A comprehensive stufy reveals that 57% of French internet users aged 18+ use tools to block online advertisements

A comprehensive study by mind Media-366 (in French) reveals that 57% of French internet users aged 18+ use tools to block online advertisements.

A Global Phenomenon

France's dramatic growth from 36% adoption (2016) to 57% (2025) reflects broader patterns. The study notes that "figures from our study align with those observed in the United Kingdom," while Poland reported 45% adoption in 2023. This 20-percentage-point increase over eight years represents a fundamental shift in user behavior across Western markets.

The demographic concentration is particularly concerning:

• 72% of 18-24 year-olds use adblocking tools
• 66% of higher socio-professional categories employ these technologies

Beyond Revenue Loss: The Analytics Crisis

While businesses focus on blocked ad revenue, adblocking creates a more insidious problem—it breaks the data collection ecosystem essential for calculating return on ad spend (ROAS) and making strategic decisions.

As the study emphasizes:

"This over-representation of young generations and favored socio-professional categories, often the most connected and valued by advertisers, accentuates the necessity for awareness among publishers, ad networks and their adtech partners."

The ROAS Calculation Problem

Accurate ROAS (Return on Ad Spend) measurement requires complete visibility into:

• Customer acquisition costs across channels
• Attribution pathways from first touch to conversion
• Lifetime value calculations based on behavior tracking
• Cross-device journey mapping

When a significant share of users block tracking scripts, ROAS calculations become fundamentally unreliable, leading to:

• Misallocated advertising budgets
• Incorrect channel performance assessments
• Failed campaign optimizations
• Inflated customer acquisition costs

The Technical Reality

Sacha Morard, former CTO of Le Monde group, explains how accumulated technical constraints create systemic challenges:

"Year after year, the phenomenon has developed among internet users, and now it effectively takes multiple forms. Beyond just blocking advertisements, it's a whole set of devices and technical solutions used by publishers for their customer knowledge or for their relationship with their audiences that are impacted: for tracking, recruitment, A/B testing, etc."

Client-side data collection becomes inherently unreliable when significant audiences actively block tracking scripts, creating:

1. Incomplete Attribution Models

Traditional last-click or multi-touch attribution fails when tracking pixels are blocked, making it impossible to accurately measure which channels drive conversions.

2. Skewed Performance Analytics

Since tech-savvy, high-value demographics disproportionately use adblockers, analytics systematically under-represent the most valuable customer segments.

3. Broken Conversion Tracking

E-commerce and lead generation campaigns lose visibility into their actual performance, making optimization impossible.

The Selective Blocking Challenge

The study reveals users aren't completely opposed to advertising. 46% occasionally disable adblockers to access content, while only 15% never disable blocking tools.

As the research notes:

"A slight majority of internet users equipped accept to sometimes or often expose themselves to advertisements (56%)", creating additional ROAS measurement complexity as user behavior becomes inconsistent across sessions.

Industry Recognition

The advertising industry is awakening to these challenges. One specialist notes: "For a long time, adblocking hasn't been a subject seriously taken into account by publishers and ad networks" due to mobile's initial immunity and technical complexity.

However, this is changing rapidly:

• Mobile data and adblocking expanding through Safari and dedicated apps
• New browsers incorporating native blocking
• AI-powered browsers likely to enhance blocking capabilities

Bertrand Gié, director of Le Figaro's News division, warns:

"What's concerning is the ease of using an adblocker now, and its generalization in recent browsers and probably in those that will be launched by AI providers."

Moving Beyond Client-Side Dependency

While the client side is constrained by rules defined by major players, the edge computing side remains under the control of the web service companies.

Edge computing utilizes distributed servers that offer computational power close to service users. Edge computing solutions (not to be confused with server-side tagging, see this article) provide accurate measurement despite widespread blocking technologies.

Beyond offering easier maintenance and removing client-side constraints, transferring logic from the client side to edge computing also improves web performance.

The AI Corrupted Training Data

Beyond immediate ROAS measurement challenges, widespread adblocking creates a more fundamental long-term problem: corrupted training data for artificial intelligence systems. Modern marketing automation, predictive analytics, and AI-driven optimization rely heavily on comprehensive behavioral datasets to function effectively.

When a significant share of users block tracking scripts, machine learning models trained on this incomplete data develop systematic biases. Customer lifetime value predictions, recommendation engines, and automated bidding systems all suffer from what data scientists call "selection bias"—where the training dataset doesn't represent the true population.

Consider the implications: AI systems trained primarily on data from non-adblocking users (typically older, less tech-savvy demographics) will consistently misunderstand and mispredict behavior patterns of younger, higher-value customer segments.

This creates a vicious cycle where AI-driven marketing becomes increasingly ineffective at reaching the most valuable audiences.

Personalization algorithms become particularly problematic. When recommendation systems and content optimization tools can't observe the complete user journey for 57% of visitors, they default to generic, less effective experiences. This degradation in user experience ironically drives more users toward adblocking solutions, further corrupting the training data.

The financial implications extend beyond immediate campaign performance. Organizations investing millions in AI infrastructure—from customer data platforms to predictive analytics tools—find their systems making decisions based on fundamentally flawed datasets. The garbage-in, garbage-out principle means expensive AI initiatives deliver suboptimal results when trained on blocked-data-polluted datasets.

Strategic Imperatives for Data Quality

This crisis demands immediate attention to data collection methodologies. Edge-side tracking becomes not just advantageous but essential for organizations serious about AI-driven growth. Only complete, unbiased datasets can train AI systems capable of understanding true customer behavior across all demographic segments.

First-party data collection through direct customer relationships, loyalty programs, and authenticated experiences becomes crucial. These voluntary data-sharing arrangements provide the clean, comprehensive datasets necessary for effective AI training while respecting user privacy preferences.

Organizations must also implement data quality monitoring systems that can identify and correct for adblocking-induced biases in their training datasets. This might involve demographic weighting, synthetic data generation, or hybrid approaches combining multiple data collection methodologies.

Conclusion

Adblocking's growth across Europe and North America represents more than revenue loss or measurement challenges—it's corrupting the data foundation that powers modern AI-driven marketing and customer experience optimization. With 57% of users blocking tracking scripts, organizations face a stark choice: evolve their data collection practices or watch their AI systems become increasingly ineffective at understanding and serving their most valuable customers.

The future belongs to businesses that can collect complete, unbiased data to train AI systems effectively. In an era where artificial intelligence drives competitive advantage, organizations cannot afford to train their models on fundamentally flawed, adblocking-corrupted datasets. The companies that solve this data quality challenge first will build AI systems that actually understand their customers—and will dominate markets where competitors remain blind to majority user behavior.

The adblocking revolution isn't just changing how we measure marketing performance—it's determining which organizations will have the data quality necessary to compete in an AI-driven future.