Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach

Antonello Maruotti1, Jan Bulla2, Tyler Mark3
1Dipartimento di Giurisprudenza, Economia, Politica e Lingue Moderne, Libera Università Maria Ss. Assunta, Rome, Italy
2Department of Mathematics, University of Bergen, Bergen, Norway
3College of Business and Economics, University of Guelph, Guelph, Canada

Tóm tắt

Từ khóa


Tài liệu tham khảo

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)

Ansari, A., Mela, C.F., Neslin, S.A.: Customer channel migration. J. Mark. Res. 45(1), 60–76 (2008)

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)

Huang, C.Y.: Excess loyalty in online retailing. Int. J. Electron. Commun. 16(2), 115–134 (2011)

Iyengar, R., Jedidi, K.: A conjoint model of quantity discounts. Mark. Sci. 31(2), 334–350 (2012)

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)