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Consumer Segmentation and Profiling using Demographic Data and Spending Habits Obtained through Daily Mobile Conversations

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Samuel W. Kamande, Evans A. K. Miriti, Emmanuel Ahishakiye
10.5120/ijca2018917609

Samuel W Kamande, Evans A K Miriti and Emmanuel Ahishakiye. Consumer Segmentation and Profiling using Demographic Data and Spending Habits Obtained through Daily Mobile Conversations. International Journal of Computer Applications 181(9):33-42, August 2018. BibTeX

@article{10.5120/ijca2018917609,
	author = {Samuel W. Kamande and Evans A. K. Miriti and Emmanuel Ahishakiye},
	title = {Consumer Segmentation and Profiling using Demographic Data and Spending Habits Obtained through Daily Mobile Conversations},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {9},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {33-42},
	numpages = {10},
	url = {http://www.ijcaonline.org/archives/volume181/number9/29802-2018917609},
	doi = {10.5120/ijca2018917609},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Knowledge of customer behaviour helps organizations to continuously re-evaluate their strategies with the consumers and plan to improve and expand their application of the most effective strategies. Using expenditure data collected through daily mobile conversations with consumers in Kenya, this study sought to compare various clustering algorithms and establish one that best segments consumers, and subsequently providing profiles that provide a basis for marketing and brand strategy based on existing demographic data – age, gender, region and primary income source. K-Means, Hierarchical and Partitioning around Medoids (PAM) clustering algorithms were compared using internal and stability validation tests. Hierarchical clustering with four clusters had the best Connectivity (0.847) and Silhouette width (0.924) measures. Stability validation compares the results by removing a column, one at a time. Average Proportion of Non-overlap (APN), Average Distance (AD), Average Distance Between Means (AND) and Figure of Merit (FOM) were used to compare the algorithms. Again, Hierarchical clustering with four clusters was found to partition the data best. The study forms a basis for the use of additional profile descriptors once available to provide a firmer understanding of the customer segments built on expenditure data in Kenya.

References

  1. Hosseini, Monireh; Shabani, Mostafa. New approach to customer segmentation based on changes in customer value. Journal of Marketing Analytics, Volume 3, Number 3, 1 September 2015, pp. 110-121(12).
  2. Central Bank of Kenya, Kenya National Bureau of Statistics & FSD Kenya. (2016). The 2016 FinAccess Household Survey on financial inclusion. Nairobi, Kenya: FSD Kenya.
  3. Pedro QuelhasBrito, CarlosSoares, SérgioAlmeida, Ana Monte, Michel Byvoet (2015) Customer segmentation in a large database of an online customized fashion business 36, 93-100.
  4. Ozer, M. (2001) User segmentation of online music services using fuzzy clustering, Omega, 29, 193–206.
  5. Anderson, E.W., C. Fornell And S.K. Mazvancheryl (2004) Customer satisfaction and shareholder value, Journal of Marketing, 68, 172–185.
  6. White, C. and Y.T. YU (2005) Satisfaction emotions and consumer behavioural intentions, Journal of Services Marketing, 19, 411–420.
  7. Chang, H.H. and P.W. KU (2009) Implementation of relationship quality for CRM performance: acquisition of BPR and organisational learning, Total Quality Management & Business Excellence, 20, 327–348.
  8. Baines, P.; Bailey, C.; Wilson, H. and Clarke, M. (2009), “Segmentation and customer insight in contemporary services marketing practice: why grouping customers is no longer enough”, Journal of Marketing Management, Vol.25, No.3/4, pp.227-252.
  9. Dibb, S. and Simkin, L. (1997), “A program for implementing market segmentation”, Journal of Business and Industrial Marketing, Vol.12, No.1, pp.51-66.
  10. Dibb, S. and Wensley, R. (2002), “Segmentation analysis for industrial markets: problems of integrating customer requirements into operations strategy”, European Journal of Marketing, Vol.36, No.1/2, pp.231-251.
  11. Laiderman, J. (2005), “A structured approach to B2B segmentation”, Database Marketing and Customer Strategy Management, Vol.13, No.1, pp.64.75.
  12. McDonald, M. and Dunbar, I. (2005) Market segmentation. Butterworth Heinemann, Oxford.
  13. Stevens (1951): "Mathematics, measurement, and psychophysics." In S.S. Stevens (ed.): Handbook of experimental psychology. New York: Wiley.
  14. Castellan, N. J. (1975). The modern minicomputer in laboratory automation. American Psychologist, 30(3), 205-211. http://dx.doi.org/10.1037/0003-066X.30.3.
  15. James N. (Ed) Butcher. Computerized psychological assessment: A practitioner's guide. January 1987.
  16. Michael H. Birnbaum. Psychological Experiments on the Internet. February 2003. DOI: 10.1016/B978-012099980-4/50001-0.
  17. Mattison, R., Data Warehousing and Data Mining for Telecommunications. Boston, London: Artech House, (1997).
  18. Weiss, G.M., Data Mining in Telecommunications. The Data Mining and Knowledge Discovery Handbook (2005), pp. 1189-1201.

Keywords

Customer segmentation, clustering, clustering algorithms.