• ISSN: 2010-023X (Print)
    • Abbreviated Title: Int. J. Trade, Economics and Financ.
    • Frequency: Quaterly
    • DOI: 10.18178/IJTEF
    • Editor-in-Chief: Prof.Tung-Zong (Donald) Chang
    • Managing Editor: Ms. Shira. W. Lu
    • Abstracting/ Indexing:  Crossref, Electronic Journals Library , EBSCO
    • E-mail: ijtef.editorial.office@gmail.com
HOME > Archive > 2022 > Volume 13 Number 5 (Oct. 2022) >
IJTEF 2022 Vol.13(5): 168-173 ISSN: 2010-023X
DOI: 10.18178/ijtef.2022.13.5.741

Improving Marketing Efficiency in the Retail Bicycle Industry through Geospatial Segmentation

Mitchell C. Beckner, Ross A. Jackson, and Kevin S. Steidel

Abstract— Developments in technology and communications have placed more emphasis on the demand side of consumer markets due to the fact that potential customers now have the ability to conduct in-depth research and purchase products from a larger number of sources. Market segmentation is a technique that has been commonly used to improve the understanding of customer values and to maximize business resources. However, traditional methods are often heavily dependent on large amounts of historical data which many small to medium businesses do not have. This project’s objective was the creation of a geospatial customer segmentation model that could be used to increase the effectiveness of marketing and advertising funds in the retail bicycle industry without reliance on historical customer purchase data. U.S. Census data at the census block level was used with K-means clustering to produce primary segments that were then further divided into subclusters in a divisive, hierarchical manner. These segments were evaluated in order to determine the characteristics and decisive buying criteria of each group. Individual businesses can then prioritize these segments and develop specific marketing strategies based on the segment characteristics and the particular business objectives. If customer data is available or collected going forward, that data can be merged with the segmentation model using the Census Bureau’s geocoding API.

Index Terms—Cluster analysis, data mining, geospatial, market segmentation.

The authors are with Wittenberg University, Springfield, OH 45504 USA (e-mail: becknerm@wittenberg.edu, jacksonr@wittenberg.edu, steidelk@wittenberg.edu).

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Cite: Mitchell C. Beckner, Ross A. Jackson, and Kevin S. Steidel, "Improving Marketing Efficiency in the Retail Bicycle  Industry through Geospatial Segmentation," International Journal of Trade, Economics and Finance vol.13, no.5, pp. 168-173, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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