Empirical method for developing a hyper-personalization artifact

This research synthesized an artifact that applied Artificial Intelligence to enable telecommunication businesses to offer hyper-personalized products and services. The study was based on a database of the customers’ digital demography, and a range of telecommunication products and services the customer used. Relevant attributes were included in a Google Form to collect customer usage data. The data collected was screened for bad data and normality checked by conducting multivariable Mahalanobis outlier detection and normality tests. Outlier data was removed and multivariable normality of the data was ensured. Using the preprocessed database, several procedures were conducted to determine the best artificial intelligence algorithm for subsequent analysis, namely, the Logistic Model Tree algorithm. Using this algorithm and the customer digital demography dataset, the telecommunication business offerings for the customers were predicted with a 97.6 % accuracy. The proof of concept was developed using the Waikato Environment for Knowledge Analysis software. The artifact created was named Hypersona. Implemented within telecommunication systems, the model can be integrated into customer relationship management platforms allowing real-time adaptation to user needs. The methodology ensures feasibility by leveraging existing data infrastructures, while scalability is achieved through automated learning mechanisms that adapt to changing user environments. The research contributions lie in the applicability of real-time identification of changing personalized products and services. This research highlights the potential of artificial intelligence driven hyper-personalization in the telecommunications sector. Further research can be extended to other contemporary artificial intelligence methods and exploring the scaling of the artifact across diverse businesses.