TITLE:
Selective Updating for Customer Segmentation under Concept Drift
AUTHORS:
Maria El Maaroud, Chi Zhang
KEYWORDS:
Customer Segmentation, Concept Drift, Model Replacement, BIRCH, MiniBatchKMeans
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.11,
November
11,
2026
ABSTRACT: Scheduled refitting can replace an effective customer segmentation even when a newly fitted model offers little improvement. We examine selective replacement for batchwise customer segmentation under concept drift, comparing BIRCH and MiniBatchKMeans under static, periodic, quality-only, and reward-based policies on Instacart, Ta-Feng, and Olist. The reward policy has the highest reported mean deployed silhouette in all six dataset-algorithm combinations; its advantage over quality-only is 0.0045 - 0.0170 silhouette points. Five supplied paired seed effects per combination are positive for reward versus each baseline, with seed-bootstrap intervals excluding zero. These same-snapshot comparisons do not establish temporally disjoint gains or isolate the reward’s stability term. The raw run record is not yet archived; label-aligned stability and denominator-aware event analyses are not reported.