Exploring Causal Pathways Between Customer Engagement and Sales Outcomes Using Big Data Experiments
Keywords:
Customer Engagement, Sales Outcomes, Big Data Experiments, Causal Pathways, Engage-ment Analytics, AI-driven Marketing, Data Architecture, Business IntelligenceSynopsis
This research explores the causal pathways linking customer engagement and sales outcomes using big data-driven experimental methods. Leveraging engagement metrics such as interaction frequency, trust, satisfaction, and loyalty, the study examines their mediating role in influencing sales. By integrating deep learning-based personalization, experimental retail trials, and international case studies, this research highlights how big data experiments can validate engagement–sales causality beyond correlational analysis. Findings suggest that properly designed experimental architectures, including data lakes, AI-driven engagement analytics, and forecasting pipelines, significantly improve predictive accuracy and business outcomes. The paper contributes to marketing science by providing a causal evidence framework for understanding engagement–sales dynamics.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
