the DNA of their marketing efforts and be more effective in how they develop future strategies, while looking to improve their team development and knowledge.
The click tax: The trap of Retail Media fragmentation in LATAM
If you are a mass consumption brand in Latin America, you are not buying advertising; you are paying a "data toll" to each retailer separately. Retail Media is the great promise of the decade, but the lack of standards is creating a black hole in marketing budgets. This post analyzes why your reports from Mercado Ads and Rappi are never going to match and how to avoid being charged three times for the same sale.
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Julián Iglesias Bello
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Julian Chadwick