Digital marketing is no longer measured solely by the number of clicks or positioning in traditional search results. With the integration of generative AI in search engines and the tightening of European regulations on targeted advertising, marketing strategies that worked two years ago are losing effectiveness. Which levers still produce measurable results, and which now fall into the background noise?
GEO and Traditional SEO: Two Visibility Logics to Balance
The rise of AI-generated responses in Google (AI Overviews), ChatGPT, or Perplexity creates a clear distinction between two disciplines. Traditional SEO seeks to gain clicks through good rankings in search results. GEO aims to be cited in an AI-generated response, which changes the very nature of the content to be produced.
| Criterion | Traditional SEO | GEO (Generative Optimization) |
|---|---|---|
| Objective | Clicks to the site | Citation in the AI response |
| Type of Content Favored | Keyword-optimized pages, internal linking | Evergreen, well-argued, structured, and expert content |
| Trust Signal | Backlinks, domain authority | Depth of argumentation, factual evidence |
| Update Cycle | Regular (Google algorithm) | Less frequent, but requires constant reliability |
Content that performs in this generative environment is described as content that AI “has no choice but to consider reliable and recommend.” In other words, a superficial article optimized for a keyword is no longer sufficient: depth of expertise becomes the dominant signal.
Resources like Le Blog du Marketing document this transition between traditional visibility and presence in AI responses, a balancing act that every company must now integrate into its content strategy.
European Advertising Regulation: What the DMA and DSA Change for Targeting
Most articles on marketing strategies overlook a factor that is changing the rules of the game: the Digital Markets Act and the Digital Services Act now regulate targeted advertising on major platforms. These two European regulations impose transparency obligations and limit certain profiling practices.
The DSA prohibits advertising targeting based on sensitive categories (political opinions, health data, sexual orientation). The DMA, on its part, prevents “gatekeepers” (Meta, Google, Amazon, Apple, among others) from cross-referencing personal data collected across their various services without explicit consent.
Consequences for Advertising Campaigns
- Audiences built on the cross-referencing of multi-service data (for example, Instagram data combined with WhatsApp) become inaccessible without clear opt-in, reducing the granularity of available targeting
- Advertisers must rethink their campaigns by relying more on first-party data (collected directly through their site or CRM) rather than third-party profiling
- Transparency in advertising bids becomes mandatory, allowing advertisers to better understand why an ad was shown, but also requiring documentation of each targeting criterion used
For companies that invest heavily in advertising on social media, the shift to first-party data is no longer optional. It is a regulatory constraint, not a trend.
Content Strategy and Generative AI: The Trap of Mass Production
Generative AI facilitates large-scale content production. Many companies see it as an opportunity to publish more, faster, and at a lower cost. Available data suggests that this quantitative approach produces the opposite effects of those sought.
Content produced en masse by AI without editorial oversight loses credibility with audiences and algorithms. Several recent analyses point to a saturation of channels with generic content, prompting platforms to value signals of authenticity and human expertise.
Brand Voice and Authenticity: A Technical Balancing Act
The question of “brand voice” (the creative signature of a brand in its communications) becomes a measurable differentiation issue. When all competitors use the same tools to produce similar texts, distinctive brand voice becomes the only recognition criterion.
This does not mean rejecting generative AI. Rather, the balancing act lies between fully delegating production to AI and assisted production where humans retain editorial control. B2B companies that document their sector expertise with well-argued content authored by identified writers achieve better engagement results than those that publish anonymous and generic content.
Hyper-Personalization of Customer Journeys: First-Party Data and B2B Marketing
Hyper-personalization relies on the ability to tailor each touchpoint to the customer’s profile and behavior. With the restriction on third-party targeting imposed by the DMA and GDPR, this personalization can no longer rely on data purchased from brokers.
In B2B, companies that invest in collecting first-party data (qualified forms, CRM interactions, browsing history on their own site) have a structural advantage. Personalization based on proprietary data withstands regulatory changes, unlike strategies dependent on third-party cookies or external advertising profiles.
Conversely, companies that have not yet structured their collection of direct data find themselves with increasingly imprecise campaigns, with no possibility of compensating through third-party algorithmic targeting.
The most underestimated lever remains the quality of the data collected. A form that asks for five relevant fields produces more actionable segments than a tracking pixel that records thousands of anonymous visits. The most profitable marketing strategy in 2024 is not the one that adopts the latest trendy tool, but the one that builds a reliable and usable proprietary database over time.