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Estimating Cannibalization Rates for Pioneering Innovations

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  • Harald J. van Heerde

    (Waikato Management School, University of Waikato, Hamilton 3240, New Zealand; and CentER, Tilburg University, 5000 LE Tilburg, The Netherlands)

  • Shuba Srinivasan

    (Boston University School of Management, Boston University, Boston, Massachusetts 02215)

  • Marnik G. Dekimpe

    (Tilburg University, 5000 LE Tilburg, The Netherlands; and Catholic University Leuven, 3000 Leuven, Belgium)

Abstract

To evaluate the success of a new product, managers need to determine how much of its new demand is due to cannibalizing the firm's other products, rather than drawing from competition or generating primary demand. We introduce a time-varying vector error-correction model to decompose the base sales of a new product into its constituent sources. The model allows managers to estimate cannibalization effects and calculate the new product's net demand, which may be considerably less than its total demand. We apply our methodology to the introduction of the Lexus RX300 using detailed car transaction data. This case is especially interesting because the Lexus RX300 was the first crossover sport utility vehicle (SUV), implying that its demand could come from both the luxury SUV and the luxury sedan categories. Because Lexus was active in both categories, there was a double cannibalization potential. We show how the contribution of the different demand sources varies over time and discuss the managerial implications for both the focal brand and its competitors.

Suggested Citation

  • Harald J. van Heerde & Shuba Srinivasan & Marnik G. Dekimpe, 2010. "Estimating Cannibalization Rates for Pioneering Innovations," Marketing Science, INFORMS, vol. 29(6), pages 1024-1039, 11-12.
    • Handle: RePEc:inm:ormksc:v:29:y:2010:i:6:p:1024-1039
    DOI: 10.1287/mksc.1100.0575
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    4. Bernhard Dachs & Martin Hud & Christian Koehler & Bettina Peters, 2017. "Innovation, creative destruction and structural change: firm-level evidence from European countries," Industry and Innovation, Taylor & Francis Journals, vol. 24(4), pages 346-381, May.
    5. Guissoni, Leandro Angotti & Rodrigues, Jonny Mateus & Zambaldi, Felipe & Neves, Marcos Fava, 2021. "Distribution effectiveness through full- and self-service channels under economic fluctuations in an emerging market," Journal of Retailing, Elsevier, vol. 97(4), pages 545-560.
    6. Csilla Horváth & Dennis Fok, 2013. "Moderating Factors of Immediate, Gross, and Net Cross-Brand Effects of Price Promotions," Marketing Science, INFORMS, vol. 32(1), pages 127-152, July.
    7. Becker, Maren & Gijsenberg, Maarten J., 2023. "Consistency and commonality in advertising content: Helping or Hurting?," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 128-145.
    8. Rémy Garnier, 2022. "Concurrent neural network: a model of competition between times series," Annals of Operations Research, Springer, vol. 313(2), pages 945-964, June.
    9. Gielens, Katrijn & Gijsbrechts, Els & Dekimpe, Marnik G., 2014. "Gains and losses of exclusivity in grocery retailing," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 239-252.
    10. Michelle Haynes & Steve Thompson & Peter W. Wright, 2014. "New Model Introductions, Cannibalization and Market Stealing: Evidence from Shopbot Data," Manchester School, University of Manchester, vol. 82(4), pages 385-408, July.
    11. Kathleen Cleeren & Marnik G. Dekimpe & Harald J. Heerde, 2017. "Marketing research on product-harm crises: a review, managerial implications, and an agenda for future research," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 593-615, September.

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