The acquisition of consumer behaviour data using integrated indoor positioning systems

• Authors: Bodziak, B. , Steć, B.
• Languages of publication: EN

Abstracts

EN

In this article, the authors have introduced trends in developing technologies used for analysing consumer behaviour and supporting marketing actions in retail networks focusing on using data in marketing information systems. The authors have proposed a developed model of a system which integrates separate technology groups such as POS, computer video analysis, social media data and the indoor navigation system based on beacon and radio tomography in one consistent solution in order to achieve the synergy effect in the area of customer behaviour analysis and behavioural targeting. The model can be developed by other technologies which gather unique data about customers.

Keywords

EN

customer behaviour; IoT; big data

PL

zachowania klientów; IoT; Big Data

• Journal: Organizacja i Zarządzanie : kwartalnik naukowy
• Year: 2017
• Volume: nr 3
• Pages: 21--32
• Physical description: Bibliogr. 22 poz.

Contributors

author

Bodziak, B.

• UMSC in Lublin, Department of Market Analysis, Lublin

author

Steć, B.

• UMSC in Lublin, Scientific Circle of Quality and Knowledge Management, Lublin

References

• 1. Ailawadi, K.L., Harlam B.A., Cesar J., and Trounce D. (2006). Promotion profitability for a retailer: the role of promotion, brand, category, and store characteristics. Journal of Marketing Research, 43(4), pp. 518-535. doi: 10.1509/jmkr.43.4.518.
• 2. Connell, J., Fan, Q., Gabbur, P., Haas, N., Pankanti, S., and Trinh, H. (2013). Retail video analytics: an overview and survey. Proceedings of the SPIE Video surveillance and transportation imaging applications, 8663. doi: 10.1117/12.2008899.
• 3. Dudhane, N.A., and Pitambare, S.T. (2015). Location Based and Contextual Services Using Bluetooth Beacons: New Way to Enhance Customer Experience. Lecture Notes on Information Theory, 3(1), 31-34. doi: 10.18178/lnit.3.1.31-34.
• 4. Engel, J.F., Blackwell, R.D., and Miliard, P.W. (1993). Consumer behavior. Chicago: The Dryden Press.
• 5. Free 2016 Global Mobile Market Report. (2016, Dec. 12). Available online https://newzoo.com/insights/trend-reports/2016-global-mobile-market-report/.
• 6. Haller, S., Karnouskos, S., and Schroth, C. (2009). The Internet of Things in an Enterprise Context. In J. Domingue, D. Fensel, and P. Traverso (Eds.), First Future Internet Symposium – FIS 2008. Lecture Notes in Computer Science, 5468. Berlin, Heidelberg: Springer.
• 7. Hanse, F. (1972). Consumer choice behavior: A cognitive theory. New York: The Free Press.
• 8. Jobber, D. (1997). Marketing information systems in United States and British industry. Management Decision, 15(2), 297-304. doi: 10.1108/eb001131.
• 9. Karunarathna, K.M.D.M., Weerasingha, H.M.D.A., Rumy, M.M., Rajapaksha, M.M., De Silva, D.I., and Kodagoda, N. (2014). A Fully Functional Shopping Mall Application – SHOPPING EYE. Proceedings of 2nd International Conference on Artificial Intelligence, Modelling and Simulation 2014, pp. 292-296. doi: 10.1109/AIMS.2014.14.
• 10. Larson, J.S., Bradlow, E.T., and Fader P.S. (2005). An exploratory look at supermarket shopping paths. International Journal of Research in Marketing, 22(4), pp. 395-414. doi: 10.1016/j.ijresmar.2005.09.005.
• 11. Liciotti, D., Contigiani, M., Frontoni, E., Mancini, A., Zingaretti, P., and Placidi V. (2014). Shopper analytics: A customer activity recognition system using a distributed RGB-D camera network. In C. Distante, S. Battiato, and A. Cavallaro (Eds.), Video Analytics for Audience Measurement. VAAM 2014. Lecture Notes in Computer Science, 8811, pp. 146-157. doi: 10.1007/978-3-319-12811-5_11.
• 12. Marciniak, B. (2011). Marketing Decisions Corporate Support Systems. Studies and works of the Collegium of Management and Finance Warsaw School of Economics, 110, pp. 51-68.
• 13. Murthy, D.N., and Kumar, B.V. (2015). Internet of Things (IoT): Is IoT a Disruptive Technology or a Disruptive Business Model? Indian Journal of Marketing, 45(8), pp. 18-27. doi: 10.17010/ijom/2015/v45/i8/79915.
• 14. Osman, M.Z. (1993). A conceptual model of retail image influences on loyalty patronage behaviour. International review of retail, distribution and consumer research, 3(2), pp. 133-148. doi: 10.1080/09593969300000011.
• 15. Pawełoszek, I., and Wieczorkowski, J. (2015). Big data as a business opportunity: an Educational Perspective. Proceeding of the 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 1563-1568. doi: 10.15439/2015F365.
• 16. Płoszajski, P. (2013). Big Data: nowe źródło przewag i wzrostu firm [The Big Data: a new source of companies' advantages and growth]. E-mentor, 50(3), pp. 5-10.
• 17. Popa, M.C., Gritti, T., Rothkrantz, L.J., Shan, C., and Wiggers, P. (2011). Detecting customers’ buying events on a real-life database. In P. Real, D. Diaz-Pernil, H. Molina-Abril, A. Berciano, and W. Kropatsch (Eds.), Computer Analysis of Images and Patterns. Lecture Notes in Computer Science, 6854, pp. 17-25. doi: 10.1007/978-3-642-23672-3_3.
• 18. Roberts, J.H., and Lilien, G.L. (1993). Explanatory and predictive models of consumer behaviour. Handbooks in Operations Research and Management Science, 5, pp. 27-82. doi: 10.1016/S0927-0507(05)80025-8.
• 19. Rymarczyk, T., Adamkiewicz, P., Szumowski, J., Niderla, K., Gołąbek, Ł., and Sikora, J. (2017). Concept of detection system to localize inside closed area by radio tomographic imaging. Informatics Control Measurement in Economy and Environment Protection, 7(1), pp. 92-95. doi: 10.5604/01.3001.0010.4592.
• 20. Schiffman, L.G., and Kanuk, L.L. (1984). Consumer behaviour. New Jersey: Prentice Hall.
• 21. Trejderowski, T. (2009). Social engineering: Basics of manipulation in practice. Warszawa: ENETEIA Psychology and Culture Publishing.
• 22. Wilson, J., and Patwari, N. (2010). Radio tomographic imaging with wireless networks. IEEE Transactions on Mobile Computing, 9(5), pp. 621-632. doi: 10.1109/ TMC.2009.174.

Notes

Rekord pochodzi z bazy danych BazTech.

Identifiers

DOI

10.29119/1899-6116.2017.39.2