Digital Marketing: Alphabet, Meta, and Amazon in Battle for Advertising Billions

In a guest contribution by Markus Caspari, the major platforms are vying to dictate how advertising success is measured and offering seemingly objective solutions for it. This sheds light on the new power struggle in digital marketing.

Beyond the AI race, there has been a side stage battle among the major platform providers for some time. Alphabet, Meta, and Amazon are not competing for the best language model but rather for control over how advertising success is defined. This also determines where the billions of advertising budgets will flow in the future. The in-house tools, dashboards, and metrics are under suspicion of favoring their own tightly knit ecosystems to attract more advertising revenue, making them vulnerable.

A growing number of marketers are seeking intelligent alternatives to these tools and are focusing on more objective approaches where marketing traditions merge with modern techniques. This involves utilizing new AI, automation, and analytical capabilities while recognizing that certain effects like the fundamental principles of advertising effectiveness remain unchanged.

Enter Marketing Mix Modeling (MMM). These models have been established for years and are used in media planning to calculate causality between media investments and the ROI of advertising. MMM goes beyond analyzing what happens within a single ecosystem by scrutinizing all marketing activities of a company, including television ads, print, outdoor advertising, and considering factors like seasonality or weather conditions. The calculations typically employ the Bayesian model, a statistical approach that updates probabilities step by step and combines historical data (prior knowledge) with new observations.

Ultimately, this calculation determines how much advertising budget should be allocated to each media channel. With the rise of platform providers in recent years, traditional media outlets have suffered compared to social media, internet search, programmatic advertising, and retail media. This shift has unjustly led to dramatic consequences for journalism funding and democracy as a whole. It’s worth noting the 18 initiative aimed at promoting free, secure, and sustainable media as the 18th Sustainable Development Goal of the United Nations to protect democracies.

The platform providers initially focused on retraining a generation of marketing managers about concentrating on short-term marketing KPIs about a decade ago. They supported this development with real-time dashboards featuring intuitive interfaces and many supposedly untouchable objective metrics. Since then, performance marketing has been brimming with confidence as these metrics can be easily presented to internal and external stakeholders compared to supposedly “soft,” harder-to-measure marketing indicators like brand awareness and perception.

This often led to an overemphasis on easily measurable performance marketing activities at the expense of long-term brand-building initiatives. Consequently, debates about “Branding versus Performance” have resurfaced regularly with similar arguments being recycled. The impact of AI has further fueled these discussions lately. Marketing executives are increasingly self-critical on this matter questioning whether the focus on “Walled Gardens,” i.e., the enclosed ecosystems of platform providers, and constantly emerging digital platforms have led to excessive audience fragmentation making marketing less effective and efficient.

Interestingly enough, the very platform providers now offer solutions to the challenges they created: their own MMM products. They promote providing a holistic long-term decision-making basis with MMM considered as the pinnacle of media budget attribution even before digital marketing gained prominence but somewhat faded into obscurity.

The approach goes far beyond typical attribution models; it is holistic and extends well beyond aspects of digital marketing. It analyzes all available data within a company including offline/online marketing budgets, sales data, seasonality trends comp