For retail marketers, especially those leading Retail Media Networks (RMNs), the implications of Mark Zuckerberg’s prediction that the age of traditional Segmentation, Targeting, and Positioning (STP) marketing may soon be over are profound. In a recent interview with Stratechery and in commentary covered by The Verge, Meta’s CEO described a near future in which AI systems autonomously manage the entire advertising process, eliminating the need for human-led traditional segmentation, creative development or measurement.

“You don’t need any creative, you don’t need any targeting demographic, you don’t need any measurement, except to be able to read the results that we spit out,” Zuckerberg asserted. If AI can outperform traditional segmentation and targeting, is STP still relevant? Or are we standing at the threshold of a retail marketing reset, driven by hyper-personalization, predictive behavior models, and AI-curated discovery?

Retailers are uniquely positioned to develop DToCs because of their direct access to rich, omnichannel first-party data. By offering advertisers access to ethically built, privacy-compliant DToCs, RMNs can deliver hyper-personalized targeting with greater transparency and brand safety compared to black-box big tech models.

The AI Model

Demographic traits are becoming secondary indicators; behavioral signals are the new marketing unlock. At the heart of Zuckerberg’s vision is a decisive shift away from demographic targeting, age, gender, income and location, toward dynamic behavior-based targeting. AI models no longer rely on advertisers specifying segments like “women 18-34 interested in fitness.” Instead, the platform detects conversion-ready consumers based on browsing patterns, purchase history, and real-time engagement signals.

As detailed by The Verge, Zuckerberg’s concept of “infinite creative” envisions AI continuously generating and testing new ad variations, optimizing on the fly. Advertising becomes a machine-led outcome engine: input objectives, connect your budget, and AI does the rest.

Retail Media Networks: Evolve or Risk Obsolescence

Retailers have built multi-billion-dollar RMNs. Amazon Ads, Walmart Connect, Target Roundel and others sell curated audience segments to brands. But if AI platforms deliver superior real-time targeting without rigid segments, RMNs face some existential questions:

• Can they pivot from static audience segmentation to real-time predictive modeling?
• Can they match the promise of AI-driven, outcome-based personalization?
• Can they provide dynamic creative optimization at scale?

On the plus side, retailers possess a strategic advantage: first-party transaction data. By investing in AI engines that harness this data for predictive, behavior-based marketing, RMNs can remain relevant, and even lead the next evolution of personalized retail media through technologies like Digital Twins of the Customer (DToC).

Leveraging DToC: The Next Frontier

To future-proof their value propositions, Retail Media Networks must move beyond static segmentation and even traditional first-party data modeling toward building DToCs. A DToC is a real-time digital replica of a customer, integrating their purchase history, browsing behavior, loyalty activity, context signals, and predictive intent patterns into a continuously evolving profile. Unlike static demographics, DToCs offer dynamic, individual-level personalization that evolves as the customer’s behavior changes.

Retailers are uniquely positioned to develop DToCs because of their direct access to rich, omnichannel first-party data. By offering advertisers access to ethically built, privacy-compliant DToCs, RMNs can deliver hyper-personalized targeting with greater transparency and brand safety compared to black-box big tech models.

Moreover, as consumer discovery shifts toward AI-driven platforms, DToC architectures allow retailers to serve predictive, contextually relevant offers across search, social, voice, and conversational commerce channels. Investing in DToC infrastructure may enable RMNs not only to match the AI-powered hyper-targeting capabilities of platforms, but to surpass them by offering brands deeper insights, greater accountability, and measurable incremental value.

The Rise of LEO

A second marketing revolution is also underway: the migration from SEO (Search Engine Optimization) to LEO (LLM Engine Optimization). As Business Insider reports, consumers increasingly discover brands via AI-powered assistants like ChatGPT, Gemini, and Perplexity. Brands must now optimize for conversational AI discovery, not just keyword search rankings. In a LEO-driven world:

• Brands must create structured, rich contextual content that answers user queries
• Product data must be optimized for ingestion by AI discovery systems
• The top-of-funnel is increasingly happening inside AI agents, not Google

Retailers and RMNs must ensure that their product catalogs and brand storytelling are discoverable in AI-generated answers, or risk losing early-stage consumer attention.

Hyper-Personalization: Brand Promise or Peril?

Zuckerberg’s AI-driven vision promises:

• Infinite personalized ads
• Predictive product recommendations
• Frictionless, individualized shopping journeys

However, as CMSWire and others note, hyper-personalization carries risks. Infinite creativity without human oversight could fracture brand identity, leading to disjointed customer experiences. As The Verge warns, automating the entire creative and targeting process risks eroding the core human connection between brands and consumers. Moreover, Inc. reports that overly personalized advertising often leads to consumer backlash rather than loyalty. To succeed, brands must build brand-tuned AI models: proprietary LLMs fine-tuned to their voice, values, and customer standards.

The Strategic Playbook for Retailers

Leading brands and RMNs must adapt across several dimensions:

• AI Driven Personalization with Guardrails: Use first-party data to enable personalization, but preserve brand identity with proprietary AI models.
• Optimize for LEO: Structure content and metadata for AI assistant discovery.
• Omnichannel AI Advertising: Connect personalized experiences across all digital and in-store touchpoints.
• Monitor Brand Trust: Balance personalization with authenticity; measure effectiveness to ensure relevance, not intrusion.

Traditional STP frameworks are being rewritten. Segmentation is shifting to real-time behavioral modeling and targeting is becoming AI-optimized. Positioning is more vital than ever to differentiate, and discovery is moving from Google to generative AI. Retailers and brands that adapt, leveraging AI’s personalization potential without sacrificing brand coherence, will thrive in this new ecosystem. Meta may be leading the end of STP as we knew it. But for those ready to evolve, AI isn’t the end of marketing, it’s the beginning of a smarter, faster, more dynamic future.