Fadhilah S. Minako
M. Mujiya Ulkhaq
ABSTRACT
The objective of this research is to do market segmentation of internet shoppers based on internet psychographics. There are thirty-eight indicators from six criteria, i.e., "internet shopping is easy and fun", "internet shopping is a hassle", "I don't know how", "fear of financial theft", "like the energy of brick-and-mortar stores", and "internet has good prices and quality". To do clustering, k-means cluster analysis was employed. Result shows that there are eight segments of internet shoppers, namely, shopping lovers, adventuresome explorers, business users, coward shoppers, suspicious learners, fun seekers, technology muddlers, and shopping avoiders. The first five segments are considered to be more likely to purchase products online, i.e., the online shoppers; while the rest three are the non-online shoppers. The ANOVA test confirmed that the eight segments were appropriate since it created more differentiated and consistent clusters. This research is expected to give a contribution both to the theoretical and empirical literature on customer segmentation where different marketing strategies could be generated for each segment.
- Internet Live Stats. 2018. Available at: http://www.internetlivestats.com/internet-users/Google Scholar
- Ulkhaq, M. M., Wijayanti, W. R., Kusumawati, A., Aulia, F. S., Wijayanti, R. S., and Wiganingrum, R. 2017. Combining the eTransQual scale and importance-performance analysis to assess service quality of online shopping. In Proceedings of the International Conference on Industrial Engineering and Applications (Nagoya, Japan, April 21--23, 2017). ICIEA '17. IEEE, 146--150.Google Scholar
- Kotler, P., Armstrong, G., and Wong, V. 1996. Principles of Marketing. Prentice Hall, London.Google Scholar
- González-Hernández, E. M. and Orozco-Gómez, M. 2012. A segmentation of Mexican consumers based on shopping centre attractiveness. Int. J. Retail Dist. Manag., 40, 10, 759--777.Google ScholarCross Ref
- El-Adly, M. I. 2007. Shopping malls attractiveness: a segmentation approach. Int. J. Retail Dist. M., 35, 11, 936--950.Google ScholarCross Ref
- Gilboa, S. 2009. A segmentation study of Israeli mall customers. J. Retailing and Consumer Services, 16, 2 (March 2009), 135--144.Google ScholarCross Ref
- Cho, M., Bonn, M. A., and Brymer, R. A. 2017. A Constraint-Based Approach to Wine Tourism Market Segmentation. J. Hosp. Tour. Res., 41, 4, 415--444.Google ScholarCross Ref
- Iversen, N. M., Hem, L. E., and Mehmetoglu, M. 2016. Lifestyle segmentation of tourists seeking nature-based experiences: the role of cultural values and travel motives. J. Travel. Tour. Mark., 33, sup1, 38--66.Google ScholarCross Ref
- Barr, S., Shaw, G., Coles, T. and Prillwitz, J. 2010. 'A holiday is a holiday': practicing sustainability, home and away. J. Transp. Geogr., 18, 3, 474--481.Google ScholarCross Ref
- Sarki, I., Bhutto, N., and Arshad, I. 2012. Impact of Pakistani university student's cultural values and lifestyles on meaning of brands. Interdiscip. J. Contemp. Res. Bus., 3, 9, 643--654.Google Scholar
- Swinyard, W. R. and Smith, S. M. 2003. Why people (don't) shop online: A lifestyle study of the internet consumer. Psychology & Marketing 20, 7, 567--597.Google ScholarCross Ref
- Kementerian Komunikasi dan Informatika Republik Indonesia. 2018. Jumlah pengguna internet 2017 meningkat, Kominfo terus lakukan percepatan pembangunan broadband {in Indonesia}. Available at: https://www.kominfo.go.id/content/detail/12640/siaran-pers-no-53hmkominfo022018-tentang-jumlah-pengguna-internet-2017-meningkat-kominfo-terus-lakukan-percepatan-pembangunan-broadband/0/siaran_pers.Google Scholar
- Statista. 2018. Retail e-commerce sales in Indonesia from 2015 to 2021 (in billion U.S. dollars). Available at: https://www.statista.com/statistics/280925/b2c-e-commerce-sales-in- indonesia/.Google Scholar
- Dhanachandra, N., Manglem, K., and Chanu, Y. J. 2015. Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Computer Science, 54, 764--771.Google ScholarCross Ref
- Wu, J., Liu, H., Xiong, H., Cao, J., and Chen, J. 2015. K-means-based consensus clustering: A unified view. IEEE Transactions on Knowledge and Data Engineering, 27, 1, 155--169.Google ScholarCross Ref
- Cohen, M. B., Elder, S., Musco, C., Musco, C., and Persu, M. 2015. Dimensionality reduction for k-means clustering and low rank approximation. In Proceedings of the forty-seventh Annual ACM Symposium on Theory of Computing (June, 2015). STOC '15. ACM, New York, 163--172.Google Scholar
- Griva, A., Bardaki, C., Pramatari, K., and Papakiriakopoulos, D. 2018. Retail business analytics: Customer visit segmentation using market basket data. Expert Syst. Appl., 100, 1--16. Google ScholarDigital Library
- Kabadayi, S. and Paksoy, B. A. 2016. A segmentation of Turkish consumers based on their motives to visit shopping centers. The International Review of Retail, Distribution and Consumer Research, 26, 4, 456--476.Google ScholarCross Ref
Index Terms
- Clustering Internet Shoppers: An Empirical Finding from Indonesia
Recommendations
Segmentation of Cinema Audiences: An Empirical Finding from Indonesia
DSDE 2019: Proceedings of the 2019 2nd International Conference on Data Storage and Data EngineeringThe swift changes that undergo in present-day society are leading to an increasing personalization in consumer behavior patterns. This effect seems to be a particularly well-reflected cinema in a shopping center. As one of the main characteristics of ...
A profile of the Internet shoppers
We studied the Internet shoppers profile from Malaysia, Singapore and Taiwan.This study formed a comparison of the Internet shoppers' profile of nine countries.The similarities and differences of Internet shoppers profile are presented.Asian Internet ...
Online Buying Impulsiveness and Word-of-Mouth Online in the E-Marketing Age: A Theoretical Model Approach
ICMECG '10: Proceedings of the 2010 International Conference on Management of e-Commerce and e-GovernmentImpulse buying represents consumers’ basic psychological trait and habit. Online buying impulsiveness is the tendency of consumers’ reaction toward stimuli in the Internet environment. In the Internet environment, Word-of-Mouth online is one stimuli ...
Comments