Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach
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Abhishek, V., Fader, P., Hosanagar, K.: The Long Road to Online Conversion: A Model of Multi-channel Attribution. http://ssrn.com/abstract=2158421 working paper (2011)
Ailawadi, K.L., Neslin, S.A.: The effect of promotion on consumption: buying more and using it faster. J. Mark. Res. 35(3), 390–398 (1998)
Aitkin, M.: A general maximum likelihood analysis of overdispersion in generalized linear models. Stat. Comput. 6(3), 251–262 (1996)
Altman, R.M.: Mixed hidden markov models: an extension of the hidden Markov model to the longitudinal data setting. J. Am. Stat. Assoc. 102(477), 201–210 (2007)
Ascarza, E., Hardie, B.: A joint model of usage and churn in contractual settings. Mark. Sci. 32(4), 570–590 (2013)
Bartolucci, F., Farcomeni, A., Pennoni, F.: Latent Markov models for longitudinal data. CRC Press, Boca Raton (2013)
Bartolucci, F., Farcomeni, A., Pennoni, F.: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates. Test 23(3), 433–465 (2014)
Bartolucci, F., Montanari, G.E., Pandolfi, S.: Three-step estimation of latent Markov models with covariates. Comput. Stat. Data Anal. 83, 287–301 (2015). https://doi.org/10.1016/j.csda.2014.10.017 . http://www.sciencedirect.com/science/article/pii/S0167947314003090
Bartolucci, F., Pennoni, F., Vittadini, G.: Assessment of school performance through a multilevel latent Markov Rasch model. J. Educ. Behav. Stat. 36, 491–522 (2011)
Bartolucci, F., Pennoni, F., Vittadini, G.: Causal latent Markov model for the comparison of multiple treatments in observational longitudinal studies. J. Educ. Behav. Stat. 41, 146–179 (2016)
Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37, 1554–1563 (1966)
Baum, L.E., Petrie, T., Soules, G., Weiss, N.: A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41, 164–171 (1970)
DiMari, R., Oberski, D.L., Vermunt, J.K.: Bias-adjusted three-step latent markov modeling with covariates. Struct. Equ. Model. Multidiscip. J. 23(5), 649–660 (2016). https://doi.org/10.1080/10705511.2016.1191015
Dinner, I.M., van Heerde, H.J., Neslin, S.A.: Driving online and offline sales: the cross-channel effects of digital versus traditional advertising. Tuck School of Business Working Paper No. 2012-103. http://ssrn.com/abstract=1955653 (2011)
Fader, P.S., Hardie, B.G.S.: Customer-base valuation in a contractual setting: the perils of ignoring heterogeneity. Mark. Sci. 29(1), 85–93 (2010)
Fader, P.S., Hardie, B.G.S., Chun-Yao, H.: A dynamic changepoint model for new product sales forecasting. Mark. Sci. 23(1), 50–65 (2004)
Hauser, J.R., Wisniewski, K.J.: Dynamic analysis of consumer response to marketing strategies. Manag. Sci. 28(5), 455–486 (1982)
Ho, T.H., Li, S., Park, S.E., Shen, Z.J.M.: Customer influence value and purchase acceleration in new product diffusion. Mark. Sci. 31(2), 236–256 (2012)
Horsky, D., Misra, S., Nelson, P.: Observed and unobserved preference heterogeneity in brand-choice models. Mark. Sci. 25(4), 322–335 (2006)
Iyengar, R., Bulte, CVd, Valente, T.W.: Opinion leadership and social contagion in new product diffusion. Mark. Sci. 30(2), 195–212 (2011)
Kalwani, M.U., Yim, C.K., Rinne, H.J., Sugita, Y.: A price expectations model of customer brand choice. J. Mark. Res. 27(3), 251–262 (1990)
Kumar, V., Venkatesan, R., Bohling, T., Beckmann, D.: The power of clv: managing customer lifetime value at IBM. Mark. Sci. 27(4), 585–599 (2008)
Lagona, F., Jdanov, D., Shkolnikova, M.: Latent time-varying factors in longitudinal analysis: a linear mixed hidden Markov model for heart rates. Stat. Med. 33(23), 4116–4134 (2014)
Langeheine, R.S., van de Pol, F.: State mastery learning: dynamic models for longitudinal data. Appl. Psychol. Meas. 18(3), 277–291 (1994)
Lanza, S.T., Cooper, B.R.: A new sas procedure for latent transition analysis: transitions in dating and sexual risk behavior. Dev. Psychol. 44(2), 446–456 (2008). https://doi.org/10.1037/0012-1649.44.2.446
Lanza, S.T., Cooper, B.R.: Latent class analysis for developmental research. Child Dev. Perspect 10(1), 59–64 (2016). https://doi.org/10.1111/cdep.12163
Lee, S., Zufryden, F., Drèze, X.: A study of consumer switching behavior across internet portal web sites. Int. J. Electron. Commun. 7(3), 39–63 (2003)
Lewis, M.: Research note: a dynamic programming approach to customer relationship pricing. Manag. Sci. 51(6), 986–994 (2005)
Marino, M.F., Alfò, M.: Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: a simulation study. Comput. Stat. Data Anal. 94, 193–209 (2016)
Mark, T., Bulla, J., Niraj, R., Bulla, I., Schwarzwällere, W.: Catalogue as a tool for reinforcing habits: empirical evidence from a multichannel retailer. Int. J. Res. Mark. (2019). https://doi.org/10.1016/j.ijresmar.2019.01.009
Mark, T., Lemon, K.N., Vandenbosch, M., Bulla, J., Maruotti, A.: Capturing the evolution of customer-firm relationships: how customers become more (or less) valuable over time. J. Retail. 89(3), 231–245 (2013)
Maruotti, A.: Mixed hidden markov models for longitudinal data: an overview. Int. Stat. Rev. 79(3), 427–454 (2011)
Maruotti, A., Rocci, R.: A mixed non-homogeneous hidden markov model for categorical data, with application to alcohol consumption. Stat. Med. 31(9), 871–886 (2012)
Maruotti, A., Rydén, T.: A semiparametric approach to hidden Markov models under longitudinal observations. Stat. Comput. 19(4), 381–393 (2009)
Moe, W.W., Fader, P.S.: Capturing evolving visit behavior in clickstream data. J. Interact. Mark. 18(1), 5–19 (2004)
Montoya, R., Netzer, O., Jedidi, K.: Dynamic allocation of pharmaceutical detailing and sampling for long-term profitability. Mark. Sci. 29(5), 909–924 (2010)
Naik, P.A., Petersm, K.: A hierarchical marketing communications model of online and offline media synergies. J. Interact. Mark. 23(4), 288–299 (2009)
Netzer, O., Lattin, M., Srinivasan, V.: A hidden Markov model of consumer relationship dynamic. Mark. Sci. 27(2), 185–204 (2008)
Netzer, O., Ebbes, P.T., Bijmolt, H.: Hidden Markov models in marketing. In: Leeflang, P., Wieringa, J., Bijmolt, T., Pauwels, K. (eds.) Advanced Methods for Modeling Markets, International Series in Quantitative Marketing, vol. 14, pp. 405–449. Springer, Cham (2017)
Niculescu, M.F., Shin, H., Whang, S.: Underlying consumer heterogeneity in markets for subscription-based it services with network effects. Inform. Syst. Res. 23(4), 1322–1341 (2012)
Park, S., Gupta, S.: A regime-switching model of cyclical category buying. Mark. Sci. 30(3), 469–480 (2011)
Pongsapukdee, V., Sukgumphaphan, S.: Goodness of fit of cumulative logit models for ordinal response categories and nominal explanatory variables with two-factor interaction. Silpakorn U Sci. Technol. J. 1(2), 29–38 (2007)
Schmiege, S.J., Meek, P., Bryan, A.D., Petersen, H.: Latent variable mixture modeling: a flexible statistical approach for identifying and classifying heterogeneity. Nurs. Res. 61(3), 204–212 (2012)
Schweidel, D.A., Bradlow, E.T., Fader, P.S.: Portfolio dynamics for customers of a multiservice provider. Manag. Sci. 57(3), 471–486 (2011)
Sen, S., Raghu, T.S., Vinze, A.: Demand heterogeneity in it infrastructure services: modeling and evaluation of a dynamic approach to defining service levels. Inf. Syst. Res. 20(2), 258–276 (2009)
Shachat, J., Wei, L.: Procuring commodities: first-price sealed-bid or english auctions? Mark. Sci. 31(2), 317–333 (2012)
Shin, S., Misra, S., Horsky, D.: Disentangling preferences and learning in brand choice models. Mark. Sci. 31(1), 115–137 (2012)
Singh, V.P., Hansen, K.T., Blattberg, R.C.: Market entry and consumer behavior: an investigation of a wal-mart supercenter. Mark. Sci. 25(5), 457–476 (2006)
Thomas, J.S., Sullivan, U.: Managing marketing communications with multichannel customers. J. Mark. 69(4), 239–251 (2005)
Valentini, S., Montaguti, E., Neslin, S.A.: Decision process evolution in customer channel choice. J. Mark. 75(6), 72–86 (2011)
Visser, I.: Seven things to remember about hidden Markov models: a tutorial on Markovian models for time series. J. Math. Psychol. 55(6), 403–415 (2011)
Wiesel, T., Pauwels, K., Arts, J.: Practice prize paper—marketing’s profit impact: quantifying online and off-line funnel progression. Mark. Sci. 30(4), 604–611 (2011)
Zucchini, W., MacDonald, I.L., Langrock, R.: Hidden Markov Models for Time Series: An Introduction Using R, 2nd edn. Chapman & Hall, Boca Raton (2016)