As businesses and marketers adapt to a rapidly evolving omnichannel context, Customer Data Platforms (CDPs) appear as important technological tools to provide a unified view of customers by consolidating fragmented data across multiple touchpoints, enabling data-driven marketing strategies. This study reviews recent studies to investigate the role and relevance of CDPs in marketing, which is still an underrepresented topic in the literature. The research is based on an analysis of studies sourced from the Scopus database and selected according to PRISMA guidelines. The findings highlight how CDPs in marketing facilitate personalization, real-time engagement, and customer lifetime value optimization. This study aims to provide a foundation for future research on CDPs in marketing, which could investigate integrating other technologies, such as advanced artificial intelligence or machine learning, to improve predictive analytics and dynamic personalization, further optimizing customer engagement and marketing efficiency.

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The Role and Relevance of Customer Data Platforms in Marketing–A Systematic Literature Review

  • Miguel Cachulo Pereira,
  • Susana Marques

摘要

As businesses and marketers adapt to a rapidly evolving omnichannel context, Customer Data Platforms (CDPs) appear as important technological tools to provide a unified view of customers by consolidating fragmented data across multiple touchpoints, enabling data-driven marketing strategies. This study reviews recent studies to investigate the role and relevance of CDPs in marketing, which is still an underrepresented topic in the literature. The research is based on an analysis of studies sourced from the Scopus database and selected according to PRISMA guidelines. The findings highlight how CDPs in marketing facilitate personalization, real-time engagement, and customer lifetime value optimization. This study aims to provide a foundation for future research on CDPs in marketing, which could investigate integrating other technologies, such as advanced artificial intelligence or machine learning, to improve predictive analytics and dynamic personalization, further optimizing customer engagement and marketing efficiency.