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A Combined Approach for Segment-Specific Analysis of Market Basket Data

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  • Yasemin Boztug
  • Thomas Reutterer

Abstract

There are two main research traditions for analyzing market basket data that exist more or less independently from each other, namely exploratory and explanatory model types. Exploratory approaches are restricted to the task of discovering cross-category interrelationships and provide marketing managers with only very limited recommendations regarding decision making. The latter type of models mainly focus on estimating the effects of category-level marketing mix variables on purchase incidences assuming cross-category dependencies. We propose a procedure that combines these two modeling approaches in a novel two-stage procedure for analyzing cross-category effects based on shopping basket data: In a data compression step we first derive a set of market basket prototypes and generate segments of households with internally more distinctive (complementary) cross-category interdependencies. Utilizing the information on categories that are most responsible for prototype construction, segment-specific multivariate logistic models are estimated in a second step. Based on the data-driven way of basket construction, we can show significant differences in cross- effects and related price elasticities both across segments and compared to the global (segment-unspecific) model.

Suggested Citation

  • Yasemin Boztug & Thomas Reutterer, 2006. "A Combined Approach for Segment-Specific Analysis of Market Basket Data," SFB 649 Discussion Papers SFB649DP2006-006, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2006-006
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    References listed on IDEAS

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    1. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    2. Evgenia Dimitriadou & Sara Dolničar & Andreas Weingessel, 2002. "An examination of indexes for determining the number of clusters in binary data sets," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 137-159, March.
    3. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
    4. 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.
    5. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    6. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    7. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    8. Andrew Ainslie & Peter E. Rossi, 1998. "Similarities in Choice Behavior Across Product Categories," Marketing Science, INFORMS, vol. 17(2), pages 91-106.
    9. Yasemin Boztug & Lutz Hildebrandt, 2005. "A Market Basket Analysis Conducted with a Multivariate Logit Model," SFB 649 Discussion Papers SFB649DP2005-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    10. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, December.
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    More about this item

    Keywords

    Marketing; Choice Models; Market Basket Analysis; Cross-Category Effects; Segmentation;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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