Optimizing Scalable Targeted Marketing Policies with Constraints

References

• Allender WJ, Liaukonyte J, Nasser S, Richards TJ (2021) Price fairness and strategic obfuscation. Marketing Sci. 40(1):122–146.Link, Google Scholar
• Anderson ET, Simester DI (2008) Research note—Does demand fall when customers perceive that prices are unfair? The case of premium pricing for large sizes. Marketing Sci. 27(3):492–500.Link, Google Scholar
• Applegate D, Díaz M, Lu H, Lubin M (2024) Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming. SIAM J. Optim. 34(1):459–484.Crossref, Google Scholar
• Applegate D, Hinder O, Lu H, Lubin M (2023) Faster first-order primal-dual methods for linear programming using restarts and sharpness. Math. Program. 201(1–2):133–184.Crossref, Google Scholar
• Applegate D, Díaz M, Hinder O, Lu H, Lubin M, O’Donoghue B, Schudy W (2021) Practical large-scale linear programming using primal-dual hybrid gradient. Adv. Neural Inform. Processing Systems 34:20243–20257.Google Scholar
• Ascarza E, Israeli A (2022) Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT). Proc. Natl. Acad. Sci. USA 119(11):e2115293119.Crossref, Google Scholar
• Athey S, Wager S (2021) Policy learning with observational data. Econometrica 89(1):133–161.Crossref, Google Scholar
• Beck A (2017) First-Order Methods in Optimization (Society for Industrial and Applied Mathematics, Philadelphia).Crossref, Google Scholar
• Bertsimas D, Tsitsiklis J (2008) Introduction to Linear Optimization (Dynamic Ideas, Belmont, MA).Google Scholar
• Bolton LE, Keh HT, Alba JW (2010) How do price fairness perceptions differ across culture? J. Marketing Res. 47(3):564–576.Crossref, Google Scholar
• Bumbaca FR, Misra S, Rossi PE (2020) Scalable target marketing: Distributed Markov chain Monte Carlo for Bayesian hierarchical models. J. Marketing Res. 5(6):999–1018.Crossref, Google Scholar
• Campbell MC (2007) “Says who?!” How the source of price information and affect influence perceived price (un)fairness. J. Marketing Res. 27(3):261–271.Crossref, Google Scholar
• Castelnovo A, Crupi R, Greco G, Regoli D, Penco IG, Cosentini AC (2022) A clarification of the nuances in the fairness metrics landscape. Sci. Rep. 12(1):4209.Crossref, Google Scholar
• CFA (2018) Auto insurers often charge identical neighbors considerably higher premiums because of ZIP code differences. Accessed January 19, 2025, https://consumerfed.org/press_release/auto-insurers-often-charge-identical-neighbors-considerably-higher-premiums-because-of-zip-code-differences/.Google Scholar
• Chakrabarti R (2020) Introduction to heterogeneity series III: Credit market outcomes. Technical report, Federal Reserve Bank of New York, New York.Google Scholar
• Chambolle A, Pock T (2011) A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40:120–145.Crossref, Google Scholar
• Chambolle A, Pock T (2016) On the ergodic convergence rates of a first-order primal-dual algorithm. Math. Program. 159:253–287.Crossref, Google Scholar
• Chen X, Owen Z, Pixton C, Simchi-Levi D (2022) A statistical learning approach to personalization in revenue management. Management Sci. 68(3):1923–1937.Link, Google Scholar
• Chen X, Xu Z, Zhao Z, Zhou Y (2023) Personalized pricing with group fairness constraint. Proc. 2023 ACM Conf. Fairness Accountability Transparency (Association for Computing Machinery, New York), 1520–1530.Google Scholar
• Cui TH, Raju JS, Zhang ZJ (2007) Fairness and channel coordination. Management Sci. 53(8):1303–1314.Link, Google Scholar
• Dantzig GB (2016) Linear Programming and Extensions (Princeton University Press, Princeton, NJ).Google Scholar
• Derakhshan M, Golrezaei N, Leme RP (2022) Linear program-based approximation for personalized reserve prices. Management Sci. 68(3):1849–1864.Link, Google Scholar
• Desai P (2024) Drivers in less-white zip codes pay more for auto insurance. Technical report, MoneyGeek, Burlingame, CA.Google Scholar
• Dubé J-P, Misra S (2023) Personalized pricing and consumer welfare. J. Political Econom. 131(1):131–189.Crossref, Google Scholar
• Dudík M, Erhan D, Langford J, Li L (2014) Doubly robust policy evaluation and optimization. Statist. Sci. 29(4):485–511.Crossref, Google Scholar
• Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize.” Management Sci. 68(1):9–26.Link, Google Scholar
• Esser E, Zhang X, Chan T (2010) A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM J. Imaging Sci. 3(4):1015–1046.Crossref, Google Scholar
• Feldman J, Zhang DJ, Liu X, Zhang N (2022) Customer choice models vs. machine learning: Finding optimal product displays on Alibaba. Oper. Res. 70(1):309–328.Link, Google Scholar
• Fisher M, Vaidyanathan R (2014) A demand estimation procedure for retail assortment optimization with results from implementations. Management Sci. 60(10):2401–2415.Link, Google Scholar
• Fougner C, Boyd S (2018) Parameter selection and preconditioning for a graph form solver. Tempo R, Yurkovich S, Misra P, eds. Emerging Applications of Control and Systems Theory (Springer, Cham, Switzerland), 41–61.Crossref, Google Scholar
• Friedman SM (2021) ZIP code matters. Accessed March 12, 2025, https://www.youtube.com/watch?v=A6zDeOP1bPE.Google Scholar
• Fu R, Aseri M, Singh P, Srinivasan K (2022) “Un”fair machine learning algorithms. Management Sci. 68(6):4173–4195.Link, Google Scholar
• Gabel S, Timoshenko A (2022) Product choice with large assortments: A scalable deep-learning model. Management Sci. 68(3):1808–1827.Link, Google Scholar
• Gaskin DJ, Dinwiddie GY, Chan KS, McCleary RR (2012) Residential segregation and the availability of primary care physicians. Health Services Res. 47(6):2353–2376.Crossref, Google Scholar
• Golrezaei N, Nazerzadeh H, Rusmevichientong P (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.Link, Google Scholar
• Guo L (2015) Inequity aversion and fair selling. J. Marketing Res. 52(1):77–89.Crossref, Google Scholar
• Guo X, Jiang B (2016) Signaling through price and quality to consumers with fairness concerns. J. Marketing Res. 53(6):988–1000.Crossref, Google Scholar
• Haff JS, Moran K, Schmidt R (2011) Differential treatment of franchisees in tough economic times. Technical report, American Bar Association, Chicago.Google Scholar
• He B, Yuan X (2012) Convergence analysis of primal-dual algorithms for a saddle-point problem: From contraction perspective. SIAM J. Imaging Sci. 5(1):119–149.Crossref, Google Scholar
• Jain L, Li Z, Loghmani E, Mason B, Yoganarasimhan H (2024) Effective adaptive exploration of prices and promotions in choice-based demand models. Marketing Sci. 43(5):1002–1030.Link, Google Scholar
• Kanuri VK, Chen Y, Sridhar SH (2018) Scheduling content on social media: Theory, evidence, and application. J. Marketing 82(6):89–108.Crossref, Google Scholar
• Karmarkar N (1984) A new polynomial-time algorithm for linear programming. Proc. Sixteenth Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 302–311.Google Scholar
• Lambrecht A, Tucker C (2019) Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Sci. 65(7):2966–2981.Link, Google Scholar
• Lemmens A, Gupta S (2020) Managing churn to maximize profits. Marketing Sci. 39(5):956–973.Link, Google Scholar
• Li KJ, Jain S (2016) Behavior-based pricing: An analysis of the impact of peer-induced fairness. Management Sci. 62(9):2705–2721.Link, Google Scholar
• Luo L (2011) Product line design for consumer durables: An integrated marketing and engineering approach. J. Marketing Res. 48(1):128–139.Crossref, Google Scholar
• Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2022) A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6):115.Crossref, Google Scholar
• Monteiro RD, Adler I (1989) Interior path following primal-dual algorithms. Part I: Linear programming. Math. Program. 44(1–3):27–41.Crossref, Google Scholar
• Nesterov Y (2013) Gradient methods for minimizing composite functions. Math. Program. 140(1):125–161.Crossref, Google Scholar
• NFHA (2014) Zip code inequality: Discrimination by banks in the maintenance of homes in neighborhoods of color. Accessed January 19, 2025, https://nationalfairhousing.org/wp-content/uploads/2022/02/2014_08_27_NFHA_REO_report.pdf.Google Scholar
• O’Donoghue B, Chu E, Parikh N, Boyd S (2016) Conic optimization via operator splitting and homogeneous self-dual embedding. J. Optim. Theory Appl. 169(3):1042–1068.Crossref, Google Scholar
• Oyer P (1998) Fiscal year ends and nonlinear incentive contracts: The effect of business seasonality. Quart. J. Econom. 113(1):149–185.Crossref, Google Scholar
• Pessach D, Shmueli E (2023) A review on fairness in machine learning. ACM Comput. Surv. 55(3):51.Crossref, Google Scholar
• Pock D, Cremers D, Bischof H, Chambolle A (2009) An algorithm for minimizing the Mumford-Shah functional. IEEE 12th Internat. Conf. Comput. Vision (IEEE, Piscataway, NJ), 1133–1140.Google Scholar
• Rafieian O, Yoganarasimhan H (2021) Targeting and privacy in mobile advertising. Marketing Sci. 40(2):193–218.Link, Google Scholar
• Rafieian O, Yoganarasimhan H (2023) AI and personalization. Sudhir K, Toubia O, eds. Artificial Intelligence in Marketing, Review of Marketing Research, vol. 20 (Emerald Publishing Limited, Bingley, UK), 77–102.Crossref, Google Scholar
• Renegar J (1988) A polynomial-time algorithm, based on Newton’s method, for linear programming. Math. Program. 40(1–3):59–93.Crossref, Google Scholar
• Sahni NS, Zou D, Chintagunta PK (2017) Do targeted discount offers serve as advertising? Evidence from 70 field experiments. Management Sci. 63(8):2688–2705.Link, Google Scholar
• SBPC (2021) Mapping exploitation: Examining for-profit colleges as financial predators in communities of color. Accessed January 19, 2025, https://protectborrowers.org/wp-content/uploads/2021/07/SBPC-Mapping-Exploitation-Report.pdf.Google Scholar
• Simester D, Timoshenko A, Zoumpoulis SI (2020a) Efficiently evaluating targeting policies: Improving on champion vs. challenger experiments. Management Sci. 66(8):3412–3424.Link, Google Scholar
• Simester D, Timoshenko A, Zoumpoulis SI (2020b) Targeting prospective customers: Robustness of machine-learning methods to typical data challenges. Management Sci. 66(6):2495–2522.Link, Google Scholar
• Smith A (2022) Starbucks loses argument that unionization should be regional. Accessed January 19, 2025, https://www.shrm.org/topics-tools/employment-law-compliance/starbucks-loses-argument-unionization-regional.Google Scholar
• Swaminathan A, Joachims T (2015) Batch learning from logged bandit feedback through counterfactual risk minimization. J. Machine Learn. 16(1):1731–1755. Google Scholar
• Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41(1):116–131.Crossref, Google Scholar
• Wirtz J, Kimes SE (2007) The moderating role of familiarity in fairness perceptions of revenue management pricing. J. Service Res. 9(3):229–240.Crossref, Google Scholar
• Wright S (1997) Primal-Dual Interior-Point Methods (SIAM, Philadelphia).Crossref, Google Scholar
• Yang J, Eckles D, Dhillon P, Aral S (2024) Targeting for long-term outcomes. Management Sci. 70(6):3841–3855.Link, Google Scholar
• Yoganarasimhan H (2020) Search personalization using machine learning. Management Sci. 66(3):1045–1070.Link, Google Scholar
• Yoganarasimhan H, Barzegary E, Pani A (2023) Design and evaluation of optimal free trials. Management Sci. 69(6):3220–3240.Link, Google Scholar
• Zhang S, Mehta N, Singh PV, Srinivasan K (2021) Frontiers: Can an artificial intelligence algorithm mitigate racial economic inequality? An analysis in the context of Airbnb. Marketing Sci. 40(5):813–1007.Link, Google Scholar
• Zhu M, Chan T (2008) An efficient primal-dual hybrid gradient algorithm for total variation image restoration. UCLA Cam Rep. 34:8–34.Google Scholar