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<p>Organizations face the dual challenge of measuring data-driven marketing while adhering to stringent privacy standards and preparing for artificial intelligence. This research introduces MWIN (Measure What Is Needed), a strategic framework that aligns business objectives with KPI definitions and embeds privacy-by-design at the definition layer. Developed through a hybrid Action Research and Design Science program, the framework was evaluated using expert reviews and quantitative indicators against hypotheses covering strategic alignment, privacy compliance, AI-readiness, data quality, and decision-making. Findings indicate that MWIN strengthens KPI traceability, integrates consent controls without eroding analytical utility, and supports higher semantic maturity, better data quality, and more routine evidence-based decisions. The study contributes a mixed-methods template for evaluating socio-technical frameworks and offers practitioners a reproducible path to minimize risk and ready data for advanced analytics. Future research should extend to longitudinal testing and AI fairness profiling to ensure ethical activation at scale.</p>

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MWIN (Measure What Is Needed): a strategic framework for privacy-first, AI-ready, and data-driven marketing analytics

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Ignacio Gorostiza-Esquerdeiro,
  • David Buján-Carballal,
  • Aitor Oyarbide-Zubillaga

摘要

Organizations face the dual challenge of measuring data-driven marketing while adhering to stringent privacy standards and preparing for artificial intelligence. This research introduces MWIN (Measure What Is Needed), a strategic framework that aligns business objectives with KPI definitions and embeds privacy-by-design at the definition layer. Developed through a hybrid Action Research and Design Science program, the framework was evaluated using expert reviews and quantitative indicators against hypotheses covering strategic alignment, privacy compliance, AI-readiness, data quality, and decision-making. Findings indicate that MWIN strengthens KPI traceability, integrates consent controls without eroding analytical utility, and supports higher semantic maturity, better data quality, and more routine evidence-based decisions. The study contributes a mixed-methods template for evaluating socio-technical frameworks and offers practitioners a reproducible path to minimize risk and ready data for advanced analytics. Future research should extend to longitudinal testing and AI fairness profiling to ensure ethical activation at scale.