Firms increasingly collect high-volume customer and interaction data, yet many still struggle to convert those data into deployable engagement decisions and measurable business gains. This paper develops a design-science artifact that links Big Data analytics capability (BDAC), marketing analytics execution, engagement design, governance safeguards, and business performance, and then demonstrates one operational slice of that artifact using the UCI Bank Marketing dataset (N = 45,211). The empirical component is intentionally bounded to leakage-controlled response prediction, scenario analysis, and exploratory customer typology rather than full causal testing of the conceptual model. The results show that deployable targeting can materially improve conversion efficiency under fixed outreach capacity and that segment-level differences in prior contact history and campaign intensity justify differentiated playbooks such as prioritization, frequency capping, and channel switching. The contribution is, therefore, practical and integrative: it clarifies how prediction, segmentation, and governance-ready controls can be assembled into an actionable analytics workflow. The study does not claim causal validation of the full framework; instead, it offers a design-oriented demonstration and a future research agenda for experimental, survey-based, and longitudinal testing.