#targeting

9 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.
• Dunnhumby — Track 2: Causal Targeting via Heterogeneous Treatment Effects Meta-learner / Causal Forest CATE under severe positivity violation (PS AUC 0.989); an OPE-validated policy targets ~31% of customers and surfaces counter-intuitive negative-CATE segments. Hypothesis-generating on public data.
• Optimal Targeting Policy An Optimal Targeting Policy maps covariates $x$ to a treatment decision $\pi(x)\in\{0,1\}$ so as to maximize policy value:
• RTB Bidding Strategy via Causal ML — From Prediction to Optimization A five-stage case study on the public iPinYou RTB dataset that moves from pCTR/pCVR prediction through causal effect estimation (CATE, SCM) to budget-constrained optimal bidding and off-policy policy evaluation.
• Targeting & Profiling Overview Targeting & Profiling is the industrial face of personalization. The same methodological core (heterogeneous effect estimation → individual-level optimal policy; MOC-Personalization) appears in clinical settings as "optimal treatment assignment per patient," and in industry as "optimal campaign/exposure assignment per customer." This domain answers who…
• Uplift Modeling Uplift is the causal increment that a treatment (campaign exposure, coupon, recommendation) induces in an individual's outcome (purchase, conversion). For binary treatment $W\in\{0,1\}$, outcome $Y$, and covariates $X$.
• User Profiling User Profiling is the task of inferring a personal preference profile (taste, context, latent patterns) from a customer's behavioral history and representing it as a vector. It is the shared input layer for targeting, segmentation, and recommendation — the industry-side counterpart to patient covariate/multimodal representations (Multimodal Clinical Data) in the clinical domain.