Meta-Learning for Cold-Start Customer Segmentation in New Markets.
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| Natura: | Recurso digital |
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2025
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| author | Dr. Siddharth Prabhakar Sorate Dr. Tanaji Dinkar Dabade |
| author_facet | Dr. Siddharth Prabhakar Sorate Dr. Tanaji Dinkar Dabade |
| contents | <p><strong><span lang="EN-US">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.</span></strong></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17208179 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Meta-Learning for Cold-Start Customer Segmentation in New Markets. Dr. Siddharth Prabhakar Sorate Dr. Tanaji Dinkar Dabade Meta learning, cold start segmentation, digital marketing, multimarket datasets etc. <p><strong><span lang="EN-US">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.</span></strong></p> |
| title | Meta-Learning for Cold-Start Customer Segmentation in New Markets. |
| topic | Meta learning, cold start segmentation, digital marketing, multimarket datasets etc. |
| url | https://doi.org/10.5281/zenodo.17208179 |