estimate stock lifetime value

This study addresses the limitation of conventional e-commerce A/B tests, which often overlook the long-term impact of interventions on profitability across an inventory item’s full lifecycle due to short experimental windows. To overcome this, the authors propose Stock Lifetime Value (SLV), a novel metric that aggregates the expected profit of current inventory over its entire sales horizon within short-term experiments, thereby enabling more accurate assessment of long-term profitability. SLV uniquely integrates inventory constraints and seasonal lifecycle dynamics into the A/B testing framework, combining causal inference with financial mapping to support both item-level and user-level experimentation while aligning with annual financial reporting. Empirical validation at Zalando demonstrates that SLV effectively predicts actual profits over an 18-month horizon, enhances pricing algorithm performance, and delivers interpretable estimates of annual financial impact.

The newsvendor problem faces challenges in dynamic inventory forecasting due to scarce historical data and unknown demand distributions. Method: This paper proposes a distribution-free stochastic modeling framework that bypasses prior distributional assumptions. Leveraging stochastic forecasting analysis, it directly learns the evolution dynamics of inventory states from limited time-series inventory and sales data, enabling dynamic probabilistic characterization of inventory levels. Contribution/Results: Unlike conventional approaches relying on strong parametric assumptions (e.g., normal or Poisson demand), our method establishes a data-driven, distribution-agnostic dynamic modeling paradigm. Experiments on real-world e-marketplace data demonstrate that the model significantly outperforms classical distribution-based methods in short-term forecasting—achieving superior accuracy, robustness, and practical deployability. It provides an interpretable, probability-based solution for inventory decision-making under small-sample regimes.

Large-scale manufacturers face challenges in after-sales demand forecasting, including difficulty in fusing heterogeneous multi-source signals, weak modeling of COVID-19 disruptions, imbalanced prediction accuracy for long-tail versus high-revenue items, and insufficient business interpretability. Method: We propose an end-to-end interpretable ensemble forecasting framework integrating statistical models, deep learning, and large language models (LLMs). Key innovations include Pareto-aware segmented forecasting, horizon-aware weighted ensemble integration, LLM-driven automated attribution narrative generation, plus integrated change-point detection and WMAPE-optimized calibration. Contribution/Results: The system delivers city-item-level calibrated probabilistic forecasts across 90+ countries and 6,000 SKUs, simultaneously improving prediction accuracy, stability, and operational alignment. Through a performance scorecard and trend attribution module, it shifts evaluation from static accuracy metrics to a dynamic, intervention-oriented decision loop.

Traditional revenue forecasting approaches struggle to uncover the underlying customer behavioral drivers—such as customer acquisition, repeat purchase rates, and average transaction value—that influence revenue dynamics. To address this limitation, this work proposes the Customer-Based Multi-Task Transformer (CBMT), which uniquely integrates multi-task learning with a Transformer architecture to jointly model customer behavioral metrics and total revenue through shared representations. Furthermore, CBMT incorporates a downstream alignment mechanism to enhance both interpretability and predictive accuracy. Empirical evaluation on real-world customer transaction panel data demonstrates that CBMT outperforms existing methods across 23 out of 24 evaluation metrics, achieving a 30% reduction in total sales prediction error compared to the strongest baseline and significantly surpassing single-task models employed by 74.3% of firms.

Traditional quantitative investment systems typically optimize a single metric—such as the information ratio—and thus struggle to meet professional investors’ multifaceted objectives, including pure alpha generation, style control, drawdown resilience, and turnover and capacity constraints. This work proposes an Objective-Oriented Quantitative Investment (OOQI) framework that formally encodes investment intent as strategy specifications and compiles them into composable, constraint-satisfying strategy assemblies. Key innovations include establishing a dual lattice structure between specifications and assemblies, designing a satisfaction-driven synthesis mechanism, and introducing rolling recertification via e-process-based validation. Empirical results demonstrate that the specification-driven approach satisfies 100% of target constraints across 32 strategies, at the cost of only a 5.5% reduction in information ratio, whereas conventional outcome-oriented methods—despite higher in-sample information ratios—fulfill merely 25% of the specified requirements.

In non-contractual settings, customer churn is unobservable, rendering accurate counts of active customers challenging. This study identifies a category error in the conventional P(alive) metric, which conflates finite-horizon, verifiable repurchase probabilities with infinite-horizon extrapolations of customer survival. To address this, we propose counting customers based on auditable, finite-horizon repurchase probabilities and develop an interval estimation framework using the beta-geometric family of models, replacing prevailing point estimation approaches. Empirical analysis leveraging a seven-year panel dataset of 31,683 customers reveals that alternative model specifications can yield customer counts differing by up to 7.6-fold, while default parameter choices introduce biases as high as 42%. The proposed method substantially enhances both predictive accuracy and verifiability.

This study addresses the challenges of extreme demand volatility, delayed pricing responses, and misalignment between short-term revenue and long-term profitability during major fashion e-commerce promotions. To tackle these issues, the authors propose a high-frequency “predict–optimize” automated pricing system that breaks away from traditional weekly decision cycles by operating at a minute-level granularity. The system achieves the first industrial-scale deployment of daily multi-objective dynamic pricing in large-scale e-commerce settings, combining gradient-boosted tree models for daily demand forecasting with a multi-objective optimization framework to generate real-time pricing strategies that jointly maximize long-term profit and net merchandise value. Evaluated across 23 A/B tests in 12 Zalando markets from 2023 to 2024, the system delivered approximately 6% higher profit while maintaining sales volume and has since been fully deployed for promotional pricing.

This work addresses the challenge that existing calibration tests for conditional quantile predictors struggle to handle distributional shifts and discrepancies in information sets, lacking feature-aware, continuous monitoring capabilities. The authors propose a distribution-free, game-theoretic sequential auditing framework that formally defines conditional quantile calibration under varying feature information sets—a notion not previously established—and provides finite-time detection guarantees without requiring independent and identically distributed data. By integrating contextual linear betting strategies with nonparametric e-processes, the method enables interpretable, feature-level calibration audits. Empirical evaluations demonstrate that the framework effectively detects significant miscalibration in state-of-the-art time series models, such as Chronos-2, across critical features.

This study addresses a critical limitation in conventional ensemble methods for probabilistic forecasting, which focus solely on statistical accuracy while neglecting their downstream impact on multi-objective inventory management decisions—often resulting in high forecast accuracy without commensurate operational gains. To bridge this gap, the paper formulates probabilistic forecast combination as a multi-objective optimization problem that jointly optimizes forecast accuracy and nonlinear, conflicting inventory decision objectives. By employing multi-objective optimization algorithms, the approach generates a Pareto-optimal set of solutions, thereby explicitly aligning prediction with decision-making. Empirical evaluations on Walmart retail data and UK Royal Air Force spare parts demonstrate that the proposed method achieves a more robust trade-off between predictive accuracy and inventory performance compared to single models, simple averaging, and single-objective optimization baselines.