Search arXiv⌕ Search

arXiv · 2610.34726

Designing Customer Feedback for Subscription Boxes

Abstract

Problem definition: Subscription box services often ask customers for ratings before choosing what to send. Responses are scarce, and longer questionnaires and finer scales lower completion. We study which candidate items a questionnaire should ask about, and in what format, when the platform then selects a fixed number of them. A design is judged by the expected value lost by not selecting the truly best items. Methodology/results: We approximate each ordinal rating by a Gaussian signal carrying its information at the cutoff between selected and unselected items. As equal numbers of responses per item grow, the ratio of this proxy's loss to the exact loss tends to one. With 1 to 100 responses per item, the two are within about 7 percent. Averaging the information over the prior instead can misstate the loss by about half. For an item far from the cutoff, the value of responses grows slowly at first and then faster, so our envelope rule values responses in blocks. In simulations with a correct prior it lowers the loss by 15 to 81 percent relative to an even spread, and on MovieLens ratings by 6 to 25 percent. Under the proxy, with responses allocated optimally, formats are ranked exactly by one number, the expected information per customer asked. Choosing the format this way comes within about 2 percent of the best on average. Managerial implications: Feedback should go to items whose place in the selection is unclear. Planning helps most when the platform's prior beliefs are informative and right. A platform unsure of its prior should widen it and send the questionnaire in waves. Calibrated on MovieLens ratings, a five-point answer carries about 1.7 times the information of a like/dislike answer, so at equal length the finer scale is better when it cuts completion by less than about 40 percent.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Renjun Hu, Hyun-Soo Ahn, Stefanus Jasin. 2026-10-26. Designing Customer Feedback for Subscription Boxes. https://arxiv.org/abs/2610.34726

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Principal Decomposition with Nested Submanifolds

Over the past decades, the increasing dimensionality of data has increased the need for effective data decomposition methods. Existing approaches, however, often rely on linear models or lack sufficient interpretability or flexibility. To address this issue, we introduce a novel nonlinear decomposition technique called the principal nested submanifolds, which builds on the foundational concepts of principal component analysis. This method exploits the local geometric information of data sets by projecting samples onto a series of nested principal submanifolds with progressively decreasing dimensions. It effectively isolates complex information within the data in a backward stepwise manner by targeting variations associated with smaller eigenvalues in local covariance matrices. The fitted structures are smooth nested manifolds rather than linear or special-shape subspaces. Simulation studies and an analysis of single-cell RNA-sequencing data illustrate how the method summarizes locally varying nonlinear structure while retaining a compatible hierarchy across dimensions. It provides flexible subspace constraints that facilitate the extraction of data components and noise reduction. This approach advances the non-Euclidean statistical analysis of data with low-dimensional intrinsic structure within Euclidean spaces and offers a complementary perspective for high-dimensional noisy data sets in fields such as bioinformatics and machine learning.

stat.ME↗

Two-sample comparison through additive tree models for density ratios

The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations from two distributions. To this end, we propose additive tree models for density ratio estimation along with efficient algorithms using a new loss function, the balancing loss. The loss allows tree-based models to be trained using several algorithms originally designed for supervised learning, such as forward-stagewise optimization and gradient boosting. Moreover, the balancing loss resembles an exponential family kernel, and it can serve as a pseudo-likelihood with conjugate priors. This property enables generalized Bayesian inference on the density ratio using backfitting samplers designed for Bayesian additive regression trees (BART). Our Bayesian strategy provides uncertainty quantification for the inferred density ratio, which is critical for applications involving high-dimensional and data-limited distributions with potentially substantial uncertainty. We further show connections of the balancing loss to the exponential loss in binary classification and to the variational form of f-divergence, particularly the squared Hellinger distance. Numerical experiments demonstrate that the proposed boosting algorithms achieve high accuracy at low computational cost, and that the Bayesian additive tree is competitive in accuracy while additionally providing uncertainty quantification, which the compared estimators do not offer. Finally, we demonstrate its application to assessing the quality of generative models for microbiome compositional data.

stat.ME↗

Learning Sequential Decisions from Multiple Sources via Group-Robust Markov Decision Processes

Learning a common policy from multiple source environments requires accounting for cross-source heterogeneity, and the offline setting introduces an additional challenge of limited data coverage. We formulate this problem as a distributionally robust Markov decision process under a shared linear MDP representation with source-specific transition and reward components. The uncertainty set is constructed through feature-wise mixtures of these components, yielding a $d$-rectangular structure that captures cross-source heterogeneity and admits a robust Bellman recursion. Building on this formulation, we develop Group-Robust Pessimistic Value Iteration (GR-PEVI), an offline policy-learning algorithm that combines feature-wise worst-case aggregation with a data-dependent pessimism penalty. Under a robust partial coverage condition, we establish finite-sample suboptimality guarantees for GR-PEVI and extend the analysis to clustered sources, characterizing the trade-off between the statistical gain from pooling similar sources and the approximation error induced by within-cluster heterogeneity. Simulation studies demonstrate the effectiveness of GR-PEVI, with empirical behavior consistent with the theoretical analysis. We apply GR-PEVI to a multi-source electronic health record study of multiple sclerosis treatment decisions to illustrate its use in a clinical setting.

stat.ME↗