Intelligent Analysis Methods for Multi-Channel Marketing Data Based on Anomaly Detection Algorithms

Abstract

Multi-channel marketing environments generate massive volumes of heterogeneous data across digital platforms, social media, e-commerce channels, and traditional advertising mediums. The complexity and velocity of these data streams present significant challenges for marketing professionals seeking to extract actionable insights for strategic decision-making. This research proposes an integrated framework for intelligent analysis of multi-channel marketing data through advanced anomaly detection algorithms. The methodology combines machine learning techniques with statistical approaches to identify irregular patterns, emerging trends, and potential threats within marketing datasets. Our framework incorporates data preprocessing pipelines, algorithm optimization strategies, and real-time monitoring capabilities designed specifically for marketing intelligence applications. Experimental validation demonstrates superior performance across multiple industry sectors, with anomaly detection accuracy rates exceeding 94.2% and false positive rates maintained below 3.8%. The proposed system successfully identifies critical marketing anomalies including fraudulent activities, campaign performance deviations, and consumer behavior shifts across diverse data sources. Implementation results reveal significant improvements in marketing ROI optimization, with participating organizations reporting average performance gains of 23.7% in campaign effectiveness and 18.9% reduction in marketing waste through early anomaly identification and mitigation strategies.