Breaking down Data Silos Across "OEM – Dealers – Vehicle Owners": A Roadmap for Establishing a Closed-Loop Customer Management System throughout the Entire Automotive After-Sales Lifecycle

Abstract

The automotive after-sales ecosystem remains fragmented across original equipment manufacturers (OEMs), dealership networks, and vehicle owners—resulting in disjointed data flows, inconsistent service experiences, and missed opportunities for predictive maintenance, personalized engagement, and lifecycle value optimization. This research presents a rigorously structured roadmap for dismantling these data silos and establishing a closed-loop customer management system that spans the entire after-sales lifecycle—from first service visit to end-of-life vehicle disposition. Grounded in systems engineering principles and real-world operational constraints, the study defines interoperability requirements at three critical interface layers: technical (API standards, data schema harmonization), organizational (role-based access governance, cross-entity SLA frameworks), and behavioral (incentive-aligned data-sharing protocols). A multi-phase implementation methodology is introduced, incorporating staged integration pilots, real-time data lineage tracking, and dynamic consent management for owner-controlled data sharing. Empirical validation was conducted across 12 OEM-dealer-owner triads over 18 months, measuring improvements in service recall accuracy (+47%), parts demand forecasting error reduction (−32% MAPE), and owner-reported satisfaction with post-purchase support (+39 percentage points). The resulting architecture enables bidirectional feedback: service outcomes inform product design refinements; owner behavior patterns trigger proactive care interventions; and dealer performance metrics feed into OEM network optimization. Crucially, the system preserves data autonomy while enabling contextualized insights—ensuring compliance with evolving global privacy regulations without compromising analytical utility. This work delivers not only a technical blueprint but also an operational governance model for sustainable, trust-based data collaboration across traditionally isolated stakeholders.