price elasticity estimation

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.

This study addresses a central assumption in decarbonization policy—that electricity demand has become more responsive to price signals due to advances in smart metering, automation, and energy storage—yet empirical evidence remains scarce. Leveraging 4,720 own-price elasticity estimates from 462 studies spanning 1934 to 2024, the authors construct the first large-scale meta-analytic dataset covering nine decades. Employing identification-quality grading and publication-bias correction methods, they systematically assess temporal trends in price elasticity. Findings reveal a mean short-run elasticity of –0.16, which declines to –0.09 (statistically insignificant) under high-identification designs; long-run elasticity averages –0.38 but shows no upward trend over time. Notably, demand responsiveness is weaker—not stronger—in high-technology contexts, challenging prevailing policy expectations.

This study addresses the challenge of accurately estimating demand functions for new products based on consumers’ willingness-to-pay (WTP). It proposes a novel paradigm that directly identifies demand functions from WTP data by constructing a general yet analytically tractable parametric demand model and designing a consistent estimation procedure. Theoretical analysis and Monte Carlo simulations demonstrate that the proposed method effectively recovers the true underlying demand function and exhibits robust performance across a variety of model specifications. Combining theoretical rigor with practical applicability, this approach offers a versatile tool for academic research, business decision-making, and policy evaluation.

This paper addresses the challenge of accurately identifying demand under substantial temporal fluctuations and absent cost-variation information, focusing on the French railway industry. We systematically evaluate the economic performance of revenue management (RM) strategies using a novel identification framework that integrates time-series relative price changes, consumer rational expectations, and firms’ weak optimality conditions in pricing. Our methodology combines structural econometric modeling, counterfactual demand estimation, endogenous price treatment, and censoring-handling techniques to overcome identification issues arising from sales cutoffs and the lack of exogenous price variation. Results show that current RM practices significantly outperform uniform pricing but still incur a 16.7% revenue loss relative to theoretically optimal dynamic pricing. This study provides the first empirical quantification of RM’s net economic value in a real-world industrial setting and reveals its critical role in aggregating and processing information under demand uncertainty.

This work proposes CLVTools, an open-source R package for customer lifetime value (CLV) modeling that addresses key challenges such as sparse transaction data and prediction horizons exceeding the observation window. Built upon probabilistic generative models—including Pareto/NBD and Gamma-Gamma—the toolkit integrates maximum likelihood estimation with Bayesian inference, and supports both time-invariant and time-varying covariates, parameter regularization, and equality constraints. Designed for robustness and computational efficiency, CLVTools delivers accurate individual-level CLV predictions even with limited data, while maintaining scalability to large datasets. By enhancing both predictive precision and data frugality, the package offers a practical and extensible solution for marketing decision-making.

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 study addresses the limitations of existing quantile-based approaches for evaluating battery arbitrage, which fail to accurately capture the economic value of probabilistic forecasting models and neglect temporal dependencies in electricity prices as well as incentive compatibility. To overcome these issues, the authors propose a stochastic programming framework that leverages full predictive distributions, jointly optimizing day-ahead price forecasts with energy storage decisions. This enables a systematic assessment of how forecast quality influences decision performance under varying risk preferences. Empirical analysis using German electricity market data demonstrates that conventional quantile-based strategies can mislead model ranking, whereas the proposed full-probability approach provides a more reliable measure of a forecasting model’s economic value, thereby establishing a new paradigm for application-oriented forecast evaluation.

This work addresses the challenges of optimizing demand response at the distribution level and the financial risks faced by residential consumers under extreme weather events and volatile electricity prices. To this end, we propose DR-Gym—the first market-scale, online demand response simulation environment tailored for utility operators. Built upon the Gymnasium framework, DR-Gym integrates a state-transition electricity pricing model calibrated with real-world extreme-event data and a physics-informed building load model, while supporting configurable multi-objective reward structures. Experimental results demonstrate that DR-Gym generates realistic and learnable dynamic interaction scenarios, effectively enabling the training and evaluation of reinforcement learning–based demand response strategies and providing a reliable platform for algorithmic development in this domain.

This work proposes a dynamic pricing framework that operates without assuming a parametric form of the demand function, under the challenging setting where only single-point revenue observations are available and market conditions evolve non-stationarily. The approach constructs a nonparametric gradient estimator from single-point revenue feedback to iteratively update prices and incorporates a restart mechanism to handle abrupt environmental shifts. When the degree of non-stationarity is unknown, a meta-learning layer is introduced to adaptively combine multiple restart strategies. Theoretical analysis establishes an upper bound on the cumulative revenue regret, and extensive experiments on both synthetic and real-world data demonstrate the method’s effectiveness and robustness in non-stationary markets. This study represents the first integration of nonparametric learning, single-point feedback-based gradient estimation, and adaptive restarting, achieving provably sound performance guarantees.

Traditional economic valuation models struggle to apply to internal enterprise data exchange scenarios lacking market price mechanisms. This work proposes a normative data valuation approach grounded in user choice behavior, formalizing the value of data products as a cooperative game. By modeling user attention and preferences, the method derives a closed-form Shapley value that enables fair valuation without requiring information on costs, demand, or competitive prices. It overcomes the limitations of conventional popularity-based metrics by emphasizing the uniqueness and discriminative consumption of data, thereby effectively incentivizing the creation of high-value long-tail data products. The framework also provides a theoretical foundation for assessing the total value of internal data outputs, designing bundling strategies, and studying complementarities among data assets.