Real-Time Context-Aware Reward Allocation in Mobile Loyalty Networks: Architecture, Budgeted Bandit Optimization, and Reproducible Evaluation
Year: 2026
Type: preprint
Abstract: Traditional customer engagement and loyalty plat-forms in enterprise retail depend on static spending tiers and offline, batch-processed transaction logs [4]. These systems miss critical windows of customer intent and suffer from margin ero-sion or user churn. This paper presents a real-time, closed-loop system architecture and dynamic optimization framework that allocates personalized incentives by coupling client-side spatial telemetry with server-side behavioral state engines. We model reward selection as a spatially constrained contextual multi-armed bandit problem, introducing an exponential distance-decay scoring heuristic and an off-peak temporal scaling factor to maximize conversion probability while adhering to promotional campaign budget limits [1], [8]. The proposed architecture processes client proximity breaches and transaction updates concurrently with sub-50ms processing latencies across all metric percentiles. Empirical validation across a synthetic workload of 100,000 mobile profiles and 1,000,000 interaction events demonstrates that our context-aware optimization achieves a 34.2% offer conversion rate and a revenue-to-cost ratio of $4.82 per promotional dollar spent, outperforming static tiering and heuristic geo-fencing baselines.
Authors: Alapati, Naresh, Koteswararao, Nallabothu
Institutions: Walmart (United States), Lowe's (United States)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7204268987