#segmentation

3 notes

• Customer Segmentation Customer Segmentation is the unsupervised task of partitioning customers into a finite set of segments by similarity in behavior, value, and preference. A common recipe is latent-factor decomposition followed by clustering: behavioral features → NMF (non-negative, parts-based decomposition) → factor scores → K-Means → segments.
• Customer Segmentation & Causal Targeting — An Applied Case Study An end-to-end applied case study on the public Dunnhumby dataset — NMF latent factors and K-Means segmentation feeding meta-learner / Causal Forest HTE and an OPE-validated optimal targeting policy, with a candid look at positivity violation and counter-intuitive "sleeping dog" segments.
• Dunnhumby — Track 1: Latent-Factor Customer Segmentation NMF latent factors (92.44% explained variance) + K-Means yield 7 stable behavioral segments (Bootstrap ARI 0.77) with per-segment marketing actions. Illustrative case study on the public Dunnhumby retail dataset.