SIGNIFICANCE OF ANALYTICS
Abstract
Despite the number of times marketers have been using analytics as a tool to predict and drive consumer behavior, it is a relatively new application of the science. Technology continues to evolve and offers even more data choices and metrics for analysis, increasing the abilities of marketers to reach their audience. This article expands on several sectors of use, including real estate, social media, and healthcare, and theorizes the impact that analytics will have in the future as the technological means to interpret data catches up with the sheer amount of real-time information available for potential use, especially with the development of the Internet of Things, and rising concerns around data use, regarding data protection and copyright.
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Abdallah, M., Muhairat, M., Althunibat, A., & Abdalla, A. (2020). big data quality factors, frameworks and challenges. CompuSoft: An International Journal of Advanced Computer Technology, 9(8), 3785-3790.
Agarwal, S., Fan, Y., McMillen, D. P., & Sing, T. F. (2021). Tracking the pulse of a city—3D real estate price heat maps. Journal of Regional Science, 61(3), 543-569. https://doi.org/10.1111/jors.12522
Asri, H., Mousannif, H., & Al Moatassime, H. (2019). Reality mining and predictive analytics for building smart applications. Journal of Big Data, 6(1), 1-25. https://doi.org/10.1186/s40537-019-0227-y
Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M., Cheung, K., Tonkoski, R., ... & Zhang, X. (2019). Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions. IET Smart Grid, 2(2), 141-154.
Birnhack, M. (2021). Who controls covid-related medical data? copyright and personal data. IIC - International Review of Intellectual Property and Competition Law, 52(7), 821-824. https://doi.org/10.1007/s40319-021-01067-5
Bogaert, M., Ballings, M., Bergmans, R., & Van den Poel, D. (2021). Predicting Self‐declared movie watching behavior using Facebook data and Information‐Fusion sensitivity analysis. Decision Sciences, 52(3), 776-810. https://doi.org/10.1111/deci.12406
Boone, C. A., Hazen, B. T., Skipper, J. B., & Overstreet, R. E. (2016). A framework for investigating optimization of service parts performance with big data. Annals of Operations Research, 270(1-2), 65-74. https://doi.org/10.1007/s10479-016-2314-1
Broeders, D., Schrijvers, E., van der Sloot, B., van Brakel, R., de Hoog, J., & Ballin, E. H. (2017). Big Data and security policies: Towards a framework for regulating the phases of analytics and use of Big Data. Computer Law & Security Review, 33(3), 309-323.
Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A., & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a big data framework. Future Generation Computer Systems, 90, 262-272. https://doi.org/10.1016/j.future.2018.08.003
Cao, H., Wachowicz, M., & Cha, S. (2017, December). Developing an edge computing platform for real-time descriptive analytics. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4546-4554). IEEE.
Chan, C. (2011). Using online advertising to increase the impact of a library Facebook page. Library Management, 32(4/5), 361-370. https://doi.org/10.1108/01435121111132347
Chang, V. (2021). An ethical framework for big data and smart cities. Technological Forecasting & Social Change, 165, 120559. https://doi.org/10.1016/j.techfore.2020.120559
Chu, B. (2020, November). Analysis on the Success of Coca-Cola Marketing Strategy. In 2020 2nd International Conference on Economic Management and Cultural Industry (ICEMCI2020) (pp. 96-100). Atlantis Press.
Davis, B., Grewal, D., & Hamilton, S. (2021). The future of marketing analytics and public policy. Journal of Public Policy & Marketing, 40(4), 447-452. https://doi.org/10.1177/07439156211042372
Deshpande, P. S., Sharma, S. C., & Peddoju, S. K. (2019). Predictive and prescriptive analytics in Big-data Era. In Security and data storage aspect in cloud computing (pp. 71-81). Springer, Singapore.
Domalewska, D. (2021). A longitudinal analysis of the creation of environmental identity and attitudes towards energy sustainability using the framework of identity theory and big data analysis. Energies (Basel), 14(647), 647. https://doi.org/10.3390/en14030647
Farney, T. A. (2011). Click analytics: Visualizing website use data. Information Technology and Libraries, 30(3), 141. https://doi.org/10.6017/ital.v30i3.1771
Fatehkia, M., Kashyap, R., & Weber, I. (2018). Using Facebook ad data to track the global digital gender gap. World Development, 107, 189-209. https://doi.org/10.1016/j.worlddev.2018.03.007
Gamage, C. C. (2020). Big Data Analytics: Significance, Challenges and Techniques.
Geiger, C., Frosio, G., & Bulayenko, O. (2018). Text and data mining in the proposed copyright reform: Making the EU ready for an age of big data?: Legal analysis and policy recommendations. IIC - International Review of Intellectual Property and Competition Law, 49(7), 814-844. https://doi.org/10.1007/s40319-018-0722-2
Golmohammadi, H., Hamzei, Z., Hosseinifard, M., & Ahmadi, S. H. (2020). Smart fully integrated lab: A Smartphone‐Based compact miniaturized Analytical/Diagnostic device. Advanced Materials Technologies, 5(12), 2000742-n/a. https://doi.org/10.1002/admt.202000742
Hegde, G. P., & Hegde, N. (2021). Significance of big data frameworks and speculative approaches in healthcare systems. International Journal of Advanced Networking and Applications, 12(6), 4787-4792. https://doi.org/10.35444/IJANA.2021.12609
Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356-365. https://doi.org/10.1016/j.jbusres.2020.01.022
Järvinen, J., & Karjaluoto, H. (2015). The use of web analytics for digital marketing performance measurement. Industrial Marketing Management, 50, 117-127. https://doi.org/10.1016/j.indmarman.2015.04.009
Karpurapu, B. S. H., & Jololian, L. (2017). A framework for social network sentiment analysis using big data analytics. In Big Data and Visual Analytics (pp. 203-217). Springer, Cham.
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523-537. https://doi.org/10.1016/j.indmarman.2019.08.003
Kee, D. M. H., Hidayah, N., Syamilah, H., Nasuhah, N. N., Syasya, N. H., & Norathirah, W. (2021). How Starbucks Maintain Its Competitive Edge? – The Secret of Its Success. Journal of the Community Development in Asia (JCDA), 4(2), 34-43.
Kennedy, H., Kunkel, T., & Funk, D. C. (2021). Using predictive analytics to measure effectiveness of social media engagement: A digital measurement perspective. Sport Marketing Quarterly, 30(4), 265-277. https://doi.org/10.32731/SMQ.304.1221.02
Kostic, Z., & Jevremovic, A. (2020; 2021). What image features boost housing market predictions? IEEE Transactions on Multimedia, 22(7), 1904-1916. https://doi.org/10.1109/TMM.2020.2966890
Kozjek, D., Vrabič, R., Rihtaršič, B., Lavrač, N., & Butala, P. (2020). Advancing manufacturing systems with big-data analytics: A conceptual framework. International Journal of Computer Integrated Manufacturing, 33(2), 169-188. https://doi.org/10.1080/0951192X.2020.1718765
Kumari, R., & Saini, K. (2020). Automobile Industries using Data Mining and Predictive Analytics: An Industry 4.0 Approach. India: Journal of Xi'an University of Architecture & Technology, 12(V).
Laurie, G. T. (2019). Cross-sectoral big data: The Application of an ethics framework for big data in health and research. Asian Bioethics Review, 11(3), 327-339. https://doi.org/10.1007/s41649-019-00093-3
Li, M., Bao, Z., Sellis, T., Yan, S., & Zhang, R. (2018). HomeSeeker: A visual analytics system of real estate data. Journal of Visual Languages and Computing, 45, 1-16. https://doi.org/10.1016/j.jvlc.2018.02.001
Lipworth, W. (2019). Real-world data to generate evidence about healthcare interventions: The application of an ethics framework for big data in health and research. Asian Bioethics Review, 11(3), 289-298. https://doi.org/10.1007/s41649-019-00095-1
Lock, O., Bednarz, T., & Pettit, C. (2021). The visual analytics of big, open public transport data - a framework and pipeline for monitoring system performance in greater Sydney. Big Earth Data, 5(1), 134-159. https://doi.org/10.1080/20964471.2020.1758537
Lv, Z., Song, H., Basanta-Val, P., Steed, A., & Jo, M. (2017). Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Transactions on Industrial Informatics, 13(4), 1891-1899.
Mao, J., & Mao, Z. (2017). Significance of deep learning on big data analytics. In Civil, Architecture and Environmental Engineering (pp. 1597-1600). CRC Press.
Mavragani, A., & Ochoa, G. (2018). Forecasting AIDS prevalence in the United States using online search traffic data. Journal of Big Data, 5(1), 1-21. https://doi.org/10.1186/s40537-018-0126-7
Mazzei, M. J., & Noble, D. (2017). Big data dreams: A framework for corporate strategy. Business Horizons, 60(3), 405-414. https://doi.org/10.1016/j.bushor.2017.01.010
Merlin, N. R. G., & Prem. M, V. (2021). Efficient indexing and retrieval of patient information from the big data using MapReduce framework and optimisation. Journal of Information Science, 16555152110137. https://doi.org/10.1177/01655515211013708
Mooney, S. J., & Pejaver, V. (2018). Big data in public health: Terminology, machine learning, and privacy. Annual Review of Public Health, 39(1), 95-112. https://doi.org/10.1146/annurev-publhealth-040617-014208
Munawar, H. S., Qayyum, S., Ullah, F., & Sepasgozar, S. (2020). Big data and its applications in smart real estate and the disaster management life cycle: A systematic analysis. Big Data and Cognitive Computing, 4(2), 4. https://doi.org/10.3390/bdcc4020004
Pathak, M. A. (2014). Data visualization. (pp. 31-60). Springer International Publishing. https://doi.org/10.1007/978-3-319-12066-9_4
Poursaeed, O., Matera, T., & Belongie, S. (2018;2017;). Vision-based real estate price estimation. Machine Vision and Applications, 29(4), 667-676. https://doi.org/10.1007/s00138-018-0922-2
Queiroz, M. M. (2018). A framework based on twitter and big data analytics to enhance sustainability performance. Environmental Quality Management, 28(1), 95-100. https://doi.org/10.1002/tqem.21576
Rakheja, J. (2018). How 3D visualization technology transforming real estate SMBs? PC Quest, https://www.pcquest.com/3d-visualization-technology-transforming-real-estate-smbs/ Accessed 6 March 2022.
Saranya, P., & Asha, P. (2019, November). Survey on Big Data Analytics in health care. In 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 46-51). IEEE.
Sellami, M., Mezni, H., & Hacid, M. S. (2020). On the use of big data frameworks for big service composition. Journal of Network and Computer Applications, 166, 102732. https://doi.org/10.1016/j.jnca.2020.102732
Shettar, M., Karkal, R., Kakunje, A., Mendonsa, R. D., & Chandran, V. M. (2017). Facebook addiction and loneliness in the post-graduate students of a university in southern India. International Journal of Social Psychiatry, 63(4), 325-329. https://doi.org/10.1177/0020764017705895
Siliverstovs, B., & Wochner, D. S. (2018). Google trends and reality: Do the proportions match? appraising the informational value of online search behavior: Evidence from Swiss tourism regions. Journal of Economic Behavior & Organization, 145, 1. https://doi.org/10.1016/j.jebo.2017.10.011
Stergiou, C., & Psannis, K. E. (2017). Recent advances delivered by mobile cloud computing and internet of things for big data applications: A survey. International Journal of Network Management, 27(3), n/a. https://doi.org/10.1002/nem.1930
Sun, G., Liang, R., Wu, F., & Qu, H. (2013). A web-based visual analytics system for real estate data. Science China. Information Sciences, 56(5), 154-166. https://doi.org/10.1007/s11432-013-4830-9
Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.
Todri, V. (2021). Frontiers: The impact of ad-blockers on online consumer behavior. Marketing Science (Providence, R.I.), https://doi.org/10.1287/mksc.2021.1309
Van der Voet, J. How Netflix is Changing the Entertainment Industry. EBF Groningen.–2017.–URL: https://screenrant.com/netflix-original-anime-series-future.
Walsh, K., Wallace, A., Pavis, M., Olszowy, N., Griffin, J., & Hawkins, N. (2021). Intellectual property rights and access in crisis. IIC - International Review of Intellectual Property and Competition Law, 52(4), 379-416. https://doi.org/10.1007/s40319-021-01041-1
Weaver, A. (2021). Tourism, big data, and a crisis of analysis. Annals of Tourism Research, 88, 103158. https://doi.org/10.1016/j.annals.2021.103158
Wu, W., & Kou, G. (2016). A group consensus model for evaluating real estate investment alternatives. Financial Innovation (Heidelberg), 2(1), 1-10. https://doi.org/10.1186/s40854-016-0027-8
Xu, Y., & Shi, B. (2021). Copyright protection method of big data based on Nash equilibrium and constraint optimization. Peer-to-Peer Networking and Applications, 14(3), 1520-1530. https://doi.org/10.1007/s12083-021-01096-4
Xun, J. (2014). Revisiting the two-stage choice model: An empirical study of consumer choice on brand website visits. Behaviour & Information Technology, 33(11), 1192-1207. https://doi.org/10.1080/0144929X.2013.872188
DOI: http://dx.doi.org/10.46827/ejmms.v7i2.1246
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