Design and Evaluation of Optimal Free Trials

References

• Anderson ET, Simester DI (2004) Long-run effects of promotion depth on new vs. established customers: Three field studies. Marketing Sci. 23(1):4–20.Link, Google Scholar
• Athey S, Wager S (2020) Efficient policy learning. Preprint, submitted September 4, https://arxiv.org/abs/1702.02896.Google Scholar
• Bruhn M, McKenzie D (2009) In pursuit of balance: Randomization in practice in development field experiments. Amer. Econom. J. Appl. Econom. 1(4):200–232.Crossref, Google Scholar
• Cheng HK, Liu Y (2012) Optimal software free trial strategy: The impact of network externalities and consumer uncertainty. Inform. Systems Res. 23(2):488–504.Link, Google Scholar
• Dey D, Lahiri A, Liu D (2013) Consumer learning and time-locked trials of software products. J. Management Inform. Systems 30(2):239–268.Crossref, Google Scholar
• Dudík M, Langford J, Li L (2011) Doubly robust policy evaluation and learning. Proc. 28th Internat. Conf. on Machine Learn. (Omnipress), 1097–1104.Google Scholar
• Fader PS, Hardie BG (2009) Probability models for customer-base analysis. J. Interactive Marketing 23(1):61–69.Crossref, Google Scholar
• Fong N, Zhang Y, Luo X, Wang X (2019) Targeted promotions on an e-book platform: Crowding out, heterogeneity, and opportunity costs. J. Marketing Res. 56(2):310–323.Crossref, Google Scholar
• Foubert B, Gijsbrechts E (2016) Try it, you’ll like it—or will you? The perils of early free-trial promotions for high-tech service adoption. Marketing Sci. 35(5):810–826.Link, Google Scholar
• Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J. Statist. Software 33(1):1.Crossref, Google Scholar
• Gartner (2019) Forecast: Public cloud services, worldwide, 2016-2022, 4q18 update. Accessed August 1, 2022, https://www.gartner.com/en/newsroom/press-releases/2019-04-02-gartner-forecasts-worldwide-public-cloud-revenue-to-g.Google Scholar
• Guo T, Sriram S, Manchanda P (2021) The effect of information disclosure on industry payments to physicians. J. Marketing Res. 58(1):115–140.Google Scholar
• Gupta S, Hanssens D, Hardie B, Kahn W, Kumar V, Lin N, Ravishanker N, Sriram S (2006) Modeling customer lifetime value. J. Serv. Res. 9(2):139–155.Crossref, Google Scholar
• Hauser JR, Urban GL, Liberali G, Braun M (2009) Website morphing. Marketing Sci. 28(2):202–223.Link, Google Scholar
• Hitsch GJ, Misra S (2018) Heterogeneous treatment effects and optimal targeting policy evaluation. Preprint, submitted February 6, https://dx.doi.org/10.2139/ssrn.3111957.Google Scholar
• Horvitz DG, Thompson DJ (1952) A generalization of sampling without replacement from a finite universe. J. Amer. Statist. Assoc. 47(260):663–685.Crossref, Google Scholar
• Imbens GW, Rubin DB (2015) Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, Cambridge, UK).Crossref, Google Scholar
• Kitagawa T, Tetenov A (2018) Who should be treated? Empirical welfare maximization methods for treatment choice. Econometrica 86(2):591–616.Crossref, Google Scholar
• Lewis RA, Rao JM (2015) The unfavorable economics of measuring the returns to advertising. Quart. J. Econom. 130(4):1941–1973.Crossref, Google Scholar
• Manski CF (2004) Statistical treatment rules for heterogeneous populations. Econometrica 72(4):1221–1246.Crossref, Google Scholar
• McCarthy DM, Fader PS, Hardie BG (2017) Valuing subscription-based businesses using publicly disclosed customer data. J. Marketing 81(1):17–35.Crossref, Google Scholar
• McKenzie D (2017) Should we require balance t-tests of baseline observables in randomized experiments? Accessed August 1, 2022, https://blogs.worldbank.org/impactevaluations/should-we-require-balance-t-tests-baseline-observables-randomized-experiments.Google Scholar
• Mela CF, Gupta S, Lehmann DR (1997) The long-term impact of promotion and advertising on consumer brand choice. J. Marketing Res. 34(2):248–261.Crossref, Google Scholar
• Mutz DC, Pemantle R, Pham P (2019) The perils of balance testing in experimental design: Messy analyses of clean data. Amer. Statist. 73(1):32–42.Crossref, Google Scholar
• Pauwels K, Hanssens DM, Siddarth S (2002) The long-term effects of price promotions on category incidence, brand choice, and purchase quantity. J. Marketing Res. 39(4):421–439.Crossref, Google Scholar
• Prentice RL (1989) Surrogate endpoints in clinical trials: Definition and operational criteria. Statist. Medicine 8(4):431–440.Crossref, Google Scholar
• Rafieian O (2019a) Optimizing user engagement through adaptive ad sequencing. Technical report, Cornell Tech, New York.Google Scholar
• Rafieian O (2019b) Revenue-optimal dynamic auctions for adaptive ad sequencing. Technical report, Cornell Tech, New York.Google Scholar
• Rafieian O, Yoganarasimhan H (2021) Targeting and privacy in mobile advertising. Marketing Sci. 40(2):193–218.Link, Google Scholar
• Schwartz EM, Bradlow ET, Fader PS (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.Link, Google Scholar
• Scott CA (1976) The effects of trial and incentives on repeat purchase behavior. J. Marketing Res. 13(3):263–269.Crossref, Google Scholar
• Simester D, Timoshenko A, Zoumpoulis SI (2020a) Efficiently evaluating targeting policies: Improving on champion vs. challengerexperiments. Management Sci. 66(8):3412–3424.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.Google Scholar
• Sunada T (2018) Customer learning and revenue-maximizing trial design. Technical report, University of Rochester, Rochester, NY.Google Scholar
• Swaminathan A, Joachims T (2015) Counterfactual risk minimization: Learning from logged bandit feedback. Bach F, Blei D, eds. Proc. Internat. Conf. Machine Learn., vol. 37, 814–823.Google Scholar
• Swaminathan A, Krishnamurthy A, Agarwal A, Dudik M, Langford J, Jose D, Zitouni I (2017) Off-policy evaluation for slate recommendation. Advances in Neural Information Processing Systems, 3632–3642.Google Scholar
• Tibshirani R (1996) Regression shrinkage and selection via the lasso. J. Royal Statist. Soc. B 58(1):267–288.Crossref, Google Scholar
• VanderWeele TJ (2013) Surrogate measures and consistent surrogates. Biometrics 69(3):561–565.Crossref, Google Scholar
• Wang S, Özkan-Seely GF (2018) Signaling product quality through a trial period. Oper. Res. 66(2):301–312.Link, Google Scholar
• Yang J, Eckles D, Dhillon P, Aral S (2022) Targeting for long-term outcomes. Preprint, submitted April 9, https://arxiv.org/abs/2010.15835.Google Scholar
• Yoganarasimhan H (2020) Search personalization using machine learning. Management Sci. 66(3):1045–1070.Link, Google Scholar
• Zhu M, Yang Y, Hsee CK (2018) The mere urgency effect. J. Consumer Res. 45(3):673–690.Google Scholar