A digital strategy that drives growth does not rely on the accumulation of channels. It is based on the interplay between proprietary data, rapid execution capability, and budget allocation on a channel-by-channel basis. Over the past two years, we have observed an acceleration of testing cycles made possible by generative AI and no-code platforms, while the European regulatory framework is reshuffling the cards of acquisition. Three areas deserve in-depth technical analysis.
Multi-touch attribution model and budget allocation by digital channel
The choice of attribution model directly determines the distribution of your budget among channels. It is the parameter that weighs most heavily on the overall profitability of a digital strategy.
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A last-click attribution model systematically overestimates paid search and undervalues organic content or newsletters that come into play earlier in the customer journey. To make informed decisions, we recommend deploying a data-driven model in your analytics tool, or alternatively a U-shaped model (40% for the first contact, 40% for the last, 20% distributed across intermediate interactions).
This choice of model concretely changes your budget distribution. A channel that seems unprofitable in last-click may become your best lever for initiating journeys in multi-touch attribution. Without this perspective, you are optimizing blindly.
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Players like Be 2 Biz are effectively structuring the connection between companies and service providers capable of deploying these models, which shortens the sourcing phase for SMEs and mid-sized companies that do not have an internal data team.
Generative AI and testing cadence: concrete impact on digital marketing
Generative AI has changed the economics of marketing production. What interests us here is not the generation of raw text, but the measurable acceleration of A/B testing cadence.
The time to produce marketing content and the associated agency costs have significantly decreased since the massive adoption of these tools. The gain lies not in the unit quality of a piece of content, but in the ability to test more variations of landing pages, email hooks, and ad creatives within the same timeframe.
Where generative AI provides a real growth lever
The main lever lies in the personalization of customer journeys at scale. Three concrete applications stand out for their measurable return:
- Generation of segmented landing page variants by persona, with automatic performance scoring by cohort
- Dynamic personalization of email sequences based on browsing behavior (pages viewed, time spent, cart abandonment)
- Creation of ad variations tailored to each audience segment on social media, tested in parallel rather than sequentially
The key remains the testing protocol. Without a formalized hypothesis and sufficient traffic volume, multiplying variants only produces statistical noise. We recommend a minimum of two weeks per test and a significance threshold set before the launch, not after.
DMA and DSA: what the European regulatory framework changes for digital acquisition
The implementation of the Digital Markets Act and the Digital Services Act structurally changes the acquisition rules on major platforms. This topic is largely overlooked in digital strategy guides aimed at businesses, even though it has direct consequences on the visibility and profitability of campaigns.
Algorithmic transparency obligations
The DMA imposes transparency obligations on gatekeepers (notably Google, Meta, Amazon) regarding the functioning of their ranking and recommendation algorithms. For a company investing in SEO or advertising on these platforms, this means gradual access to more granular performance data.
Companies that diversify their acquisition channels now reduce their exposure to the risk of delisting. The DSA also opens new avenues for recourse in case of account blocking or content removal, which secures advertising investments.
Regulation of targeted advertising and proprietary data
The strengthened regulation of targeted advertising pushes advertisers to enhance their collection of first-party data. Companies with a qualified email database, a structured CRM, and proprietary audience segments are mechanically advantaged compared to those relying solely on third-party targeting.
In practice, this requires revisiting consent management on your digital properties (website, application), investing in compliant data collection tools, and building lookalike audiences from your own data rather than from the platforms.
No-code digital execution: reducing the gap between strategy and deployment
The gap between strategic decision-making and its implementation remains the main barrier to the digital growth of SMEs. No-code and low-code platforms significantly reduce this delay.
The rise of these tools, documented by Gartner and Forrester since 2022, particularly concerns marketing automation (building workflows, lead scoring, nurturing sequences) and the creation of conversion funnels without developer intervention.
- Automation of lead qualification workflows between the contact form and the CRM, with integrated behavioral scoring
- Building landing pages and multi-step forms without dependence on an agency or front-end developer
- Native connection between marketing tools, management tools, and advertising platforms via pre-configured API connectors
- Deployment of chatbots or conversational assistants for customer service, powered by the company’s knowledge base
The adoption of no-code does not eliminate the need for technical skills, but it shifts the bottleneck from production to design. The main risk remains technical debt: poorly integrated tool stacks that create data silos and complicate overall performance measurement.
A digital strategy that generates growth is driven by data, executed quickly, and adapts to ongoing regulatory constraints. Mastery of the attribution model, rigorous exploitation of generative AI, and anticipation of the effects of the DMA and DSA on the acquisition mix remain the three most underutilized technical levers.