Estimating Marketing Component Effects: Double Machine Learning from Targeted Digital Promotions

Published Online:https://doi.org/10.1287/mksc.2022.1401

References

  • Ansari A, Mela CF (2003) E-customization. J. Marketing Res. 40(2):131–145.CrossrefGoogle Scholar
  • Ascarza E (2018) Retention futility: Targeting high-risk customers might be ineffective. J. Marketing Res. 55(1):80–98.CrossrefGoogle Scholar
  • Athey S, Imbens G (2016) Recursive partitioning for heterogeneous causal effects. Proc. Natl. Acad. Sci. USA 113(27):7353–7360.CrossrefGoogle Scholar
  • Athey S, Imbens GW (2019) Machine learning methods that economists should know about. Annual Rev. Econom. 11:685–725.CrossrefGoogle Scholar
  • Athey S, Wager S (2019) Estimating treatment effects with causal forests: An application. Observational Stud. 5(2):37–51.CrossrefGoogle Scholar
  • Bonfrer A, Drèze X (2009) Real-time evaluation of email campaign performance. Marketing Sci. 28(2):251–263.LinkGoogle Scholar
  • Box G, Wilson K (1951) On the experimental attainment of optimum conditions. J. Roy. Statist. Soc. B 13(1):1–45.CrossrefGoogle Scholar
  • Chatterjee P, McGinnis J (2010) Customized online promotions: Moderating effect of promotion type on deal value, perceived fairness, and purchase intent. J. Appl. Bus. Res. 26(4).CrossrefGoogle Scholar
  • Chernozhukov V, Demirer M, Duflo E, Fernandez-Val I (2017) Generic machine learning inference on heterogenous treatment effects in randomized experiments. Technical report, National Bureau of Economic Research, Cambridge, MA.Google Scholar
  • Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W, Robins J (2018) Double/debiased machine learning for treatment and structural parameters. Econom. J. 21(1):C1–C68.CrossrefGoogle Scholar
  • Cohen J (2013) Statistical Power Analysis for the Behavioral Sciences (Academic Press, New York).CrossrefGoogle Scholar
  • Cook TD, Campbell DT, Shadish W (2002) Experimental and Quasi-Experimental Designs for Generalized Causal Inference (Houghton Mifflin, Boston).Google Scholar
  • Cox DR (1958) Planning of Experiments (Wiley, Hoboken, NJ).Google Scholar
  • Crump RK, Hotz VJ, Imbens GW, Mitnik OA (2009) Dealing with limited overlap in estimation of average treatment effects. Biometrika 96(1):187–199.CrossrefGoogle Scholar
  • Diamond WD, Johnson RR (1990) The framing of sales promotions: An approach to classification. Goldberg ME, Gorn G, Pollay RW, eds. Advances in Consumer Research, vol. 17 (Association for Consumer Research, Provo, UT), 494–500.Google Scholar
  • Dudík M, Langford J, Li L (2011) Doubly robust policy evaluation and learning. Proc. 28th Internat. Conf. Internat. Conf. Machine Learn., 1097–1104.Google Scholar
  • Dudík M, Erhan D, Langford J, Li L (2014) Doubly robust policy evaluation and optimization. Statist. Sci. 29(4):485–511.CrossrefGoogle Scholar
  • Gopalakrishnan A, Park Y-H (2021) The impact of coupons on the visit-to-purchase funnel. Marketing Sci. 40(1):48–61.LinkGoogle Scholar
  • Gordon BR, Moakler R, Zettelmeyer F (2022) Close enough? A large-scale exploration of non-experimental approaches to advertising measurement. Preprint, submitted January 18, https://arxiv.org/abs/2201.07055.Google Scholar
  • Gordon BR, Zettelmeyer F, Bhargava N, Chapsky D (2019) A comparison of approaches to advertising measurement: Evidence from big field experiments at Facebook. Marketing Sci. 38(2):193–225.LinkGoogle Scholar
  • Grimmer J, Messing S, Westwood SJ (2017) Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methods. Political Anal. 25(4):413–434.CrossrefGoogle Scholar
  • Heckman JJ, Vytlacil EJ (2007) Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, vol. 6, 4779–4874.CrossrefGoogle Scholar
  • Hernán MA, Robins JM (2006) Estimating causal effects from epidemiological data. J. Epidemiology Community Health 60(7):578–586.CrossrefGoogle Scholar
  • Hernán MA, Robins JM (2020) Causal Inference: What If (Chapman & Hall/CRC, Boca Raton, FL).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
  • Holland PW (1986) Statistics and causal inference. J. Amer. Statist. Assoc. 81(396):945–960.CrossrefGoogle Scholar
  • Imai K, Ratkovic M (2013) Estimating treatment effect heterogeneity in randomized program evaluation. Ann. Appl. Statist. 7(1):443–470.CrossrefGoogle Scholar
  • Imai K, Strauss A (2011) Estimation of heterogeneous treatment effects from randomized experiments, with application to the optimal planning of the get-out-the-vote campaign. Political Anal. 19(1):1–19.CrossrefGoogle Scholar
  • Imbens GW, Rubin DB (2015) Causal Inference in Statistics, Social, and Biomedical Sciences (Cambridge University Press, Cambridge, MA).CrossrefGoogle Scholar
  • Jacob D (2019) Group average treatment effects for observational studies. Preprint, submitted November 7, https://arxiv.org/abs/1911.02688.Google Scholar
  • Johnson G (2020) Inferno: A guide to field experiments in online display advertising. Preprint, submitted May 15, https://dx.doi.org/10.2139/ssrn.3581396.Google Scholar
  • Johnson G, Lewis RA, Nubbemeyer E (2017). The online display ad effectiveness funnel & carryover: Lessons from 432 field experiments. Preprint, submitted December 11, 2015, https://dx.doi.org/10.2139/ssrn.2701578.Google Scholar
  • Kalwani MU, Yim CK, Rinne HJ, Sugita Y (1990) A price expectations model of customer brand choice. J. Marketing Res. 27(3):251–262.CrossrefGoogle Scholar
  • Kodinariya TM, Makwana PR (2013) Review on determining number of clusters in k-means clustering. Internat. J. 1(6):90–95.Google Scholar
  • Krishna A, Currim IS, Shoemaker RW (1991) Consumer perceptions of promotional activity. J. Marketing 55(2):4–16.CrossrefGoogle Scholar
  • Kumar V, Zhang X, Luo A (2014) Modeling customer opt-in and opt-out in a permission-based marketing context. J. Marketing Res. 51(4):403–419.CrossrefGoogle Scholar
  • Lee BK, Lessler J, Stuart EA (2011) Weight trimming and propensity score weighting. PLoS One 6(3):e18174.CrossrefGoogle Scholar
  • Lewis RA, Rao JM (2015) The unfavorable economics of measuring the returns to advertising. Quart. J. Econom. 130(4):1941–1973.CrossrefGoogle Scholar
  • Liaukonyte J, Teixeira T, Wilbur KC (2015) Television advertising and online shopping. Marketing Sci. 34(3):311–330.LinkGoogle Scholar
  • McCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF (2013) A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Statist. Medicine 32(19):3388–3414.CrossrefGoogle Scholar
  • Neyman J (1923) Statistical problems in agricultural experimentation. J. Roy. Statist. Soc. 2(2):107–180.Google Scholar
  • Park CH, Park Y-H, Schweidel DA (2018) The effects of mobile promotions on customer purchase dynamics. Internat. J. Res. Marketing 35(3):453–470.CrossrefGoogle Scholar
  • Quandt RE (1958) The estimation of the parameters of a linear regression system obeying two separate regimes. J. Amer. Statist. Assoc. 53(284):873–880.CrossrefGoogle Scholar
  • Robins JM, Rotnitzky A (1995) Semiparametric efficiency in multivariate regression models with missing data. J. Amer. Statist. Assoc. 90(429):122–129.CrossrefGoogle Scholar
  • Rubin DB (1978) Bayesian inference for causal effects: The role of randomization. Ann. Statist. 6(1):34–58.CrossrefGoogle Scholar
  • Rubin DB (1980) Randomization analysis of experimental data: The Fisher randomization test. J. Amer. Statist. Assoc. 75(371):591–593.Google Scholar
  • Sahni NS, Wheeler SC, Chintagunta P (2018) Personalization in email marketing: The role of noninformative advertising content. Marketing Sci. 37(2):236–258.LinkGoogle 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.LinkGoogle Scholar
  • Semenova V, Chernozhukov V (2021) Debiased machine learning of conditional average treatment effects and other causal functions. Econom. J. 24(2):264–289.CrossrefGoogle Scholar
  • Sinha I, Smith MF (2000) Consumers’ perceptions of promotional framing of price. Psych. Marketing 17(3):257–275.CrossrefGoogle Scholar
  • Van der Laan MJ, Rose S (2011) Targeted Learning: Causal Inference for Observational and Experimental Data (Springer Science & Business Media, New York).CrossrefGoogle Scholar
  • Van der Laan MJ, Polley EC, Hubbard AE (2007) Super learner. Statist. Appl. Genetics Molecular Biol. 6(1):1–21.Google 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
  • Yi Y, Yoo J (2011) The long-term effects of sales promotions on brand attitude across monetary and non-monetary promotions. Psych. Marketing 28(9):879–896.CrossrefGoogle Scholar
  • Yoganarasimhan H, Barzegary E, Pani A (2020) Design and evaluation of personalized free trials. Technical report, University of Washington, Seattle.Google Scholar
  • Zeelenberg M, Putten MV (2005) The dark side of discounts: An inaction inertia perspective on the post-promotion dip. Psych. Marketing 22(8):611–622.CrossrefGoogle Scholar
  • Zhang X, Kumar V, Cosguner K (2017) Dynamically managing a profitable email marketing program. J. Marketing Res. 54(6):851–866.CrossrefGoogle Scholar
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