Contrary to the trend of tightening data privacy, the European Commission has proposed a package to soften GDPR and cookie rules. This could lead to fewer consent banners for "low risk" data collection, signaling a potential shift towards more practical and less burdensome privacy regulations for businesses.
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To succeed, marketers must stop passively accepting the data they're given. Instead, they must proactively partner with IT and privacy teams to advocate for the specific data collection and governance required to power their growth and personalization initiatives.
Recent antitrust lawsuits against Meta and Google resulted in minimal consequences ("nothing burgers"), signaling a more permissive regulatory environment. Combined with anticipated economic stimulus, this creates ideal conditions for a wave of large-scale M&A ($25B-$250B) among major tech companies in the coming year.
Companies often focus on avoiding fines by being overly cautious with data, a practice called "under-permissioning." This creates a huge opportunity cost by shrinking the marketable audience and leading to wasted ad spend on generalized campaigns.
Cookie deprecation blinds ad platforms like Google and Meta to on-site conversion quality. Marketers can gain a significant performance edge by creating a feedback loop, pushing their attributed first-party data (like lifetime value and margins) back into the platforms' AI systems in near real-time.
Due to signal loss from cookie deprecation, no single model like MTA or MMM is sufficient. The new gold standard is using all available algorithms together in a machine learning framework, allowing them to influence each other for a more accurate ROI picture.
As AI personalization grows, user consent will evolve beyond cookies. A key future control will be the "do not train" option, letting users opt out of their data being used to train AI models, presenting a new technical and ethical challenge for brands.
While fast-moving, unregulated competitors like FTX garner hype, a deliberate, compliance-first approach builds a more resilient and defensible business in sectors like finance. This unsexy path is the key to building a lasting, mainstream company with a strong regulatory moat.
Digital trust with partners requires embedding privacy considerations into their entire lifecycle, from onboarding to system access. This proactive approach builds confidence and prevents data breaches within the extended enterprise, rather than treating privacy as a reactive compliance task.
When developing AI for sensitive industries like government, anticipate that some customers will be skeptical. Design AI features with clear, non-AI alternatives. This allows you to sell to both "AI excited" and "AI skeptical" jurisdictions, ensuring wider market penetration.
To earn consumer data, brands must offer a clear value exchange beyond vague promises of "better experiences." The most compelling benefits are tangible utilities like time savings and seamless cross-device continuity, which are often undervalued by marketers.