nach oben

Marketing Letters

Erschienen in:

01.09.2016

Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model

verfasst von: Joseph Pancras, Xia Wang, Dipak K. Dey

Erschienen in: Marketing Letters | Ausgabe 3/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this paper, we study the impact of customer stochasticity on firm price discrimination strategies. We develop a new model termed the Bayesian Mixture Scale Heterogeneity (BMSH) model that incorporates both parameter heterogeneity and customer stochasticity using a mixture model approach, and demonstrate model identification using extensive simulations. We estimate the model on yogurt scanner data and find that compared to the benchmark mixed logit and multinomial probit models, our model shows that markets are less price elastic, and that a majority of customers exhibit stochasticity in purchases; our model also obtains better prediction and more profitable targeting strategies.

Vorheriger Artikel The effect of disfluency on consumer perceptions of information security

Nächster Artikel From digital media influencers to celebrity endorsers: attributions drive endorser effectiveness

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

The Web Appendix material is available from the authors on request.

While for simplicity, we maintain the same true value of the price coefficient for the MIXL and HetSMNL utility components of the BMSH model; we have conducted additional simulations to ensure that the true values are recovered when these coefficients are different (\( {\beta}_p^M\ne {\beta}_p^S \)). Results are available from the authors on request.

We term this as heterogeneous SMNL (HetSMNL) to distinguish it from the SMNL model of Fiebig et al. (2010) (Figure 1, page 398) which does not have heterogeneity in the ASCs.

However, in the case of 70 % MIXL, NP = 100, T=10, price coefficient = −6, the MNP model does not converge well and shows multimodality in the posterior distribution.

However, we note that the DIC measure may not always give accurate values for mixture models. We thank an anonymous reviewer for pointing this out (see Geweke and Keane 2006).

These figures for the other three brands as focal brands are available from the authors on request.

We have examined conditions under which targeted coupon face values are higher for two different discrete choice models and find that this is related to the differing price coefficients as well as the magnitude of difference between the odds computed using the two different models. The factors affecting different optimal coupon face values are thus more nuanced than just a comparison of price coefficients or price elasticities in the two models would suggest. Further results are available from the authors on request.

Ben-Akiva, M., & Lerman, S. (1985). Discrete choice analysis: theory and application to travel demand. Cambridge: MIT Press.

Chintagunta, P. K. (1992). Heterogeneity in nested logit models: an estimation approach and empirical results. International Journal of Research in Marketing, 9(2), 161–175.CrossRef

Feit EM (2009) Extending the generalized multinomial logit model: error scale and decision-maker characteristics. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1566068. Accessed August 23, 2012.

Fiebig, D. G., Keane, M. P., Louviere, J., & Wasi, N. (2010). The generalized multinomial logit model: accounting for scale and coefficient heterogeneity. Marketing Science, 29(3), 393–421.CrossRef

Geweke, J., & Keane, M. (2006). Smoothly mixing regressions. Journal of Econometrics, 138(1), 252–290.CrossRef

Hartmann, W. R. (2010). Demand estimation with social interactions and the implications for targeted marketing. Marketing Science, 29(4), 585–601.CrossRef

Hutchinson, J. W., Zauberman, G., & Meyer, R. (2010). Commentary—on the interpretation of temporal inflation parameters in stochastic models of judgment and choice. Marketing Science, 29(1), 23–31.CrossRef

Jain, D. C., Vilcassim, N. J., & Chintagunta, P. K. (1994). A random-coefficients logit brand-choice model applied to panel data. Journal of Business & Economic Statistics, 12(3), 317–328.

Kasahara, H., & Shimotsu, K. (2009). Nonparametric identification of finite mixture models of dynamic discrete choices. Econometrica, 77(1), 135–175.CrossRef

Maddala, G. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge: Cambridge University Press.CrossRef

Pancras, J., & Sudhir, K. (2007). Optimal marketing strategies for a customer data intermediary. Journal of Marketing Research, 44(4), 560–578.CrossRef

Quandt, R. E. (1972). A new approach to estimating switching regressions. Journal of the American Statistical Association, 67(338), 306–310.CrossRef

Rossi, P. E., McCulloch, R. E., & Allenby, G. M. (1996). The value of purchase history data in target marketing. Marketing Science, 15(4), 321–340.CrossRef

Salisbury, L. C., & Feinberg, F. M. (2010a). Alleviating the constant stochastic variance assumption in decision research: theory, measurement, and experimental test. Marketing Science, 29(1), 1–17.CrossRef

Salisbury, L. C., & Feinberg, F. M. (2010b). Rejoinder-temporal stochastic inflation in choice-based research. Marketing Science, 29(1), 32–39.CrossRef

Singh, V. P., Hansen, K. T., & Gupta, S. (2005). Modeling preferences for common attributes in multicategory brand choice. Journal of Marketing Research, 42(2), 195–209.CrossRef

Tirole, J. (1988). The theory of industrial organization. Cambridge: MIT Press.

Titterington, D. M., Smith, A. F., & Makov, U. E. (1985). Statistical analysis of finite mixture distributions (Vol. 7). New York: Wiley.

Zhang, J., & Wedel, M. (2009). The effectiveness of customized promotions in online and offline stores. Journal of Marketing Research, 46(2), 190–206.CrossRef

Zhang, J. Z., Netzer, O., & Ansari, A. (2014). Dynamic targeted pricing in B2B relationships. Marketing Science, 33(3), 317–337.CrossRef

Titel: Investigating the impact of customer stochasticity on firm price discrimination strategies using a new Bayesian mixture scale heterogeneity model
verfasst von: Joseph Pancras
Xia Wang
Dipak K. Dey
Publikationsdatum: 01.09.2016
Verlag: Springer US
Erschienen in: Marketing Letters / Ausgabe 3/2016
Print ISSN: 0923-0645
Elektronische ISSN: 1573-059X
DOI: https://doi.org/10.1007/s11002-015-9362-1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2016

Altering the past to influence the future: the effect of mental unpacking on past evaluations and future preferences

Indicators of opinion leadership in customer networks: self-reports and degree centrality

Pricing and persuasive advertising in a differentiated market

The effect of attribute-based and alternative-based processing on consumer choice in context

The effect of disfluency on consumer perceptions of information security

When a picture’s worth a thousand words: The effects of visual construal priming on information acquisition and choice