DIGITAL MARKETING INTELLIGENCE IN THE ENTERPRISE COMPETITIVE SECURITY SYSTEM

Abstract

The article develops a conceptual and methodological framework for integrating digital marketing intelligence into an enterprise competitive security system. The study addresses a managerial gap: firms accumulate large volumes of digital traces about customers, competitors, channels and platforms, yet these data are often used for campaign reporting rather than for early detection of competitive threats and coordinated response. The research applies a systems approach, a structured synthesis of peer-reviewed studies on competitive intelligence, digital marketing, marketing analytics, social media analytics and dynamic capabilities, comparative analysis, indicator normalization, additive aggregation and scenario modelling. Digital marketing intelligence is defined as a lawful, ethical and continuous process of collecting, validating, integrating, analysing and communicating digital market signals to identify competitive opportunities and threats and to support timely security decisions. A five-layer architecture is proposed: digital signal sources; data governance and quality control; analytical transformation; threat indicators and early-warning rules; decision, action and feedback. The framework distinguishes digital marketing intelligence from episodic marketing research, routine marketing analytics, business intelligence and broader competitive intelligence. Six indicator blocks are specified: competitors’ strategic activity; price and assortment pressure; digital visibility; customer and reputational vulnerability; channel and platform resilience; and intelligence-process effectiveness. Risk-oriented and protective indicators are normalized on a common scale; block risks are aggregated into an integral competitive-threat score, while a separate data-confidence coefficient prevents uncertain evidence from being interpreted as precise knowledge. A hypothetical calculation demonstrates how the model prioritizes platform dependence and intensified competitor activity and links them to named response owners and review deadlines. All numerical inputs in the demonstration are illustrative and do not represent a real enterprise. The scientific contribution is an operational link between marketing analytics and competitive-security management through the control chain “indicator – threshold – owner – action – feedback” and an explicit separation of threat level from evidence confidence. Practical implications include a phased implementation roadmap for enterprises with different levels of digital maturity. The framework requires sector-specific calibration, legal review of data collection practices and empirical validation on longitudinal enterprise data.