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Inexpensive Marketing Tools for SMEs

  • José Avelino Vitor
  • Teresa Guarda
  • Maria Fernanda Augusto
  • Marcelo Leon
  • Datzania Villao
  • Luis Mazon
  • Yovany Salazar Estrada
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 721)

Abstract

Today small and medium-sized enterprises (SMEs) play a key role in the economy and are considered the engines of global economic growth. In today’s environment of mature economies, stagnant markets and fierce competition, consumers are increasingly informed and demanding personalized treatment and products and services that meet their needs. In this context, SMEs can remain in the market, and maintain a competitive advantage, if they are able to respond to customers’ needs in a timely manner. That is possible if supported by the appropriate information systems and information technologies. Actually, many SMEs are far from accessing all the available data, because they have neither the knowledge nor financial capacity to acquire tools that allow you to extract knowledge from your internal and external databases. However, is possible by combining a database that provides behavioral information from your prospects and combining that data with the spatial information of those customers. This joint allows a comprehensive analysis that is possible through the use of segmentations techniques, which supports marketing campaigns in an effective way, promoting visibility in the market, and allowing acquiring or maintaining a strategic positioning, using inexpensive tools.

Keywords

Competitive advantage Database marketing GeoMarketing RFM model Costumer segmentation 

References

  1. 1.
    Romano, C., Ratnatunga, J.: The role of marketing: its impact on small enterprise research. Eur. J. Market. 29, 9–30 (1995)CrossRefGoogle Scholar
  2. 2.
    Culkin, N., Smith, D.: An emotional business: a guide to understanding the motivations of small business decision takers. Qual. Market Res. Int. J. 3, 145–157 (2000)CrossRefGoogle Scholar
  3. 3.
    Kotler, P.: Marketing Management. Prentice-Hall, Upper Saddle River (2000)Google Scholar
  4. 4.
    Coviello, N., Brodie, R., Danaher, P., Johnston, W.J.: How firms relate to their markets: an empirical examination of contemporary marketing practices. J. Market. 66, 33–46 (2002)CrossRefGoogle Scholar
  5. 5.
    Brodie, R., Winklhofer, H., Coviello, N., Johnston, W.: Is e-marketing coming of age? An examination of the penetration of e-marketing and firm performance. J. Interact. Market. 21, 2–21 (2007)CrossRefGoogle Scholar
  6. 6.
    Singh, J., Sirdeshmukh, D.: Agency and trust mechanisms in relational exchange. J. Market. 66, 5–37 (2000)Google Scholar
  7. 7.
    Venkateswaran, R.: A customer satisfied in not a customer retained. Indian Inst. Manage. Bangalore Manage. Rev. 3, 120–130 (2003)Google Scholar
  8. 8.
    Kandampully, J.: Service quality to service loyalty: a relationship which goes beyond customer services. Total Qual. Manage. 9, 431–443 (1998)CrossRefGoogle Scholar
  9. 9.
    Bond, A., Foss, B., Patron, M.: Consumer Insight: How to Use Data e Market Research to Get Closer to Your Customer. Kogen, London (2004)Google Scholar
  10. 10.
    Detlev, Z., Dholakia, N.: Whose identity is it anyway? Consumer representation in the age of database marketing. J. Macromarket. 24(1), 31–43 (2004)CrossRefGoogle Scholar
  11. 11.
    Hughes, A.: Strategic Database Marketing. Probus Publishing Company, Chicago (1994)Google Scholar
  12. 12.
    Gama, M.: Database marketing, age-old customer savvy gets an algorithmic boost. Medical Industry Information Report (1997)Google Scholar
  13. 13.
    Tucker, M.: Fresh dough. Datamation (1997)Google Scholar
  14. 14.
    Fletcher, K., Deans, K.: The structure and content of the marketing information system: a guide for management. Market. Intell. Plan. 6, 27–35 (1998)CrossRefGoogle Scholar
  15. 15.
    Cross, R., Janet, S.: Retailers move toward new customer relations. Direct Market. J. 57, 20–22 (2004)Google Scholar
  16. 16.
    Chan, C.: Online auction customer segmentation using a neural network model. Int. J. Appl. Sci. Eng. 3, 101–109 (2005)Google Scholar
  17. 17.
    McCarty, J., Hastak, M.: Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J. Bus. Res. 60, 656–662 (2007)CrossRefGoogle Scholar
  18. 18.
    Fader, P., Hardie, B., Lee, K.: RFM and CLV: using iso-value curves for customer base analysis. J. Market. Res. 42, 415–430 (2005)CrossRefGoogle Scholar
  19. 19.
    Colombo, R., Weina, J.: A stochastic RFM model. J. Interact. Market. 13, 2–12 (1999)CrossRefGoogle Scholar
  20. 20.
    Yeh, I., Yang, K.J., Ting, T.M.: Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst. Appl. 36, 5866–5871 (2009)CrossRefGoogle Scholar
  21. 21.
    Wang, C.H.: Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques. Expert Syst. Appl. 37, 8395–8400 (2010)CrossRefGoogle Scholar
  22. 22.
    Hughes, A.M.: Strategic Database Marketing. McGraw–Hill, Chicago (2000)Google Scholar
  23. 23.
    Venkatesan, R., Kumar, V.: A customer lifetime value framework for customer selection and resource allocation strategy. J. Market. 68, 106–125 (2004)CrossRefGoogle Scholar
  24. 24.
    Lai, P., So, F., Chan, K.: Spatial Epidemiological Approaches in Disease Mapping and Analysis. CRC Press, Boca Raton (2008)Google Scholar
  25. 25.
    Mennecke, B.: Understanding the role of geographic information technologies in business: applications and research directions. J. Geogr. Inf. Decis. Anal. 1(1), 44–68 (1997)Google Scholar
  26. 26.
    QGIS: QGIS - The Leading Open Source Desktop GIS. http://www.qgis.org/
  27. 27.
    gvSIG: gvSIG: Technologies and open source software solutions for working with geographic data. In: gvSIG Association. http://www.gvsig.com/en/products
  28. 28.
    The University of Chicago: Software of the Center for Spatial Data Science. Geoda. https://spatial.uchicago.edu/software

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • José Avelino Vitor
    • 1
    • 2
  • Teresa Guarda
    • 3
    • 4
    • 5
  • Maria Fernanda Augusto
    • 3
  • Marcelo Leon
    • 3
    • 4
  • Datzania Villao
    • 4
  • Luis Mazon
    • 4
  • Yovany Salazar Estrada
    • 6
  1. 1.Instituto Universitário da MaiaMaiaPortugal
  2. 2.Instituto Politécnico da MaiaMaiaPortugal
  3. 3.Universidad de las Fuerzas Armadas-ESPESangolquiEcuador
  4. 4.Universidad Estatal Península de Santa Elena – UPSELa LibertadEcuador
  5. 5.Algoritmi CentreMinho UniversityBragaPortugal
  6. 6.Universidad Nacional de LojaLojaEcuador

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