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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Romano, C., Ratnatunga, J.: The role of marketing: its impact on small enterprise research. Eur. J. Market. 29, 9–30 (1995)
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)
Kotler, P.: Marketing Management. Prentice-Hall, Upper Saddle River (2000)
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)
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)
Singh, J., Sirdeshmukh, D.: Agency and trust mechanisms in relational exchange. J. Market. 66, 5–37 (2000)
Venkateswaran, R.: A customer satisfied in not a customer retained. Indian Inst. Manage. Bangalore Manage. Rev. 3, 120–130 (2003)
Kandampully, J.: Service quality to service loyalty: a relationship which goes beyond customer services. Total Qual. Manage. 9, 431–443 (1998)
Bond, A., Foss, B., Patron, M.: Consumer Insight: How to Use Data e Market Research to Get Closer to Your Customer. Kogen, London (2004)
Detlev, Z., Dholakia, N.: Whose identity is it anyway? Consumer representation in the age of database marketing. J. Macromarket. 24(1), 31–43 (2004)
Hughes, A.: Strategic Database Marketing. Probus Publishing Company, Chicago (1994)
Gama, M.: Database marketing, age-old customer savvy gets an algorithmic boost. Medical Industry Information Report (1997)
Tucker, M.: Fresh dough. Datamation (1997)
Fletcher, K., Deans, K.: The structure and content of the marketing information system: a guide for management. Market. Intell. Plan. 6, 27–35 (1998)
Cross, R., Janet, S.: Retailers move toward new customer relations. Direct Market. J. 57, 20–22 (2004)
Chan, C.: Online auction customer segmentation using a neural network model. Int. J. Appl. Sci. Eng. 3, 101–109 (2005)
McCarty, J., Hastak, M.: Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression. J. Bus. Res. 60, 656–662 (2007)
Fader, P., Hardie, B., Lee, K.: RFM and CLV: using iso-value curves for customer base analysis. J. Market. Res. 42, 415–430 (2005)
Colombo, R., Weina, J.: A stochastic RFM model. J. Interact. Market. 13, 2–12 (1999)
Yeh, I., Yang, K.J., Ting, T.M.: Knowledge discovery on RFM model using Bernoulli sequence. Expert Syst. Appl. 36, 5866–5871 (2009)
Wang, C.H.: Apply robust segmentation to the service industry using kernel induced fuzzy clustering techniques. Expert Syst. Appl. 37, 8395–8400 (2010)
Hughes, A.M.: Strategic Database Marketing. McGraw–Hill, Chicago (2000)
Venkatesan, R., Kumar, V.: A customer lifetime value framework for customer selection and resource allocation strategy. J. Market. 68, 106–125 (2004)
Lai, P., So, F., Chan, K.: Spatial Epidemiological Approaches in Disease Mapping and Analysis. CRC Press, Boca Raton (2008)
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)
QGIS: QGIS - The Leading Open Source Desktop GIS. http://www.qgis.org/
gvSIG: gvSIG: Technologies and open source software solutions for working with geographic data. In: gvSIG Association. http://www.gvsig.com/en/products
The University of Chicago: Software of the Center for Spatial Data Science. Geoda. https://spatial.uchicago.edu/software
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Vitor, J.A. et al. (2018). Inexpensive Marketing Tools for SMEs. In: Rocha, Á., Guarda, T. (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-319-73450-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-73450-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73449-1
Online ISBN: 978-3-319-73450-7
eBook Packages: EngineeringEngineering (R0)