Frontiers in Service Science: Data-Driven Revenue Management: The Interplay of Data, Model, and Decisions
Published Online:10 Mar 2023https://doi.org/10.1287/serv.2023.0322
References
- (2021) Demand estimation under the multinomial logit model from sales transaction data. Manufacturing Service Oper. Management 23(5):1196–1216.Link, Google Scholar
- (2017) Thompson sampling for the MNL-bandit. Kale S, Shamir O, eds. Proc. 2017 Conf. Learn. Theory, Proceedings of Machine Learning Research Series, vol. 65 (PMLR), 76–78.Google Scholar
- (2019) MNL-bandit: A dynamic learning approach to assortment selection. Oper. Res. 67(5):1453–1485.Link, Google Scholar
- (2022) Pricing for heterogeneous products: Analytics for ticket reselling. Manufacturing Service Oper. Management Forthcoming.Link, Google Scholar
- (2022) Pricing with samples. Oper. Res. 70(2):1088–1104.Link, Google Scholar
- (2016) The exponomial choice model: A new alternative for assortment and price optimization. Oper. Res. 64(1):79–93.Link, Google Scholar
- (2021) Heteroscedastic exponomial choice. Oper. Res. 69(3):841–858.Link, Google Scholar
- (2014) Repeated contextual auctions with strategic buyers. Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 27 (Curran Associates, Inc., Red Hook, NY), 622–630. https://proceedings.neurips.cc/paper/2014/file/66368270ffd51418ec58bd793f2d9b1b-Paper.pdf.Google Scholar
- (2017) Censored demand estimation in retail. Proc. ACM Measurement Anal. Comput. Systems vol. 1, 1–28.Google Scholar
- (1998) Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Sci. 17(4):406–423.Link, Google Scholar
- (2022) Representing random utility choice models with neural networks. Preprint, submitted July 26, https://arxiv.org/abs/2207.12877.Google Scholar
- (2018) The exponomial choice model: Algorithmic frameworks for assortment optimization and data-driven estimation case studies. Preprint, submitted June 21, https://dx.doi.org/10.2139/ssrn.3192068.Google Scholar
- (2022) Market segmentation trees. Manufacturing Service Oper. Management Forthcoming.Google Scholar
- (2022) Data-driven sports ticket pricing for multiple sales channels with heterogeneous customers. Manufacturing Service Oper. Management 24(2):1241–1260.Link, Google Scholar
- (2014) A taxonomy of demand uncensoring methods in revenue management. J. Revenue Pricing Management 13(6):440–456.Crossref, Google Scholar
- (2015) A non-parametric approach to demand forecasting in revenue management. Comput. Oper. Res. 63:23–31.Crossref, Google Scholar
- (2020) Detecting customer trends for optimal promotion targeting. Manufacturing Service Oper. Management 25(2):448–467.Link, Google Scholar
- (2018) Are two (samples) really better than one? Proc. 2018 ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 175.Google Scholar
- (2013) Bandits with knapsacks. 2013 IEEE 54th Annual Sympos. Foundations Comput. Sci. (IEEE), 207–216.Google Scholar
- (2019) Learning in repeated auctions with budgets: Regret minimization and equilibrium. Management Sci. 65(9):3952–3968.Link, Google Scholar
- (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.Link, Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2019) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management 21(4):798–815.Link, Google Scholar
- (2022) Pricing and optimization in shared vehicle systems: An approximation framework. Oper. Res. 70(3):1783–1805.Link, Google Scholar
- (2022) Meta dynamic pricing: Transfer learning across experiments. Management Sci. 68(3):1865–1881.Link, Google Scholar
- (2022) Pricing in on-demand (and one-way) vehicle sharing networks. Preprint, submitted January 5, https://dx.doi.org/10.2139/ssrn.3998297.Google Scholar
- (2023) Online learning for pricing in on-demand vehicle sharing networks. Preprint, submitted February 1, https://dx.doi.org/10.2139/ssrn.4344364.Google Scholar
- (2022) A comparative empirical study of discrete choice models in retail operations. Management Sci. 68(6):4005–4023.Link, Google Scholar
- (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.Abstract, Google Scholar
- (2004) Data, Models, and Decisions: The Fundamentals of Management Science (Dynamic Ideas, Waltham, MA).Google Scholar
- (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
- (2023) The power and limits of predictive approaches to observational data-driven optimization: The case of pricing. INFORMS J. Optim. Forthcoming.Link, Google Scholar
- (2012) Blind network revenue management. Oper. Res. 60(6):1537–1550.Link, Google Scholar
- (2020) Pricing analytics for rotable spare parts. INFORMS J. Appl. Anal. 50(5):313–324.Link, Google Scholar
- (2020) Near-optimal A-B testing. Management Sci. 66(10):4477–4495.Link, Google Scholar
- (2022) Convex loss functions for contextual pricing with observational posted-price data. Preprint, submitted February 16, https://arxiv.org/abs/2202.10944.Google Scholar
- (2021a) Loss functions for discrete contextual pricing with observational data. Preprint, submitted November 18, https://arxiv.org/abs/2111.09933.Google Scholar
- (2021b) Model distillation for revenue optimization: Interpretable personalized pricing. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn. Proceedings of Machine Learning Research Series, vol. 139 (PMLR), 946–956.Google Scholar
- (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.Link, Google Scholar
- (2020) Estimating and optimizing the impact of inventory on consumer choices in a fashion retail setting. Manufacturing Service Oper. Management 22(3):582–597.Link, Google Scholar
- (2023) Design and analysis of switchback experiments. Management Sci. Forthcoming.Link, Google Scholar
- (2020) Online pricing with offline data: Phase transition and inverse square law. Daumé III H, Aarti S, eds. Proc. 37th Internat. Conf. Machine Learn. Proceedings of Machine Learning Research Series, vol. 119 (PMLR), 1202–1210.Google Scholar
- (2022) Deep learning for choice modeling. Preprint, submitted August 19, https://arxiv.org/abs/2208.09325.Google Scholar
- (2019) Dynamic learning of sequential choice bandit problem under marketing fatigue. Proc. Conf. AAAI Artificial Intelligence (AAAI Press, Palo Alto, CA), 33:3264–3271.Google Scholar
- (2010) Inventory management of a fast-fashion retail network. Oper. Res. 58(2):257–273.Link, Google Scholar
- (2012) Clearance pricing optimization for a fast-fashion retailer. Oper. Res. 60(6):1404–1422.Link, Google Scholar
- (2021) Nonparametric pricing analytics with customer covariates. Oper. Res. 69(3):974–984.Link, Google Scholar
- (2022) A primal-dual learning algorithm for personalized dynamic pricing with an inventory constraint. Math. Oper. Res. 47(4):2585–2613.Link, Google Scholar
- (2022a) Learning consumer preferences from bundle sales data. Preprint, submitted September 11, https://arxiv.org/abs/2209.04942.Google Scholar
- (2019c) The use of binary choice forests to model and estimate discrete choices. Preprint, submitted August 3, 2019, https://arxiv.org/abs/1908.01109.Google Scholar
- (2021a) Revenue maximization and learning in products ranking. Proc. 22nd ACM Conf. Econom. Comput., 316–317.Google Scholar
- (2020) Learning and optimization with seasonal patterns. Preprint, submitted May 16, https://arxiv.org/abs/2005.08088.Google Scholar
- (2023) Model-free assortment pricing with transaction data. Management Sci. Forthcoming.Link, Google Scholar
- (2019a) Nonparametric self-adjusting control for joint learning and optimization of multiproduct pricing with finite resource capacity. Math. Oper. Res. 44(2):601–631.Link, Google Scholar
- Chen X, Jasin S, Shi C, eds. (2022b) The Elements of Joint Learning and Optimization in Operations Management, 1st ed., Springer Series in Supply Chain Management, vol. 18 (Springer International Publishing).Crossref, Google Scholar
- (2022c) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.Link, Google Scholar
- (2020c) Dynamic assortment optimization with changing contextual information. J. Machine Learn. Res. 21(1):8918–8961.Google Scholar
- (2022d) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Link, Google Scholar
- (2021b) Dynamic assortment planning under nested logit models. Production Oper. Management 30(1):85–102.Crossref, Google Scholar
- (2023) Network revenue management with online inverse batch gradient descent method. Production Oper. Management Forthcoming.Crossref, Google Scholar
- (2019b) Dynamic pricing in an evolving and unknown marketplace. Preprint, submitted June 6, https://dx.doi.org/10.2139/ssrn.3382957.Google Scholar
- (2022) Decision forest: A nonparametric approach to modeling irrational choice. Management Sci. 68(10):7090–7111.Link, Google Scholar
- (2017) Thompson sampling for online personalized assortment optimization problems with multinomial logit choice models. Preprint, submitted November 27, https://dx.doi.org/10.2139/ssrn.3075658.Google Scholar
- (2023) Estimating personalized demand with unobserved no-purchases using a mixture model: An application in the hotel industry. Manufacturing Service Oper. Management Forthcoming.Google Scholar
- (2020) Feature-based dynamic pricing. Management Sci. 66(11):4921–4943.Link, Google Scholar
- (2017) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.Link, Google Scholar
- (2014) The sample complexity of revenue maximization. Proc. 46th Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 243–252.Google Scholar
- (2008) An experimental comparison of click position-bias models. Proc. 2008 Internat. Conf. Web Search Data Mining, (Association for Computing Machinery, New York), 87–94.Google Scholar
- (2020) More revenue from two samples via factor revealing SDPs. Proc. 21st ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 257–272.Google Scholar
- (2017) Design and Analysis of Experiments. Springer Texts in Statistics (Springer International Publishing).Crossref, Google Scholar
- (1977) Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39(1):1–22.Crossref, Google Scholar
- (2015) Dynamic pricing and learning: Historical origins, current research, and new directions. Surveys Oper. Res. Management Sci. 20(1):1–18.Crossref, Google Scholar
- (2020) Discontinuous demand functions: Estimation and pricing. Management Sci. 66(10):4516–4534.Link, Google Scholar
- (2022) Dynamic pricing with demand learning and reference effects. Management Sci. 68(10):7112–7130.Link, Google Scholar
- (2022) Linear program-based approximation for personalized reserve prices. Management Sci. 68(3):1849–1864.Link, Google Scholar
- (2020) Multinomial logit bandit with low switching cost. Daumé H III, Singh A, eds. Proc. 37th Internat. Conf. Machine Learn., vol. 119 (PMLR), 2607–2615.Google Scholar
- (2017) Task-based end-to-end model learning in stochastic optimization. Proc. 31st Internat. Conf. Neural Inform. Processing Systems, vol. 30 (NIPS) (Curran Associates Inc., Red Hook, NY), 5490–5500.Google Scholar
- (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
- (2018) Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations. Math. Programming 171(1):115–166.Crossref, Google Scholar
- (2020) Inferring sparse preference lists from partial information. Stochastic Systems 10(4):335–360.Link, Google Scholar
- (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.Link, Google Scholar
- (2022) Markovian interference in experiments. Preprint, submitted June 6, https://arxiv.org/abs/2206.02371.Google Scholar
- (2022) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Oper. Res. 70(1):309–328.Link, Google Scholar
- (2022) Robust learning of consumer preferences. Oper. Res. 70(2):918–962.Link, Google Scholar
- (2016) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
- (2022) Learning to rank an assortment of products. Management Sci. 68(3):1828–1848.Link, Google Scholar
- (2018) Online network revenue management using Thompson sampling. Oper. Res. 66(6):1586–1602.Link, Google Scholar
- (2018) Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Sci. 64(6):2496–2514.Link, Google Scholar
- (2015) Randomization beats second price as a prior-independent auction. Proc. 16th ACM Conf. Econom. Comput. (Association for Computing Machinery, New York), 323.Google Scholar
- (1997) A multiproduct dynamic pricing problem and its applications to network yield management. Oper. Res. 45(1):24–41.Link, Google Scholar
- (2015) Initial shipment decisions for new products at Zara. Oper. Res. 63(2):269–286.Link, Google Scholar
- (2022) Joint learning and optimization for multi-product pricing (and ranking) under a general cascade click model. Management Sci. 68(10):7362–7382.Link, Google Scholar
- (2021) Dynamic incentive-aware learning: Robust pricing in contextual auctions. Oper. Res. 69(1):297–314.Link, Google Scholar
- (2023) Learning product rankings robust to fake users. Oper. Res. Forthcoming.Google Scholar
- (2017) Parameter identification in Markov chain choice models. Hanneke S, Reyzin L, eds. Pro. 28th Internat. Conf. Algorithmic Learn. Theory Proc. Machine Learn. Res., vol. 76 (PMLR), 330–340.Google Scholar
- (2021) Frontiers: Algorithmic collusion: Supra-competitive prices via independent algorithms. Marketing Sci. 40(1):1–12.Link, Google Scholar
- (2021) Dynamic data-driven estimation of nonparametric choice models. Oper. Res. 69(4):1228–1239.Link, Google Scholar
- (2018) Making the most of your samples. SIAM J. Comput. 47(3):651–674.Crossref, Google Scholar
- (2017) A nonparametric joint assortment and price choice model. Management Sci. 63(9):3128–3145.Link, Google Scholar
- (2018) A partial-order-based model to estimate individual preferences using panel data. Management Sci. 64(4):1609–1628.Link, Google Scholar
- (2019) Dynamic pricing in high dimensions. J. Machine Learn. Res. 20(1):315–363.Google Scholar
- (2003) Measuring heterogeneous reservation prices for product bundles. Marketing Sci. 22(1):107–130.Link, Google Scholar
- (2020) A partially ranked choice model for large-scale data-driven assortment optimization. INFORMS J. Optim. 2(4):297–319.Link, Google Scholar
- (2023) Online learning and pricing for service systems with reusable resources. Oper. Res. Forthcoming.Google Scholar
- (2022) Experimental design in two-sided platforms: An analysis of bias. Management Sci. 68(10):7069–7089.Link, Google Scholar
- (2023) Stochastic optimization forests. Management Sci. Forthcoming.Link, Google Scholar
- (2020) Dynamic assortment personalization in high dimensions. Oper. Res. 68(4):1020–1037.Link, Google Scholar
- (2021) Incentive-compatible learning of reserve prices for repeated auctions. Oper. Res. 69(2):509–524.Link, Google Scholar
- (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.Crossref, Google Scholar
- (2007) Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6):1001–1021.Link, Google Scholar
- (2020) Bandit Algorithms (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2014) Latent variable copula inference for bundle pricing from retail transaction data. Xing EP, Jebara T, eds. Proc. 31st Internat. Conf. Machine Learn. Proceedings of Machine Learning Research Series, vol. 32 (PMLR), 217–225.Google Scholar
- (2020) Estimating demand with unobserved no-purchases on revenue-managed data. Preprint, submitted March 7, https://dx.doi.org/10.2139/ssrn.3525773.Google Scholar
- (2022) Constructing demand curves from a single observation of bundle sales. Web Internet Econom. 18th Internat. Conf., (Springer-Verlag, Berlin, Heidelberg), 150–166.Google Scholar
- (2022) Learning to collude in a pricing duopoly. Manufacturing Service Oper. Management 24(5):2577–2594.Link, Google Scholar
- (2021) Dynamic joint assortment and pricing optimization with demand learning. Manufacturing Service Oper. Management 23(2):525–545.Google Scholar
- (2021) Network revenue management with nonparametric demand learning: T-regret and polynomial dimension dependency. Preprint, submitted October 25, https://dx.doi.org/10.2139/ssrn.3948140.Google Scholar
- (2022) Context-based dynamic pricing with online clustering. Production Oper. Management 31(9):3559–3575.Crossref, Google Scholar
- (2014) On theoretical and empirical aspects of marginal distribution choice models. Management Sci. 60(6):1511–1531.Link, Google Scholar
- (2010) Structural estimation of the effect of out-of-stocks. Management Sci. 56(7):1180–1197.Link, Google Scholar
- (2019) Dynamic learning and pricing with model misspecification. Management Sci. 65(11):4980–5000.Link, Google Scholar
- (2009) Persistency model and its applications in choice modeling. Management Sci. 55(3):453–469.Link, Google Scholar
- (2014) Estimation of choice-based models using sales data from a single firm. Manufacturing Service Oper. Management 16(2):184–197.Link, Google Scholar
- (2019) Thompson sampling for multinomial logit contextual bandits. Wallach H, Larochelle H, Beygelzimer A, d’Alché Buc F, Fox E, Garnett R, eds. Proc. 33rd Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY).Google Scholar
- (2016) Dynamic pricing with demand covariates. Preprint, submitted April 25, https://arxiv.org/abs/1604.07463.Google Scholar
- (2016) Pairwise choice Markov chains. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Proc. 30th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Inc., Red Hook, NY), 3206–3214.Google Scholar
- (2008) A multi-flight recapture heuristic for estimating unconstrained demand from airline bookings. J. Revenue Pricing Management 7(2):153–171.Crossref, Google Scholar
- (2010) Dynamic assortment optimization with a multinomial logit choice model and capacity constraint. Oper. Res. 58(6):1666–1680.Link, Google Scholar
- (2013) Optimal dynamic assortment planning with demand learning. Manufacturing Service Oper. Management 15(3):387–404.Link, Google Scholar
- (2018) Dynamic pricing under competition on online marketplaces: A data-driven approach. Proc. 24th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 705–714.Google Scholar
- (2019) Semi-parametric dynamic contextual pricing. Wallach H, Larochelle H, Beygelzimer A, d’Alché Buc F, Fox E, Garnett R, eds. Adv. Neural Inform. Processing Systems, vol. 32 (Curran Associates, Inc.).Google Scholar
- (2014) OM forum—OM research: From problem-driven to data-driven research. Manufacturing Service Oper. Management 16(1):2–10.Link, Google Scholar
- (2022) Online learning and optimization for revenue management problems with add-on discounts. Management Sci. 68(10):7402–7421.Link, Google Scholar
- (2018) An expectation-maximization algorithm to estimate the parameters of the Markov chain choice model. Oper. Res. 66(3):748–760.Link, Google Scholar
- (2021) Demand modeling in the presence of unobserved lost sales. Management Sci. 67(6):3803–3833.Link, Google Scholar
- (2020) Differentially private contextual dynamic pricing. Proc. 19th Internat. Conf. Autonomous Agents MultiAgent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC), 1368–1376.Google Scholar
- (2009) Discrete Choice Methods with Simulation, 2nd ed. (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
- (2012) Learning consumer tastes through dynamic assortments. Oper. Res. 60(4):833–849.Link, Google Scholar
- (2015) A market discovery algorithm to estimate a general class of nonparametric choice models. Management Sci. 61(2):281–300.Link, Google Scholar
- (2017) An expectation-maximization method to estimate a rank-based choice model of demand. Oper. Res. 65(2):396–407.Link, Google Scholar
- (2010) OM practice—Choice-based revenue management: An empirical study of estimation and optimization. Manufacturing Service Oper. Management 12(3):371–392.Link, Google Scholar
- (2012) Estimating primary demand for substitutable products from sales transaction data. Oper. Res. 60(2):313–334.Link, Google Scholar
- (2018) Demand estimation under multi-store multi-product substitution in high density traditional retail. Eur. J. Oper. Res. 266(1):99–111.Crossref, Google Scholar
- (2022) An instrumental variable forest approach for detecting heterogeneous treatment effects in observational studies. Management Sci. 68(5):3399–3418.Link, Google Scholar
- (2021) On dynamic pricing with covariates. Preprint, submitted December 25, https://arxiv.org/abs/2112.13254.Google Scholar
- (2021) Technical note—Consumer choice and market expansion: Modeling, optimization, and estimation. Oper. Res. 69(4):1044–1056.Link, Google Scholar
- (2019) Designing and evaluating dynamic pricing policies for major league baseball tickets. Manufacturing Service Oper. Management 21(1):121–138.Link, Google Scholar
- (2023) Cold start to improve market thickness on online advertising platforms: Data-driven algorithms and field experiments. Management Sci. Forthcoming.Google Scholar
- (2022) Pigeonhole design: Balancing sequential experiments from an online matching perspective. Preprint, submitted January 30, https://arxiv.org/abs/2201.12936.Google Scholar
- (2020) When demands evolve larger and noisier: Learning and earning in a growing environment. Proc. 37th Internat. Conf. Machine Learn. Proceedings of Machine Learning Research Series, vol. 119 (PMLR), 11629–11638.Google Scholar

