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Building a model of consumer behavior is a multicriteria complex task that depends on many factors, ranging from the specifics of the business industry to global changes in the market. However, promising developments in artificial intelligence, composable data, the development of data fabric standards, and information technology, in general, make it possible to build the most optimistic forecasts (Provost and Fawcett, 2013). First of all, the data analytics market is booming, with companies spending 10% more on the industry in 2021 than they did in 2020 worth billions of dollars (Goepfert and Shirer, 2021). Accordingly, the number of experts in this field is also growing; for example, in the United States, it is expected to grow by more than a third over ten years, as well as the number of organizations that have taken the vector to the effective use of information (U.S. Bureau of Labor Statistics, 2022). Given the rapid pace of technology development, it can be assumed that behavioral prediction tools will appear in the relatively near future.
First, artificial intelligence and neural networks can go beyond the automation of human processes and complex calculations. Segmentation will be used as input to differentiate results better, while intelligent systems will look for correlations between the time of purchase, the season of the year, ticket size, product category, and a host of other sometimes obscure aspects (Perez-Vega et al., 2021 ). Secondly, at the intersection with big data, machine learning has learned to work with disparate and diverse information, providing the ability to compose it into modules with subsequent deployment in any conditions (Li, Pan, and Huang, 2019). It is not known what information will become the determinant of consumer behavior, and therefore such a layout will always be able to refer to any aspects related to the transaction, product, or client. Finally, the data fabric technology simplifies the management of complex data structures by standardizing their structure for easier handling (Moon, Kang, and Park, 2021). It is not worth expecting high efficiency in the coming years, but steps in this direction can be taken soon, and consumer behavior will become more predictable.
Reference List
Goepfert, J. and Shirer, M. (2021) Global spending on Big Data and analytics solutions will reach $215.7 billion in 2021, according to a new IDC spending guide, IDC. Web.
Li, J., Pan, S., and Huang, L. (2019) A machine learning based method for customer behavior prediction, Tehni
ki Vjesnik, 26(6), pp. 1670-1676. Web.
Moon, S. J., Kang, S. B., and Park, B. J. (2021) A study on a distributed data fabric-based platform in a multi-cloud environment, International Journal of Advanced Culture Technology, 9(3), pp. 321-326.
Perez-Vega, R., et al. (2021) Reshaping the contexts of online customer engagement behavior via artificial intelligence: A conceptual framework, Journal of Business Research, 129, pp. 902-910.
Provost, F. and Fawcett, T. (2013) Data sciences for business: what you need to know about data mining and data-analytics thinking. Sebastopol, CA: OReilly Media.
U.S. Bureau of Labor Statistics. (2022) Fastest growing occupations. Web.
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