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Note—Estimating Geographic Customer Densities Using Kernel Density Estimation

Author

Listed:
  • Naveen Donthu

    (Georgia Institute of Technology)

  • Roland T. Rust

    (University of Texas at Austin)

Abstract

This paper shows how kernel density estimation may be used to estimate flexibly the geographic distribution of customers in a market. In addition it shows how a density-based product positioning methodology may be applied to site selection, using the estimated geographic customer density to help locate a new or (relocated) store or distribution center. This application provides a conceptual basis for more complicated site selection and spatial demand models which might involve several predictor variables.

Suggested Citation

  • Naveen Donthu & Roland T. Rust, 1989. "Note—Estimating Geographic Customer Densities Using Kernel Density Estimation," Marketing Science, INFORMS, vol. 8(2), pages 191-203.
    • Handle: RePEc:inm:ormksc:v:8:y:1989:i:2:p:191-203
    DOI: 10.1287/mksc.8.2.191
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    Citations

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    Cited by:

    1. Wolfgang Jank & P. K. Kannan, 2005. "Understanding Geographical Markets of Online Firms Using Spatial Models of Customer Choice," Marketing Science, INFORMS, vol. 24(4), pages 623-634, December.
    2. Abdullah Dasci & Gilbert Laporte, 2005. "A Continuous Model for Multistore Competitive Location," Operations Research, INFORMS, vol. 53(2), pages 263-280, April.
    3. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    4. Richard Francis & Timothy Lowe, 2014. "Comparative error bound theory for three location models: continuous demand versus discrete demand," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 144-169, April.
    5. Moon, Sangkil & Azizi, Kathryn, 2013. "Finding Donors by Relationship Fundraising," Journal of Interactive Marketing, Elsevier, vol. 27(2), pages 112-129.

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