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Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models

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  • A. PRINZIE

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  • D. VAN DEN POEL

    ()

Abstract

The acquisition process of consumer durables is a ‘sequence’ of purchase events. Priority-pattern research exploits this ‘sequential order’ to describe a prototypical acquisition order for durables. This paper adds a predictive perspective to increase managerial relevance. Besides order information, the acquisition sequence also reveals precise timing between purchase events (‘sequential duration’) as examined in the literature on durable replacement and time-to-first acquisition. This paper bridges the gap between priority-pattern research and research on duration between durable acquisitions to improve the prediction of the product group the customer might acquire his next durable from, i.e. Next-Product-to-Buy (NPTB) model. We evaluate four multinomial-choice models incorporating: 1) general covariates, 2) general covariates and sequential order, 3) general covariates and sequential duration, and 4) general covariates, sequential order and duration. The results favor the model including general covariates and duration information (3). The high predictive value of sequentialduration information emphasizes the predictive power of duration as compared to order information.

Suggested Citation

  • A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:07/442
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    1. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    2. Kasulis, Jack J & Lusch, Robert F & Stafford, Edward F, Jr, 1979. " Consumer Acquisition Patterns for Durable Goods," Journal of Consumer Research, Oxford University Press, vol. 6(1), pages 47-57, June.
    3. Hauser, John R & Urban, Glen L, 1986. " The Value Priority Hypotheses for Consumer Budget Plans," Journal of Consumer Research, Oxford University Press, vol. 12(4), pages 446-462, March.
    4. Barry L. Bayus, 1992. "Brand Loyalty and Marketing Strategy: An Application to Home Appliances," Marketing Science, INFORMS, vol. 11(1), pages 21-38.
    5. Sudeep Haldar & Vithala Rao, 1998. "A micro-analytic threshold model for the timing of first purchases of durable goods," Applied Economics, Taylor & Francis Journals, vol. 30(7), pages 959-974.
    6. Geoffrey Soutar & Steven Cornish-Ward, 1997. "Ownership patterns for durable goods and financial assets: a Rasch analysis," Applied Economics, Taylor & Francis Journals, vol. 29(7), pages 903-911.
    7. Seetharaman, P B & Chintagunta, Pradeep K, 2003. "The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 368-382, July.
    8. Dickson, Peter R & Lusch, Robert F & Wilkie, William L, 1983. " Consumer Acquisition Priorities for Home Appliances: A Replication and Re-evaluation," Journal of Consumer Research, Oxford University Press, vol. 9(4), pages 432-435, March.
    9. Johnson, Michael D, 1984. " Consumer Choice Strategies for Comparing Noncomparable Alternatives," Journal of Consumer Research, Oxford University Press, vol. 11(3), pages 741-753, December.
    10. Murphy, Patrick E & Staples, William A, 1979. " A Modernized Family Life Cycle," Journal of Consumer Research, Oxford University Press, vol. 6(1), pages 12-22, June.
    11. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    12. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    13. Viviana P. Fernandez, 2000. "Decisions To Replace Consumer Durables Goods: An Econometric Application Of Wiener And Renewal Processes," The Review of Economics and Statistics, MIT Press, vol. 82(3), pages 452-461, August.
    14. Paas, Leonard J., 1998. "Mokken scaling characteristic sets and acquisition patterns of durable- and financial products," Journal of Economic Psychology, Elsevier, vol. 19(3), pages 353-376, June.
    15. Ratneshwar, S & Pechmann, Cornelia & Shocker, Allan D, 1996. " Goal-Derived Categories and the Antecedents of Across-Category Consideration," Journal of Consumer Research, Oxford University Press, vol. 23(3), pages 240-250, December.
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    Cited by:

    1. Fan, Zhi-Ping & Sun, Minghe, 2016. "A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendationsAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 255(1), pages 110-120.
    2. repec:spr:binfse:v:60:y:2018:i:2:d:10.1007_s12599-017-0485-1 is not listed on IDEAS
    3. Michelsen, Carl Christian & Madlener, Reinhard, 2016. "Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany," Energy Policy, Elsevier, vol. 89(C), pages 95-105.
    4. Vera Miguéis & Dirk Poel & Ana Camanho & João Falcão e Cunha, 2012. "Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 337-353, December.
    5. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.
    6. Fan, Zhi-Ping & Sun, Minghe, 2015. "Behavior-aware user response modeling in social media: Learning from diverse heterogeneous dataAuthor-Name: Chen, Zhen-Yu," European Journal of Operational Research, Elsevier, vol. 241(2), pages 422-434.
    7. V. L. Miguéis & D. Van Den Poel & A.S. Camanho & J. Falcao E Cunha, 2012. "Modeling Partial Customer Churn: On the Value of First Product-Category Purchase Sequences," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/790, Ghent University, Faculty of Economics and Business Administration.
    8. D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.
    9. Zhen-Yu Chen & Zhi-Ping Fan & Minghe Sun, 2014. "Ensemble Learning for Cross-Selling Using Multitype Multiway Data," Working Papers 0155mss, College of Business, University of Texas at San Antonio.
    10. Joseph Guiltinan, 2010. "Consumer durables replacement decision-making: An overview and research agenda," Marketing Letters, Springer, vol. 21(2), pages 163-174, June.
    11. D. Thorleuchter & D. Van Den Poel, 2013. "Weak Signal Identification with Semantic Web Mining," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/860, Ghent University, Faculty of Economics and Business Administration.
    12. Talla Nobibon, Fabrice & Leus, Roel & Spieksma, Frits C.R., 2011. "Optimization models for targeted offers in direct marketing: Exact and heuristic algorithms," European Journal of Operational Research, Elsevier, vol. 210(3), pages 670-683, May.

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    Keywords

    cross-sell; sequence analysis; choice modeling; durable goods; analytical CRM;

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    1. Alineamiento de secuencias in Wikipedia Spanish ne '')
    2. Aliñamento de secuencias in Wikipedia Galician ne '')
    3. User:Webridge/我的沙盘/2 in Wikipedia Chinese ne '')
    4. Dizi hizalaması in Wikipedia Turkish ne '')
    5. Sequence alignment in Wikipedia English ne '')

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