A Learning Approach for Interactive Marketing to a Customer Segment
Published Online:1 Dec 2007https://doi.org/10.1287/opre.1070.0427
References
- Relaxations of weakly coupled stochastic dynamic programs. Oper. Res. (2007) . ForthcomingGoogle Scholar
- Asymptotically efficient allocation rules for the multiarmed bandit problem with multiple plays—Part I: I.I.D. rewards. IEEE Trans. Automat. Control (1987) 32(11):968–976Crossref, Google Scholar
- E-customization. J. Marketing Res. (2003) 40(2):131–145Crossref, Google Scholar
- Design to learn: Customizing services when the future matters. (2002) . Working paper, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
- Discrete Choice Analysis: Theory and Application to Travel Demand (1985) (MIT Press, Cambridge, MA) Google Scholar
- Bandit Problems: Sequential Allocation of Experiments (1985) (Chapman and Hall, London, UK) Crossref, Google Scholar
- Neuro-Dynamic Programming (1996) (Athena Scientific, Belmont, MA) Google Scholar
- Mailing decisions in the catalog sales industry. Management Sci. (1996) 42(9):1364–1381Link, Google Scholar
- Optimal selection for direct mail. Marketing Sci. (1995) 14(4):378–394Link, Google Scholar
- Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. (2007) 53(2):276–292Link, Google Scholar
- Approximate dynamic programming for sensor management. Proc. 36th IEEE Conf. Decision and Control (1997) San Diego, CA:1202–1207Crossref, Google Scholar
- Asymptotic optimal group sequential strategies in two-armed bandit problems. (2002) . Technical Report UTMDABTR-001-02, M.D. Anderson Cancer Center, University of Texas, Austin, TXGoogle Scholar
- Fundamentals of Applied Probability Theory (1967) (McGraw Hill, New York) Google Scholar
- Bandit processes and dynamic allocation indices. J. Roy. Statist. Soc. B (1979) 41:148–164Crossref, Google Scholar
- Multiarmed Bandit Allocation Indices (1989) (Wiley, Chichester, UK) Google Scholar
- , Gani J. A dynamic allocation index for the sequential design of experiments. Progress in Statistics (1974) (North-Holland, Amsterdam, The Netherlands) 241–266Google Scholar
- Optimal mailing of catalogs: A new methodology using estimable structural dynamic programming models. Management Sci. (1998) 44(9):1249–1262Link, Google Scholar
- Dynamic customization of marketing messages in interactive media. (2000) . Research Paper 1664, Graduate School of Business, Stanford University, Stanford, CAGoogle Scholar
- Optimal few-stage designs. J. Statist. Plan. Infer. (2002) 104:121–145Crossref, Google Scholar
- A Lagrangian decomposition approach to weakly coupled dynamic optimization problems and its applications. (2003) . Ph.D. thesis, Operations Research Center, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
- Adaptive treatment allocation and the multi-armed bandit problem. Ann. Statist. (1987) 15(3):1091–1114Crossref, Google Scholar
- Asymptotically efficient adaptive allocation rules. Adv. Appl. Math. (1985) 6:4–22Crossref, Google Scholar
- Solving very large weakly coupled Markov decision processes. Proc. 15th National Conf. Artificial Intelligence (1998) Madison, WI(American Association for Artificial Intelligence, Menlo Park, CA) 165–172Google Scholar
- Applied Statistical Decision Theory (1961) (Division of Research, Graduate School of Business Administration, Harvard University, Boston, MA) Google Scholar
- The value of purchase history data in target marketing. Marketing Sci. (1996) 15(4):430–444Link, Google Scholar
- Bayesian Statistics and Marketing (2005) (Wiley, New York) Crossref, Google Scholar
- Dynamic catalog mailing policies. Management Sci. (2006) 52(5):683–696Link, Google Scholar
- Restless bandits: Activity allocation in a changing world. A Celebration of Applied Probability. J. Appl. Probab. (1988) 25A:287–298Crossref, Google Scholar
- The LP/POMDP marriage: Optimization with imperfect information. Naval Res. Logist. (2000) 47:607–619Crossref, Google Scholar

