In many B2B acquisition and transaction workflows, companies still rely on static, fragmented marketing materials that are difficult to update consistently across channels, weakening narrative coherence and customer trust. To address this gap, this paper proposes an AI-enabled data-visualization marketing framework for automated production lines, built around a “single source of truth” content center (e.g., a CMS) that integrates production-line operational data and distributes consistent, evidence-based visual content to sales and marketing touchpoints via APIs. The approach embeds verifiable production evidence—such as yield stability, anomaly handling outcomes, delivery reliability, and batch traceability—into customer-facing materials to reduce information asymmetry and perceived supplier risk. Guided by Research Question 1 (RQ1), the study examines which categories of visual information most effectively strengthen customer trust, with a focus on quality stability trends, Pareto-style anomaly and corrective-action summaries, and traceability/compliance records—linking trust-building visual evidence to improved lead-to-order conversion performance in B2B manufacturing contexts.
Li Wensi (Fri,) studied this question.