Meta-Learning for Cold-Start Customer Segmentation in New Markets.

This paper investigates the role of meta-learning in addressing cold-start customer segmentation for new markets. We propose a meta-learning framework that leverages few-shot learning, domain adaptation, and dynamic feature fusion to rapidly tailor segmentation models when historical data from the target market are scarce. The approach combines Model-Agnostic Meta-Learning (MAML) with prototypical networks and clustering-aware representations to produce robust, interpretable segments with limited labeled data. We evaluate the framework on synthetic and real-world multi-market datasets simulating cold-start conditions, demonstrating improvements in segmentation accuracy, stability, and transferability compared to standard supervised learning and traditional domain adaptation baselines. A companion case study on a hypothetical retail expansion illustrates practical deployment considerations, including data privacy, measurement of business value, and governance of model updates. The findings suggest meta-learning can reduce time-to-insight and cost of market entry by providing actionable, data-efficient customer segments in new markets.