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Estimating Cannibalization Rates for Pioneering Innovations. Harald van Heerde (University of Waikato)* Shuba Srinivasan (Boston University) Marnik Dekimpe (Tilburg University & KU Leuven). Marketing Science-to-Practice (S2P) 2012.

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estimating cannibalization rates for pioneering innovations

Estimating Cannibalization Rates for Pioneering Innovations

Harald van Heerde (University of Waikato)*

Shuba Srinivasan (Boston University)

Marnik Dekimpe (Tilburg University & KU Leuven)

Marketing Science-to-Practice (S2P) 2012

*Van Heerde, Harald J., ShubaSrinivasan, and Marnik G. Dekimpe (2010), “Estimating Cannibalization Rates for Pioneering Innovations,” Marketing Science, 29 (6), 1024-1039.

  • Late Steve Jobs, when introducing the iPhone:

“If anyone is going to cannibalize us, I want it to be us. I don’t want it to be a competitor.”

  • Need to quantify the cannibalization effect.
problem statement
  • Evaluate new product:
    • how much new demand
    • decomposition of demand
  • Especially difficult for radical innovations, which tend to operate on the boundary of multiple categories.
  • New product may draw from same-company products (cannibalization) or from other-company products (brand switching), possibly involving category switching, and, expand primary demand.
proposed approach
  • Allows for decomposition percentages changing over time.
  • Includes short- and long-term effects.
  • Controls for marketing mix effects.
  • Handles missing data for cross-effects due to product unavailability.
  • Time-varying Vector Error Correction Model
    • Vector Error Correction (Fok et al. 2006)
    • Dynamic Linear Model (Van Heerde et al. 2004)
    • Bayesian estimation only updates when data are available.
  • Lexus RX 300 Introduction in the US.
  • Radical innovation: first cross-over SUV.
  • Potential to draw from both existing SUVs and luxury sedans.
  • Weekly transaction data from JD Power and Associates including sales volume, prices, etc.
  • Categories: luxury SUV, luxury sedans.
  • Weekly advertising data from TNS Media.

Primary demand effect (36%)

Primary demand

Within-category cannibalization (0%)

Total Demand 100%

Within category

Secondary demand

Within-category brand switching (21%)

Between-category cannibalization (26%)

More research is needed for empirical generalizations

Between categories

Between-category brand switching (16%)

managerial implications
Managerial Implications
  • Focal brand introducing radical innovation: tool to evaluate net demand, accounting for within- and across-category cannibalization.
    • This case: cannibalization effect is 26%, so net effect 74%
  • Competitor: tool to assess the extent to which their products (across different categories) are affected and how they may recoup potential losses.
  • Industry: tool to assess to what extent innovations grow primary demand.