Sequential Learning in Designing Marketing Campaigns for Market Entry

Published Online:https://doi.org/10.1287/mnsc.2019.3384

References

  • Abe N, Verma N, Apte C, Schroko R (2004) Cross channel optimized marketing by reinforcement learning. Proc. 10th ACM Internat. SIGKDD Conf. Knowledge Discovery Data Mining (ACM, New York), 767–772.Google Scholar
  • Adams RP, Murray I, MacKay DJC (2009) Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proc. 26th Annual Internat. Conf. Machine Learn. (ACM, New York), 9–16.Google Scholar
  • Agrawal S, Goyal N (2012) Analysis of Thompson sampling for the multi-armed bandit problem. Proc. Machine Learn. Res. 23:39.1–39.26.Google Scholar
  • Anderson ET, Hansen K, Tripathi M (2007) Measuring the mere measurement effect in non-experimental field settings. Working paper, Northwestern University, Evanston, IL.Google Scholar
  • Barajas J, Akella R, Holtan M, Flores A (2016) Experimental designs and estimation for online display advertising attribution in marketplaces. Marketing Sci. 35(3):465–483.LinkGoogle Scholar
  • Basu A, Basu A, Batra R (1995) Modeling the response pattern to direct marketing campaigns. J. Marketing Res. 32(2):204–212.CrossrefGoogle Scholar
  • Bauer L (1987) Direct response advertising: Forecasting responses over time. J. Direct Marketing 1(4):38–50.CrossrefGoogle Scholar
  • Bauer L (1991) Logistic vs. decaying exponential equations for describing mail survey response curves: A conceptual rationale and reanalysis. J. Direct Marketing 5(1):15–26.CrossrefGoogle Scholar
  • Bechhofer R, Santner T, Goldsman D (1995) Design and Analysis of Experiments for Statistical Selection, Screening and Multiple Comparisons (John Wiley & Sons, New York).Google Scholar
  • Bertsekas D (2012) Dynamic Programming and Optimal Control, vol. II, 4th ed. (Athena Scientific, Nashua, NH).Google Scholar
  • Bertsimas D, Mersereau A (2007) A learning approach for interactive marketing to a customer segment. Oper. Res. 55(6):1120–1135.LinkGoogle Scholar
  • Blattberg R, Kim B, Neslin S (2008) Database Marketing: Analyzing and Managing Customers, International Series in Quantitative Marketing (Springer Verlag, New York).CrossrefGoogle Scholar
  • Bose I, Chen X (2009) Quantitative models for direct marketing: A review from systems perspective. Eur. J. Oper. Res. 195(1):1–16.CrossrefGoogle Scholar
  • Bowman D, Gatignon H (2009) Market response and marketing mix models: Trends and research opportunities. Foundations Trends Marketing 4(3):129–207.CrossrefGoogle Scholar
  • Chapelle O, Li L (2012) An empirical evaluation of Thompson sampling. Shawe-Taylor J, Zemel RS, Bartlett PL, Pereira F, Weinberger KQ, eds. Advances in Neural Information Processing Systems, vol. 24 (Curran Associates, Inc., Red Hook, NY), 2249–2257.Google Scholar
  • Chen C, Fu M, Shi L (2008) Simulation and optimization. Chen Z-L, Raghavan S, Gray P, eds. State-of-the-Art Decision-Making Tools in the Information-Intensive Age, TutORials in Operations Research (INFORMS, Catonsville, MD), 247–260.Google Scholar
  • Chick S (2006) Subjective probability and Bayesian methodology. Henderson S, Nelson B, eds. Simulation, Handbooks of Operations Research and Management Science, vol. 13 (North-Holland Publishing, Amsterdam), 225–258.Google Scholar
  • Chick S, Frazier P (2013) Sequential sampling with economics of selection procedures. Management Sci. 58(3):550–569.LinkGoogle Scholar
  • Chick S, Branke J, Schmidt C (2010) Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. 22(1):71–80.LinkGoogle Scholar
  • Ching A, Erdem T, Keane M (2013) Learning models: An assessment of progress, challenges, and new developments. Marketing Sci. 32(6):913–938.LinkGoogle Scholar
  • Chun Y (2012) Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing. Eur. J. Oper. Res. 217(3):673–678.CrossrefGoogle Scholar
  • Chun Y, Jung Y (2015) Predictive modeling of customer response behavior in direct marketing. Warkentin M, ed. The Best Thinking in Business Analytics from the Decision Sciences Institute, 1st ed. (Pearson FT Press, Upper Saddle River, NJ), 1–16.Google Scholar
  • Cobanoglu C, Cobanoglu N (2003) The effect of incentives in web surveys: Application and ethical considerations. Internat. J. Marketing Res. 45(4):475–488.Google Scholar
  • Coussement K, Buckinx W (2011) A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application. Eur. J. Oper. Res. 214(3):732–738.CrossrefGoogle Scholar
  • Danaher P (2007) Modeling page views across multiple websites with an application to internet reach and frequency prediction. Marketing Sci. 26(3):422–437.LinkGoogle Scholar
  • Defourny B, Ryzhov I, Powell W (2015) Optimal information blending with measurements in the L2 sphere. Math. Oper. Res. 40(4):1060–1088.LinkGoogle Scholar
  • Del Moral P (1996) Nonlinear filtering: Interacting particle solution. Markov Processes Related Fields 2(4):555–580.Google Scholar
  • Del Moral P, Doucet A, Jasra A (2012) An adaptive sequential Monte Carlo method for approximate Bayesian computation. Statist. Comput. 22(5):1009–1020.CrossrefGoogle Scholar
  • Dholakia UM (2010) A critical review of question-behavior effect research. Rev. Marketing Res. 7:147–199.Google Scholar
  • Farris P, Bendle N, Pfeifer P, Reibstein D (2015) Marketing Metrics: The Manager’s Guide to Measuring Marketing Performance, 3rd ed. (Pearson FT Press, Upper Saddle River, NJ).CrossrefGoogle Scholar
  • Foedermayr E, Diamantopoulos A (2008) Market segmentation in practice: Review of empirical studies, methodological assessment and agenda for future research. J. Strategic Marketing 16(3):223–265.CrossrefGoogle Scholar
  • Fox R, Reddy S, Rao B (1997) Modeling response to repetitive promotional stimuli. J. Acad. Marketing Sci. 25(3):242–255.CrossrefGoogle Scholar
  • Frazier P, Powell W (2011) Consistency of sequential Bayesian sampling policies. SIAM J. Control Optim. 49(2):712–731.CrossrefGoogle Scholar
  • Frazier P, Powell W, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.CrossrefGoogle Scholar
  • Frazier P, Powell W, Dayanik S (2009) The knowledge-gradient policy for correlated normal beliefs. INFORMS J. Comput. 21(4):599–613.LinkGoogle Scholar
  • Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB (2014) Bayesian Data Analysis, 3rd ed. (CRC Press, Boca Raton, FL).Google Scholar
  • Gittins J, Glazebrook K, Weber R (2011) Multi-armed Bandit Allocation Indices, 2nd ed. (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Goyat S (2011) The basis of market segmentation: A critical review of literature. Eur. J. Bus. Management 3(9):1–11.Google Scholar
  • Graf S, Luschgy H (2000) Foundations of Quantization for Probability Distributions (Springer-Verlag, Berlin).CrossrefGoogle Scholar
  • Gupta S, Miescke K (1996) Bayesian look-ahead one-stage sampling allocations for selection of the best population. J. Statist. Planning Inference 54(2):229–244.CrossrefGoogle Scholar
  • Gupta S, Zeithaml V (2006) Customer metrics and their impact on financial performance. Marketing Sci. 25(6):718–739.LinkGoogle Scholar
  • Han B, Ryzhov I, Defourny B (2016) Optimal learning in linear regression with combinatorial feature selection. INFORMS J. Comput. 28(4):721–735.LinkGoogle Scholar
  • Hanssens D, Leeflang P, Wittink D (2005) Market response models and marketing practice. Appl. Stochastic Models Bus. Indust. 21(4–5):423–434.CrossrefGoogle Scholar
  • Hanssens D, Parsons L, Schultz R (2001) Market Response Models: Econometric and Time-Series Analysis, 2nd ed. (Kluwer Academic Press, Boston).Google Scholar
  • Hill DN, Moakler R, Hubbard AE, Tsemekhman V, Provost F, Tsemekhman K (2015) Measuring causal impact of online actions via natural experiments: Application to display advertising. Proc. 21st ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 1839–1847.Google Scholar
  • Hill R (1981) Using S-shaped curves to predict response rates. J. Marketing Res. 18(2):240–242.CrossrefGoogle Scholar
  • Huxley S (1980) Predicting response speed in mail surveys. J. Marketing Res. 17(1):63–68.CrossrefGoogle Scholar
  • Interactive Advertising Bureau (2013) Audience reach measurement guidelines, Media Rating Council, Inc., New York.Google Scholar
  • Jones D, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.CrossrefGoogle Scholar
  • Klapdor S, Anderl E, Wangenheim F, Schumann J (2014) Finding the right words: The influence of keyword characteristics on performance of paid search campaigns. J. Interactive Marketing 28(4):285–301.CrossrefGoogle Scholar
  • Kohler C, Mantrala M, Albers S, Kanuri V (2017) A meta-analysis of marketing communication carryover effects. J. Marketing Res. 54(6):990–1008.CrossrefGoogle Scholar
  • Kottas A, Sanso B (2007) Bayesian mixture modeling for spatial Poisson process intensities, with applications to extreme value analysis. J. Statist. Planning Inference 137(10):3151–3163.CrossrefGoogle Scholar
  • Kunreuther H, Meyer R, Michel-Kerjan E (2014) To get people to buy insurance, change how you talk about risk. Businessweek (September 22), 1–2.Google Scholar
  • Lam R, Willcox K (2017) Lookahead Bayesian optimization with inequality constraints. Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Inc., Red Hook, NY), 1890–1900.Google Scholar
  • Lilien G, Rangaswamy A, De Bruyn A (2012) Principles of Marketing Engineering, 2nd ed. (DecisionPro, Inc., State College, PA).Google Scholar
  • Lin S, Zhang J, Hauser J (2015) Learning from experience, simply. Marketing Sci. 34(1):1–9.LinkGoogle Scholar
  • Linoff G, Berry M (2011) Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, 3rd ed. (Wiley, Indianapolis).Google Scholar
  • Lu T, Boutilier C (2014) Dynamic segmentation for large-scale marketing optimization. Internat. Conf. Machine Learn., Beijing, 1–8.Google Scholar
  • McCrary M (2009) Enhanced customer targeting with multi-stage models: Predicting customer sales and profit in the retail industry. J. Targeting Measurement Anal. Marketing 17(4):273–295.CrossrefGoogle Scholar
  • McGranahan D, Catlin T, Ray S (2013) Beyond price: The rise of customer-centric marketing in insurance. Financial services practice report, McKinsey & Company.Google Scholar
  • Moro S, Cortez P, Rita P (2014) A data-driven approach to predict the success of bank telemarketing. Decision Support Systems 62:22–31.CrossrefGoogle Scholar
  • Moro S, Laureano R, Cortez P (2011) Using data mining for bank direct marketing: An application of the crisp-dm methodology. Novais P, Machado J, Analide C, Abelha A, eds. Proc. Eur. Simulation Model. Conf. (EUROSIS, Ostend, Belgium), 117–121.Google Scholar
  • Nesamoney D (2015) Personalized Digital Advertising: How Data and Technology Are Transforming How We Market, 1st ed. (Pearson FT Press, Old Tappan, NJ).Google Scholar
  • Pages G, Wilbertz B (2012) Optimal Delaunay and Voronoi quantization schemes for pricing American style options. Carmona RA, Moral PD, Hu P, Oudjane N, eds. Numerical Methods in Finance (Springer, Berlin, Heidelberg), 171–213.CrossrefGoogle Scholar
  • Picheny V, Gramacy R, Wild S, Le Digabel S (2016) Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian. Lee D, Sugiyama M, Luxburg U, Guyon I, Garnett R, eds. Advances in Neural Information Processing Systems, vol. 29 (Curran Associates, Inc., Red Hook, NY), 1435–1443.Google Scholar
  • Powell W (2011) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd ed., Wiley Series in Probability and Statistics (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Puterman M (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Robert CP (2007) The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, 2nd ed. (Springer-Verlag, New York).Google Scholar
  • Robert CP, Marin JM (2007) Bayesian Core: A Practical Approach to Computational Bayesian Statistics, 1st ed. (Springer, New York).Google Scholar
  • Rossi P, Allenby G (2003) Bayesian statistics and marketing. Marketing Sci. 22(3):304–328.LinkGoogle Scholar
  • Russo D, Van Roy B (2014) An information-theoretic analysis of Thompson sampling. J. Machine Learn. Res. 17(68):1–30.Google Scholar
  • Ryzhov I, Powell W (2012) Optimal Learning, 1st ed. (Wiley, Hoboken, NJ).Google Scholar
  • Schultz D, Barnes B, Schultz H, Azzaro M (2009) Building Customer-brand Relationships, 1st ed. (Routledge, New York).Google Scholar
  • Schwartz E, Bradlow E, Fader P (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.LinkGoogle Scholar
  • Tellis G, Franses P (2006) Optimal data interval for estimating advertising response. Marketing Sci. 25(3):217–229.LinkGoogle Scholar
  • Wainwright M, Jordan M (2008) Graphical models, exponential families, and variational inference. Foundations Trends Machine Learn. 1(1–2):1–305.Google Scholar
  • Webber H (1998) Divide and Conquer: Target Your Customers Through Market Segmentation (Wiley, New York).Google Scholar
  • Wedel M, Kamakura W (2000) Market Segmentation: Conceptual and Methodological Foundations, 2nd ed. (Kluwer Academic Publishers, Boston).CrossrefGoogle Scholar
  • Wedel M, Kamakura W (2002) Introduction to the special issue on market segmentation. J. Classification 19(3):179–182.Google Scholar
  • Weinstein A (2006) A strategic framework for defining and segmenting markets. J. Strategic Marketing 14(2):115–127.CrossrefGoogle Scholar
  • Whelan D, O’Neill S (2014) Customer loyalty in P&C insurance. Report, Bain & Company.Google Scholar
  • Zellner A (1986) On assessing prior distributions and bayesian regression analysis with g-prior distributions. Goel P, Zellner A, eds. Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti (Elsevier Science Publishers, Inc., New York), 233–243.Google Scholar
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