Ancillary Services in Targeted Advertising: From Prediction to Prescription

Published Online:https://doi.org/10.1287/msom.2020.0491

References

  • Agarwal D, Agrawal R, Khanna R, Kota N (2010) Estimating rates of rare events with multiple hierarchies through scalable log-linear models. Proc. 16th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 213–222.Google Scholar
  • Aouad A, Elmachtoub AN, Ferreira KJ, McNellis R (2019) Market segmentation trees. Preprint, submitted June 4, https://arxiv.org/abs/1906.01174.Google Scholar
  • Ascarza E (2018) Retention futility: Targeting high-risk customers might be ineffective. J. Marketing Res. 55(1):80–98.CrossrefGoogle Scholar
  • Athey S (2019) The impact of machine learning on economics. The Economics of Artificial Intelligence (University of Chicago Press, Chicago), 507–552.CrossrefGoogle Scholar
  • Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc. Natl. Acad. Sci. USA 113(27):7353–7360.CrossrefGoogle Scholar
  • Baardman L, Levin I, Perakis G, Singhvi D (2017) Leveraging comparables for new product sales forecasting. Preprint, submitted December 14, https://dx.doi.org/10.2139/ssrn.3086237.Google Scholar
  • Baardman L, Boroujeni SB, Cohen-Hillel T, Panchamgam K, Perakis G (2018) Detecting customer trends for optimal promotion targeting. Preprint, submitted September 12, https://dx.doi.org/10.2139/ssrn.3242529.Google Scholar
  • Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.CrossrefGoogle Scholar
  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and Regression Trees (Chapman and Hall, Wadsworth, NY).Google Scholar
  • Chapelle O, Manavoglu E, Rosales R (2014) Simple and scalable response prediction for display advertising. ACM Trans. Intelligent Systems Tech. 5(4):1–34.CrossrefGoogle Scholar
  • Chatterjee P, Hoffman DL, Novak TP (2003) Modeling the clickstream: Implications for web-based advertising efforts. Marketing Sci. 22(4):520–541.LinkGoogle Scholar
  • Chellappa RK, Kumar KR (2005) Examining the role of “free” product-augmenting online services in pricing and customer retention strategies. J. Management Inform. Systems 22(1):355–377.CrossrefGoogle Scholar
  • Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J (2017) Double/debiased machine learning for treatment and causal parameters (No. 1608.00060).Google Scholar
  • Choi JA, Lim K (2020) Identifying machine learning techniques for classification of target advertising. ICT Express 6(3):175–180.CrossrefGoogle Scholar
  • Cook DI, Gebski VJ, Keech AC (2004) Subgroup analysis in clinical trials. Medical J. Australia 180(6):289–291.CrossrefGoogle Scholar
  • Cramer-Flood E (2022) Global ecommerce forecast 2022. Insider Intelligence. Accessed May 31, 2023, https://www.emarketer.com/content/global-ecommerce-forecast-2022.Google Scholar
  • Davis J, Gallego G, Topaloglu H (2013) Assortment planning under the multinomial logit model with totally unimodular constraint structures. Working paper, Cornell University, Ithaca, NY.Google Scholar
  • De Reyck B, Fragkos I, Grushka-Cockayne Y, Lichtendahl C, Guerin H, Kritzer A (2017) Vungle Inc. improves monetization using big data analytics. INFORMS J. Appl. Anal. 47(5):454–466.LinkGoogle Scholar
  • Ettl M, Harsha P, Papush A, Perakis G (2020) A data-driven approach to personalized bundle pricing and recommendation. Manufacturing Service Oper. Management 22(3):461–480.LinkGoogle Scholar
  • Fader PS, Hardie BGS, Lee KL (2005) Counting your customers the easy way: An alternative to the Pareto/NBD model. Marketing Sci. 24(2):275–284.LinkGoogle Scholar
  • Gallego G, Li A, Truong VA, Wang X (2020) Approximation algorithms for product framing and pricing. Oper. Res. 68(1):134–160.LinkGoogle Scholar
  • Goldman M, Quistorff B (2018) Pricing engine: Estimating causal impacts in real world business settings. Preprint, submitted June 12, https://doi.org/10.48550/arXiv.1806.03285.Google Scholar
  • Golrezaei N, Nazerzadeh H, Rusmevichientong P (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.LinkGoogle Scholar
  • Guelman L, Guillén M, Pérez-Marín AM (2015) Uplift random forests. Cybernetic Systems 46(3–4):230–248.CrossrefGoogle Scholar
  • Guidotti R, Monreale A, Turini F, Pedreschi D, Giannotti F (2018) A survey of methods for explaining black box models. ACM Comput. Surveys 51(5):1–42.CrossrefGoogle Scholar
  • Huang T, Bergman D, Gopal R (2019) Predictive and prescriptive analytics for location selection of add-on retail products. Production Oper. Management 28(7):1858–1877.CrossrefGoogle Scholar
  • Johar M, Mookerjee V, Sarkar S (2014) Selling vs. profiling: Optimizing the offer set in web-based personalization. Inform. Systems Res. 25(2):285–306.LinkGoogle Scholar
  • Kamakura WA, Russell GJ (1989) A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. 26(4):379–390.CrossrefGoogle Scholar
  • Kok AG, Fisher ML, Vaidyanathan R (2008) Assortment planning: Review of literature and industry practice. Retail Supply Chain Management 122(1):99–153.CrossrefGoogle Scholar
  • Netessine S, Savin S, Xiao W (2006) Revenue management through dynamic cross selling in e-commerce retailing. Oper. Res. 54(5):893–913.LinkGoogle Scholar
  • Rokou T (2021) Airline ancillary revenue begins recovery with a 13% increase to $65.8 billion for 2021. Accessed May 31, 2023, https://www.traveldailynews.com/post/airline-ancillary-revenue-begins-recovery-with-a-13-increase-to-658-billion-for-2021.Google Scholar
  • Schmittlein DC, Peterson RA (1994) Customer base analysis: An industrial purchase process application. Marketing Sci. 13(1):41–67.LinkGoogle Scholar
  • Schmittlein DC, Morrison DG, Colombo R (1987) Counting your customers: Who are they and what will they do next? Management Sci. 33(1):1–24.LinkGoogle Scholar
  • Vermunt JK, Magidson J (2003) Latent class models for classification. Comput. Statist. Data Anal. 41(3–4):531–537.CrossrefGoogle Scholar
  • Wager S, Athey S (2018) Estimation and inference of heterogeneous treatment effects using random forests. J. Amer. Statist. Assoc. 113(523):1228–1242.CrossrefGoogle Scholar
  • Wang R, Dada M, Sahin O (2019) Pricing ancillary service subscriptions. Management Sci. 65(10):4712–4732.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.